GitLab

* add flaubert tokenization test, enrich inheritance in FlaubertTokenizer.

* fix quality code ci

* ensure parameter consistency

* fix ci

* fix copyright year and flatten vocab list.

* fix style

* doc: add info about wav2vec2 bert in older wav2vec2 models.

* apply suggestions from review.

* forward contrib credits from review

---------
Co-authored-by: Sanchit Gandhi <sanchit-gandhi@users.noreply.github.com>

Bump transformers in /examples/research_projects/deebert

Bumps [transformers](https://github.com/huggingface/transformers) from 3.5.1 to 4.38.0.
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](https://github.com/huggingface/transformers/compare/v3.5.1...v4.38.0

)

---
updated-dependencies:
- dependency-name: transformers
  dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* Move label validation checks - fail early

* Remove some formatting changes - add back labels change wav2vec2

* benchmark workflow

* benchmark workflow

* benchmark workflow

* benchmark workflow

* build

* build

* build

* build

* build

* build

* build

* build

* build

* build

* build

* build

* build

* build

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Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

Fixed torch definition error

The StoppingCriteriaList allocates is_done without specifying dtype=torch.bool. On XLA this allocates a float tensor and causes a failure on the following line:

is_done = is_done | criteria(input_ids, scores, **kwargs)

by attempting to OR float with bool.

Bump transformers in /examples/research_projects/vqgan-clip

Bumps [transformers](https://github.com/huggingface/transformers) from 4.26.0 to 4.38.0.
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](https://github.com/huggingface/transformers/compare/v4.26.0...v4.38.0

)

---
updated-dependencies:
- dependency-name: transformers
  dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* build

* build

* build

* build

* fix

* fix

* fix

---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Move out common validation

* Add missing backbone config arguments

* deprecate blip

* mention deprecation on docs

* fix

* fix

* fix

* fix

---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

✨

 Add new line switch before logging "***** Running {description} *****".
Signed-off-by: jacklanda <yonyonlau@gmail.com>

* Fix pipeline tests - torch imports

* Frameowrk dependant float conversion

* fix logits dtype

* Add bf16/fp16 tests for text_classification pipeline

* Update test_pipelines_text_classification.py

* fix

* fix

* Added interpolate pos encoding feature and test to deit

* Added interpolate pos encoding feature and test for deit TF model

* readded accidentally delted test for multi_gpu

* storing only patch_size instead of entire config and removed commented code

* Update modeling_tf_deit.py to remove extra line
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

video-llava can handle more frames

* fix(PatchTST): Wrong dropout used for PretainHead

* feat(PatchTST): remove unused config.dropout

---------
Co-authored-by: Strobel Maximilian (IFAG PSS SIS SCE ACM) <Maximilian.Strobel@infineon.com>

* add device in logits processor

* remove device when not needed

* codestyle

* tests

* forgot `melody` version

* Update src/transformers/models/whisper/generation_whisper.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* codestyle

* updates

---------
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* add tokenizer_summary to es/_toctree.yml

* add tokenizer_summary to es/

* fix link to Transformes XL in en/

* translate until Subword tokenization section

* fix GPT link in en/

* fix other GPT link in en/

* fix typo in en/

* translate the doc

* run make fixup

* Remove .md in Transformer XL link

* fix some link issues in es/

* fix typo

* build

* build

* build

* build

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Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* update to not(endswith(loss))

* ruff formatting

Update modeling_cohere.py

Update index.md - Contents = Contenu

French typo -
Contents = Contenu

* Rename sanity_evaluation to eval_on_start

* move arg back to last

fix typo

* fix the get_size_with_aspect_ratio in max_size situation

* make fix-up

* add more general solution

* consider when max_size is not defined

* fix typo

* fix typo

* simple fix

* fix error

* fix if else error

* fix error of size overwrite

* fix yolos image processing

* fix detr image processing

* make

* add longest related test script

* Update src/transformers/models/yolos/image_processing_yolos.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* add more test

* add test script about longest size

* remove deprecated

---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* add qwen2 gguf support

* Update docs

* fix qwen2 tokenizer

* add qwen2 gguf test

* fix typo in qwen2 gguf test

* format code

* Remove mistral, clarify the error message

* format code

* add typing and update docstring

* fix

* fix

* fix

* fix

---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

Update MLP

* tmp commit

* sliding window with fewer differences

* make fixup + rebase

* missing overwrite

* update non-causal mask for sdpa

* add test

* update docstrings

* add one more test

* fix cross attention bug

* gentler atol/rtol

While running the model.prepare_tf_dataset() method,
it raises the error below:
```
TypeError: Cannot convert [array([322.,   1.])] to EagerTensor of dtype int64
```

This happens, in  "DataCollatorForSeq2Seq" function when we are try
to convert the labels to tensors. While converting the labels to tensors,
the labels can be in the format of list of list or list of ndarrays.
There is no problem converting the list of list lables. There is a problem
when the list of ndarrays are float values(like below).

```
[array([322.,   1.])]
```

so the exception raises while trying to convert this label to tensors using
below code.

```
batch["labels"] = tf.constant(batch["labels"], dtype=tf.int64)
```

The labels are always integer values, so this got converted to float
values in the label padding operation below.
```
batch["labels"] = [
                    call(label)
                    if padding_side == "right"
                    else np.concatenate([[self.label_pad_token_id] * (max_label_length - len(label)), label])
                    for label in labels
                    ]
```
Here we have 2 cases:
1 - Concatenating an array having integer padding token value with labels.
2 - Concatenating an empty array with labels.

----------------------------------------------------------------------------------------
case 1: Concatenating an array having integer padding token value with labels.
WORKS EXPECTED:
----------------------------------------------------------------------------------------
```
label = np.array([233, 1])
max_label_length = 4
label_pad_token_id = -100
np.concatenate([[label_pad_token_id] * (max_label_length - len(label)), label])
o/p:
array([-100, -100,  233,    1])
```

----------------------------------------------------------------------------------------
Case 2: Concatenating an empty array with labels.
GIVES THE ISSUE:
This scenorio can happen when the label has the maximum label length -- No padding needed.
----------------------------------------------------------------------------------------
```
label = np.array([233, 1])
max_label_length = 2
label_pad_token_id = -100
np.concatenate([[label_pad_token_id] * (max_label_length - len(label)), label])
o/p:
array([233.,   1.])
```

----------------------------------------------------------------------------------------
Solution:
----------------------------------------------------------------------------------------
We need to concatenate a ndarray of dtype int with labels.

AFTER FIX:
----------
case 1:
```

label = np.array([233, 1])
max_label_length = 4
label_pad_token_id = -100
np.concatenate([np.array([label_pad_token_id] * (max_label_length - len(label)), dtype=np.int64),label])

o/p:
array([-100, -100,  233,    1])
```

case 2:
```

label = np.array([233, 1])
max_label_length = 2
label_pad_token_id = -100
np.concatenate([np.array([label_pad_token_id] * (max_label_length - len(label)), dtype=np.int64),label])

o/p:
array([233,   1])
```

* fixes

* fix-copies

* token healing impl + trie with extensions

* make fixup

* prefix-robust space tokenization

* examples readme and requirements

* make fixup

* allow input prompt and model

* redundant defaults

* Specialized Trie

* make fixup

* updated tests with new inherited Tree

* input ids to auto device_map

* rm unused import

* Update src/transformers/generation/utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* naming convention

* Revert "naming convention"

This reverts commit dd39d9c5b7a969e2d8a8d2a8e54f121b82dc44f0.

* naming convention

* last -hopefully- changes

---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Remove copied froms for deprecated models

* Remove automatically in script