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fix: (issue #32124) Exception raised when running `transformers/examples/flax/language-modeling/t5_tokenizer_model.py`. (#32157)

fix: Exception raised when running .

* up

* style

* stopping

tests! :D

nits

* Test this zach

* Test for improper init w/o zero3

* Move back

* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Get rid of stars in warning

* Make private

* Make clear

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

* fixed hybrid cache init, added test

* Fix Test Typo

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Co-authored-by: Aaron Haag <aaron.haag@siemens.com>

* Initial implementation of OffloadedCache

* enable usage via cache_implementation

* Address feedback, add tests, remove legacy methods.

* Remove flash-attn, discover synchronization bugs, fix bugs

* Prevent usage in CPU only mode

* Add a section about offloaded KV cache to the docs

* Fix typos in docs

* Clarifications and better explanation of streams

* Fix conflicting key in init kwargs in PreTrainedTokenizerBase

* Update code to check for callable key in save_pretrained

* Apply PR suggestions

* Invoke CI

* Updates based on PR suggestion

empty list in defaults

* Remove TPU device map for saving tokenizer config

* Update tokenization_utils_base.py

* Fix error msg when passing non-string device into tokenizer

* Fix error message for non-string tokenizer device

* Print out tokenizer device type in error msg

* Update tokenization_utils_base.py

Co-authored-by: Guoming Zhang <37257613+nv-guomingz@users.noreply.github.com>

* Remove size check between attn_weights and kv_seq_len

* add unit tests

* [whisper] compile compatibility with long-form decoding

* clarify comment

* fix after rebase

* finalise

* fix bsz

* fix cache split

* remove contiguous

* style

* finish

* update doc

* prevent cuda graph trace

cache class flag

* Fixed staticmethods with self as first argument.

* Fixed staticmethods with self as first argument.

* Fixed staticmethods with self as first argument.

* Fixed staticmethods with self as first argument.

* draft

* apply changes to all relevant archs

* rerun ci - check_docstrings.py failing?

* fix docstring

* move 2D->4D mask creation to modeling file

* repo consistency

* fix the batch size = 1 case - calling contiguous is not enough

* nit

* style

* propagate to gemma/gemma-2

* prepare inputs for gemma generation

* implement test and tiny fix in gemma2

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

* fix copies

* ci pass

* fix gemma's test_compile_static_cache tests

* flacky

* retrigger ci

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

Fix error when streaming agent run to gradio with non-string tool arguments

* Fix FA2 call for Perciever layer

* [run_slow] idefics2

* [run_slow] idefics2

* [run_slow] idefics2

* Fix up

* [run_slow] idefics2

* [run_slow] idefics2

* [run_slow] idefics2

fix 💩

* enable flash-attn & static cache

* this works, not the prev

* fix for sliding window layers

* not needed anymore

fix

* Remove user-defined tokens which can be obtained through merges

* Remove debug line

* formatting

* Refactor spm slow -> fast converter

* revert unnecessary refactor

* set comprehension

* remove test files

* Use `vocab_scores`

* Always replace spiece underline with space in decode

* we no longer need token filtering

* Add save fast load slow unit test

* Remove tokenizers version check

* Remove duplicate code

* Make `<start_of_turn>` and `<end_of_turn>` special tokens

* Bias merge priority with length if score is the same

* Add unit test for merge priority

* CI

* tmp

* skip files not in the diff

* use git.Repo instead of an external subprocess

* add tiny change to confirm that the diff is working on pushed changes

* add make quality task

* more profesh main commit reference

fixes #32329 : The Torch code is correct - to get an average of 10% of the total, we want to take 50% of the remainder after we've already masked 80% with [MASK] in the previous step.

fixes to properly shard FSDP across cpu and meta for cpu_efficient_loading for prequantized 4bit (#32276)

Fixed raising of few exceptions.

* new agent plan

* plan type assertion

* style corrections

* better prompt naming

* make fixup

* doc formatting nits

* ignore non-autodocs

* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

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

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

* make fixup

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

* tentative fix

* do the same for M4T

* fix epochs_trained as int when resuming training

* refactor

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Co-authored-by: teddyferdinan <teddy.ferdinan@pwr.edu.pl>

* fix gguf dequantize for gguf==0.9.1

* fix old version

* make style

Docs: fix GaLore optimizer example

Fix incorrect usage of GaLore optimizer in Transformers trainer code example.

The GaLore optimizer uses low-rank gradient updates to reduce memory usage. GaLore is quite popular and is implemented by the authors in [https://github.com/jiaweizzhao/GaLore](https://github.com/jiaweizzhao/GaLore). A few months ago GaLore was added to the HuggingFace Transformers library in https://github.com/huggingface/transformers/pull/29588.

Documentation of the Trainer module includes a few code examples of how to use GaLore. However, the `optim_targe_modules` argument to the `TrainingArguments` function is incorrect, as discussed in https://github.com/huggingface/transformers/pull/29588#issuecomment-2006289512. This pull request fixes this issue.

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

* Add stream_to_gradio method for running agent in gradio demo