Add descriptive docstring to TemperatureLogitsWarper (#24892)

* Add descriptive docstring to TemperatureLogitsWarper

It addresses https://github.com/huggingface/transformers/issues/24783



* Remove niche features
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

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

* Refactor the examples to simpler ones

* Add a missing comma
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Make args description more compact
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Remove extra text after making description more compact
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Fix linter

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

src/transformers/generation/logits_process.py

@@ -172,11 +172,58 @@ class MinNewTokensLengthLogitsProcessor(LogitsProcessor):
 
 class TemperatureLogitsWarper(LogitsWarper):
     r"""
-    [`LogitsWarper`] for temperature (exponential scaling output probability distribution).
+    [`LogitsWarper`] for temperature (exponential scaling output probability distribution), which effectively means
+    that it can control the randomness of the predicted tokens.
+
+    <Tip>
+
+    Make sure that `do_sample=True` is included in the `generate` arguments otherwise the temperature value won't have
+    any effect.
+
+    </Tip>
 
     Args:
         temperature (`float`):
-            The value used to module the logits distribution.
+            Strictly positive float value used to modulate the logits distribution. A value smaller than `1` decreases
+            randomness (and vice versa), with `0` being equivalent to shifting all probability mass to the most likely
+            token.
+
+    Examples:
+
+    ```python
+    >>> import torch
+    >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+    >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
+    >>> model.config.pad_token_id = model.config.eos_token_id
+    >>> model.generation_config.pad_token_id = model.config.eos_token_id
+    >>> input_context = "Hugging Face Company is"
+    >>> input_ids = tokenizer.encode(input_context, return_tensors="pt")
+
+    >>> torch.manual_seed(0)
+
+    >>> # With temperature=1, the default, we consistently get random outputs due to random sampling.
+    >>> outputs = model.generate(input_ids=input_ids, max_new_tokens=10, temperature=1, do_sample=True)
+    >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+    Hugging Face Company is one of these companies that is going to take a
+
+    >>> outputs = model.generate(input_ids=input_ids, max_new_tokens=10, temperature=1, do_sample=True)
+    >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+    Hugging Face Company is one of these companies, you can make a very
+
+    >>> # However, with temperature close to 0 , the output remains invariant.
+    >>> outputs = model.generate(input_ids=input_ids, max_new_tokens=10, temperature=0.0001, do_sample=True)
+    >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+    Hugging Face Company is a company that has been around for over 20 years
+
+    >>> # even if we set a different seed.
+    >>> torch.manual_seed(42)
+    >>> outputs = model.generate(input_ids=input_ids, max_new_tokens=10, temperature=0.0001, do_sample=True)
+    >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+    Hugging Face Company is a company that has been around for over 20 years
+    ```
     """
 
     def __init__(self, temperature: float):