


import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test






class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       @parameterized.expand([(None,), ('''foo.json''',)]		)
       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Tuple:




         '''simple docstring'''





         A_      :				Dict		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       bad_words_ids=[[1, 2, 3], [4, 5]]				,       )
         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(_SCREAMING_SNAKE_CASE				,       config_name=_SCREAMING_SNAKE_CASE		)
           A_      :				Tuple		     =					GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE				,       config_name=_SCREAMING_SNAKE_CASE		)

         # Checks parameters that were specified
         self.assertEqual(loaded_config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(loaded_config.temperature				,       0.7		)
         self.assertEqual(loaded_config.length_penalty				,       1.0		)
         self.assertEqual(loaded_config.bad_words_ids				,       [[1, 2, 3], [4, 5]]		)

         # Checks parameters that were not specified (defaults)
         self.assertEqual(loaded_config.top_k				,       50		)
         self.assertEqual(loaded_config.max_length				,       20		)
         self.assertEqual(loaded_config.max_time				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				str		     =					AutoConfig.from_pretrained('''gpt2'''		)
         A_      :				int		     =					GenerationConfig.from_model_config(_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					GenerationConfig()

         # The generation config has loaded a few non-default parameters from the model config
         self.assertNotEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         # One of those parameters is eos_token_id -- check if it matches
         self.assertNotEqual(generation_config_from_model.eos_token_id				,       default_generation_config.eos_token_id		)
         self.assertEqual(generation_config_from_model.eos_token_id				,       model_config.eos_token_id		)



       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         A_      :				List[Any]		     =					GenerationConfig()
         A_      :				Optional[Any]		     =					{
             '''max_new_tokens''': 1024,
             '''foo''': '''bar''',
         }
         A_      :				Dict		     =					copy.deepcopy(_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					generation_config.update(**_SCREAMING_SNAKE_CASE		)

         # update_kwargs was not modified (no side effects)
         self.assertEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         # update_kwargs was used to update the config on valid attributes
         self.assertEqual(generation_config.max_new_tokens				,       1024		)

         # `.update()` returns a dictionary of unused kwargs
         self.assertEqual(_SCREAMING_SNAKE_CASE				,       {'''foo''': '''bar'''}		)



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				List[str]		     =					GenerationConfig()
         A_      :				int		     =					'''bar'''

         with tempfile.TemporaryDirectory('''test-generation-config'''		) as tmp_dir:
           generation_config.save_pretrained(_SCREAMING_SNAKE_CASE		)

           A_      :				Optional[Any]		     =					GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
         # update_kwargs was used to update the config on valid attributes
         self.assertEqual(new_config.foo				,       '''bar'''		)

         A_      :				Optional[int]		     =					GenerationConfig.from_model_config(_SCREAMING_SNAKE_CASE		)
         assert not hasattr(_SCREAMING_SNAKE_CASE				,       '''foo'''		)  # no new kwargs should be initialized if from config







       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				str		     =					GenerationConfig()
         self.assertEqual(default_config.temperature				,       1.0		)
         self.assertEqual(default_config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(default_config.num_beams				,       1		)

         A_      :				str		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       bad_words_ids=[[1, 2, 3], [4, 5]]				,       )
         self.assertEqual(config.temperature				,       0.7		)
         self.assertEqual(config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(config.num_beams				,       1		)

         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(_SCREAMING_SNAKE_CASE		)
           A_      :				Dict		     =					GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE				,       temperature=1.0		)

         self.assertEqual(loaded_config.temperature				,       1.0		)
         self.assertEqual(loaded_config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(loaded_config.num_beams				,       1		)  # default value






@is_staging_test
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       @classmethod
       def        _snake_case       (      cls		)->Optional[int]:




         '''simple docstring'''





         A_      :				str		     =					TOKEN
         HfFolder.save_token(_SCREAMING_SNAKE_CASE		)



       @classmethod
       def        _snake_case       (      cls		)->List[Any]:




         '''simple docstring'''





         try:
           delete_repo(token=cls._token				,       repo_id='''test-generation-config'''		)
         except HTTPError:
           pass

         try:
           delete_repo(token=cls._token				,       repo_id='''valid_org/test-generation-config-org'''		)
         except HTTPError:
           pass



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       )
         config.push_to_hub('''test-generation-config'''				,       use_auth_token=self._token		)

         A_      :				Optional[int]		     =					GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)

        # Reset repo
         delete_repo(token=self._token				,       repo_id='''test-generation-config'''		)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(
               _SCREAMING_SNAKE_CASE				,       repo_id='''test-generation-config'''				,       push_to_hub=_SCREAMING_SNAKE_CASE				,       use_auth_token=self._token		)

         A_      :				Optional[Any]		     =					GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)







       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Tuple		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       )
         config.push_to_hub('''valid_org/test-generation-config-org'''				,       use_auth_token=self._token		)

         A_      :				Union[str, Any]		     =					GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)

        # Reset repo
         delete_repo(token=self._token				,       repo_id='''valid_org/test-generation-config-org'''		)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(
               _SCREAMING_SNAKE_CASE				,       repo_id='''valid_org/test-generation-config-org'''				,       push_to_hub=_SCREAMING_SNAKE_CASE				,       use_auth_token=self._token		)

         A_      :				Dict		     =					GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)








import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
     import jax
     import jax.numpy as jnp

     from transformers.generation import (
         FlaxForcedBOSTokenLogitsProcessor,
         FlaxForcedEOSTokenLogitsProcessor,
         FlaxLogitsProcessorList,
         FlaxMinLengthLogitsProcessor,
         FlaxTemperatureLogitsWarper,
         FlaxTopKLogitsWarper,
         FlaxTopPLogitsWarper,
     )






@require_flax
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					jnp.ones((batch_size, length)		) / length
         return scores



       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					None
         A_      :				Any		     =					20

         A_      :				Any		     =					self._get_uniform_logits(batch_size=2				,       length=_SCREAMING_SNAKE_CASE		)

         # tweak scores to not be uniform anymore
         A_      :				Dict		     =					scores.at[1, 5].set((1 / length) + 0.1		)  # peak, 1st batch
         A_      :				Tuple		     =					scores.at[1, 10].set((1 / length) - 0.4		)  # valley, 1st batch

         # compute softmax
         A_      :				List[str]		     =					jax.nn.softmax(_SCREAMING_SNAKE_CASE				,       axis=-1		)

         A_      :				Any		     =					FlaxTemperatureLogitsWarper(temperature=0.5		)
         A_      :				Optional[int]		     =					FlaxTemperatureLogitsWarper(temperature=1.3		)

         A_      :				Optional[Any]		     =					jax.nn.softmax(temp_dist_warper_sharper(_SCREAMING_SNAKE_CASE				,       scores.copy()				,       cur_len=_SCREAMING_SNAKE_CASE		)				,       axis=-1		)
         A_      :				List[Any]		     =					jax.nn.softmax(temp_dist_warper_smoother(_SCREAMING_SNAKE_CASE				,       scores.copy()				,       cur_len=_SCREAMING_SNAKE_CASE		)				,       axis=-1		)

         # uniform distribution stays uniform
         self.assertTrue(jnp.allclose(probs[0, :]				,       warped_prob_sharp[0, :]				,       atol=1e-3		)		)
         self.assertTrue(jnp.allclose(probs[0, :]				,       warped_prob_smooth[0, :]				,       atol=1e-3		)		)

         # sharp peaks get higher, valleys get lower
         self.assertLess(probs[1, :].max()				,       warped_prob_sharp[1, :].max()		)
         self.assertGreater(probs[1, :].min()				,       warped_prob_sharp[1, :].min()		)

         # smooth peaks get lower, valleys get higher
         self.assertGreater(probs[1, :].max()				,       warped_prob_smooth[1, :].max()		)
         self.assertLess(probs[1, :].min()				,       warped_prob_smooth[1, :].min()		)



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				Any		     =					None
         A_      :				List[Any]		     =					10
         A_      :				str		     =					2

         # create ramp distribution
         A_      :				Any		     =					np.broadcast_to(np.arange(_SCREAMING_SNAKE_CASE		)[None, :]				,       (batch_size, vocab_size)		).copy()
         A_      :				List[Any]		     =					ramp_logits[1:, : vocab_size // 2] + vocab_size

         A_      :				Any		     =					FlaxTopKLogitsWarper(3		)

         A_      :				Tuple		     =					top_k_warp(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)

         # check that correct tokens are filtered
         self.assertListEqual(jnp.isinf(scores[0]		).tolist()				,       7 * [True] + 3 * [False]		)
         self.assertListEqual(jnp.isinf(scores[1]		).tolist()				,       2 * [True] + 3 * [False] + 5 * [True]		)

         # check special case
         A_      :				Optional[int]		     =					5
         A_      :				List[Any]		     =					FlaxTopKLogitsWarper(top_k=1				,       filter_value=0.0				,       min_tokens_to_keep=3		)

         A_      :				Optional[Any]		     =					np.broadcast_to(np.arange(_SCREAMING_SNAKE_CASE		)[None, :]				,       (batch_size, length)		).copy()
         A_      :				Dict		     =					top_k_warp_safety_check(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)

         # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
         self.assertListEqual((scores == 0.0).sum(axis=-1		).tolist()				,       [2, 2]		)



       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         A_      :				str		     =					None
         A_      :				Optional[Any]		     =					10
         A_      :				Any		     =					2

         # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
         A_      :				Optional[int]		     =					np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.1_5, 0.3, 0.3, 0.2_5]]		)		)

         A_      :				str		     =					FlaxTopPLogitsWarper(0.8		)
         A_      :				Optional[int]		     =					np.exp(top_p_warp(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)		)

         # dist should be filtered to keep min num values so that sum is >= top_p
         # exp (-inf) => 0
         A_      :				Tuple		     =					np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.2_5]]		)
         self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       atol=1e-3		)		)

         # check edge cases with negative and extreme logits
         A_      :				Union[str, Any]		     =					np.broadcast_to(np.arange(_SCREAMING_SNAKE_CASE		)[None, :]				,       (batch_size, vocab_size)		).copy() - (
             vocab_size // 2
         )

         # make ramp_logits more extreme
         A_      :				str		     =					ramp_logits[1] * 1_0_0.0

         # make sure at least 2 tokens are kept
         A_      :				str		     =					FlaxTopPLogitsWarper(0.9				,       min_tokens_to_keep=2				,       filter_value=0.0		)
         A_      :				str		     =					top_p_warp(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)

         # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
         self.assertListEqual((filtered_dist != 0.0).sum(axis=-1		).tolist()				,       [3, 2]		)



       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         A_      :				str		     =					20
         A_      :				Union[str, Any]		     =					4
         A_      :				Optional[Any]		     =					0

         A_      :				Union[str, Any]		     =					FlaxMinLengthLogitsProcessor(min_length=10				,       eos_token_id=_SCREAMING_SNAKE_CASE		)

         # check that min length is applied at length 5
         A_      :				int		     =					ids_tensor((batch_size, 20)				,       vocab_size=20		)
         A_      :				List[Any]		     =					5
         A_      :				Optional[int]		     =					self._get_uniform_logits(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					min_dist_processor(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
         self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()				,       4 * [-float('''inf'''		)]		)

         # check that min length is not applied anymore at length 15
         A_      :				Tuple		     =					self._get_uniform_logits(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					15
         A_      :				int		     =					min_dist_processor(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
         self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE		).any()		)



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					20
         A_      :				Optional[int]		     =					4
         A_      :				Optional[int]		     =					0

         A_      :				Optional[Any]		     =					FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE		)

         # check that all scores are -inf except the bos_token_id score
         A_      :				Optional[Any]		     =					ids_tensor((batch_size, 1)				,       vocab_size=20		)
         A_      :				str		     =					1
         A_      :				List[str]		     =					self._get_uniform_logits(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				List[str]		     =					logits_processor(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
         self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]		).all()		)
         self.assertListEqual(scores[:, bos_token_id].tolist()				,       4 * [0]		)  # score for bos_token_id shold be zero

         # check that bos_token_id is not forced if current length is greater than 1
         A_      :				Optional[int]		     =					3
         A_      :				List[Any]		     =					self._get_uniform_logits(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					logits_processor(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
         self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE		).any()		)



       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					20
         A_      :				str		     =					4
         A_      :				Dict		     =					0
         A_      :				Optional[int]		     =					5

         A_      :				Tuple		     =					FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE				,       eos_token_id=_SCREAMING_SNAKE_CASE		)

         # check that all scores are -inf except the eos_token_id when max_length is reached
         A_      :				List[Any]		     =					ids_tensor((batch_size, 4)				,       vocab_size=20		)
         A_      :				Any		     =					4
         A_      :				Optional[Any]		     =					self._get_uniform_logits(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					logits_processor(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
         self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]		).all()		)
         self.assertListEqual(scores[:, eos_token_id].tolist()				,       4 * [0]		)  # score for eos_token_id should be zero

         # check that eos_token_id is not forced if max_length is not reached
         A_      :				int		     =					3
         A_      :				Union[str, Any]		     =					self._get_uniform_logits(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					logits_processor(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
         self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE		).any()		)



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				str		     =					4
         A_      :				Dict		     =					10
         A_      :				Union[str, Any]		     =					15
         A_      :				str		     =					2
         A_      :				int		     =					1
         A_      :				List[str]		     =					15

         # dummy input_ids and scores
         A_      :				Tuple		     =					ids_tensor((batch_size, sequence_length)				,       _SCREAMING_SNAKE_CASE		)
         A_      :				int		     =					input_ids.copy()

         A_      :				List[Any]		     =					self._get_uniform_logits(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				Union[str, Any]		     =					scores.copy()

         # instantiate all dist processors
         A_      :				Dict		     =					FlaxTemperatureLogitsWarper(temperature=0.5		)
         A_      :				Any		     =					FlaxTopKLogitsWarper(3		)
         A_      :				List[Any]		     =					FlaxTopPLogitsWarper(0.8		)

         # instantiate all logits processors
         A_      :				List[Any]		     =					FlaxMinLengthLogitsProcessor(min_length=10				,       eos_token_id=_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE				,       eos_token_id=_SCREAMING_SNAKE_CASE		)

         A_      :				Union[str, Any]		     =					10

         # no processor list
         A_      :				int		     =					temp_dist_warp(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
         A_      :				List[str]		     =					top_k_warp(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					top_p_warp(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					min_dist_proc(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					bos_dist_proc(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[Any]		     =					eos_dist_proc(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)

         # with processor list
         A_      :				Any		     =					FlaxLogitsProcessorList(
             [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
         A_      :				List[str]		     =					processor(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)

         # scores should be equal
         self.assertTrue(jnp.allclose(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       atol=1e-3		)		)

         # input_ids should never be changed
         self.assertListEqual(input_ids.tolist()				,       input_ids_comp.tolist()		)







       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				str		     =					4
         A_      :				Dict		     =					10
         A_      :				Tuple		     =					15
         A_      :				List[str]		     =					2
         A_      :				List[str]		     =					1
         A_      :				Union[str, Any]		     =					15

         # dummy input_ids and scores
         A_      :				Any		     =					ids_tensor((batch_size, sequence_length)				,       _SCREAMING_SNAKE_CASE		)
         A_      :				Union[str, Any]		     =					input_ids.copy()

         A_      :				Optional[Any]		     =					self._get_uniform_logits(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					scores.copy()

         # instantiate all dist processors
         A_      :				List[str]		     =					FlaxTemperatureLogitsWarper(temperature=0.5		)
         A_      :				Optional[Any]		     =					FlaxTopKLogitsWarper(3		)
         A_      :				int		     =					FlaxTopPLogitsWarper(0.8		)

         # instantiate all logits processors
         A_      :				List[Any]		     =					FlaxMinLengthLogitsProcessor(min_length=10				,       eos_token_id=_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[Any]		     =					FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE				,       eos_token_id=_SCREAMING_SNAKE_CASE		)

         A_      :				str		     =					10

         # no processor list
         def run_no_processor_list(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
           A_      :				int		     =					temp_dist_warp(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
           A_      :				Optional[Any]		     =					top_k_warp(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
           A_      :				List[Any]		     =					top_p_warp(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
           A_      :				Dict		     =					min_dist_proc(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
           A_      :				Any		     =					bos_dist_proc(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
           A_      :				Optional[Any]		     =					eos_dist_proc(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
           return scores

         # with processor list
         def run_processor_list(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
           A_      :				Optional[int]		     =					FlaxLogitsProcessorList(
               [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
           A_      :				Optional[int]		     =					processor(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       cur_len=_SCREAMING_SNAKE_CASE		)
           return scores

         A_      :				Optional[int]		     =					jax.jit(_SCREAMING_SNAKE_CASE		)
         A_      :				Union[str, Any]		     =					jax.jit(_SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					jitted_run_no_processor_list(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					jitted_run_processor_list(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         # scores should be equal
         self.assertTrue(jnp.allclose(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       atol=1e-3		)		)

         # input_ids should never be changed
         self.assertListEqual(input_ids.tolist()				,       input_ids_comp.tolist()		)




