




from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers.models.xglm.modeling_tf_xglm import (
        TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXGLMForCausalLM,
        TFXGLMModel,
    )


@require_tf
class        UpperCamelCase_					:

    '''simple docstring'''






    UpperCAmelCase__													=   XGLMConfig
    UpperCAmelCase__													=   {}
    UpperCAmelCase__													=   '''gelu'''


    def __init__(    self   :			str      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Union[str, Any]=14      ,				UpperCAmelCase__   :			Tuple=7      ,				UpperCAmelCase__   :			List[Any]=True      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Dict=True      ,				UpperCAmelCase__   :			Any=99      ,				UpperCAmelCase__   :			Union[str, Any]=32      ,				UpperCAmelCase__   :			Any=2      ,				UpperCAmelCase__   :			Dict=4      ,				UpperCAmelCase__   :			List[str]=37      ,				UpperCAmelCase__   :			Union[str, Any]="gelu"      ,				UpperCAmelCase__   :			Tuple=0.1      ,				UpperCAmelCase__   :			Any=0.1      ,				UpperCAmelCase__   :			Any=512      ,				UpperCAmelCase__   :			List[Any]=0.02      ,				)		->str:





           '''simple docstring'''

           A__   				=					parent
           A__   				=					batch_size
           A__   				=					seq_length
           A__   				=					is_training
           A__   				=					use_input_mask
           A__   				=					use_labels
           A__   				=					vocab_size
           A__   				=					d_model
           A__   				=					num_hidden_layers
           A__   				=					num_attention_heads
           A__   				=					ffn_dim
           A__   				=					activation_function
           A__   				=					activation_dropout
           A__   				=					attention_dropout
           A__   				=					max_position_embeddings
           A__   				=					initializer_range
           A__   				=					None
           A__   				=					0
           A__   				=					2
           A__   				=					1


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Optional[int]:





           '''simple docstring'''

           return XGLMConfig.from_pretrained('''facebook/xglm-564M''')


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Tuple:





           '''simple docstring'''

           A__   				=					tf.clip_by_value(
               ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size)      ,				clip_value_min=0      ,				clip_value_max=3)

           A__   				=					None
           if self.use_input_mask:
                  A__   				=					random_attention_mask([self.batch_size, self.seq_length])

           A__   				=					self.get_config()

           A__   				=					floats_tensor([self.num_hidden_layers, self.num_attention_heads]      ,				2)

           return (
               config,
               input_ids,
               input_mask,
               head_mask,
           )


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Dict:





           '''simple docstring'''

           return XGLMConfig(
               vocab_size=self.vocab_size      ,				d_model=self.hidden_size      ,				num_layers=self.num_hidden_layers      ,				attention_heads=self.num_attention_heads      ,				ffn_dim=self.ffn_dim      ,				activation_function=self.activation_function      ,				activation_dropout=self.activation_dropout      ,				attention_dropout=self.attention_dropout      ,				max_position_embeddings=self.max_position_embeddings      ,				initializer_range=self.initializer_range      ,				use_cache=UpperCAmelCase__      ,				bos_token_id=self.bos_token_id      ,				eos_token_id=self.eos_token_id      ,				pad_token_id=self.pad_token_id      ,				return_dict=UpperCAmelCase__      ,				)





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





           '''simple docstring'''

           A__   				=					self.prepare_config_and_inputs()

           (
               (
               A__							
           )				,							(
               A__							
           )				,							(
               A__							
           )				,							(
               A__							
           )				,														
           )   				=					config_and_inputs

           A__   				=					{
               '''input_ids''': input_ids,
               '''head_mask''': head_mask,
           }

           return config, inputs_dict


@require_tf
class        UpperCamelCase_					(				UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
    UpperCAmelCase__													=   (TFXGLMForCausalLM,) if is_tf_available() else ()
    UpperCAmelCase__													=   (
        {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
    )
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False


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





           '''simple docstring'''

           A__   				=					TFXGLMModelTester(self)
           A__   				=					ConfigTester(self      ,				config_class=UpperCAmelCase__      ,				n_embd=37)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->str:





           '''simple docstring'''

           self.config_tester.run_common_tests()


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->List[str]:





           '''simple docstring'''

           for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__   				=					TFXGLMModel.from_pretrained(UpperCAmelCase__)
                  self.assertIsNotNone(UpperCAmelCase__)





    @unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.''')
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[int]:





           '''simple docstring'''

           super().test_resize_token_embeddings()


@require_tf
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Optional[Any]=True)		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')
           A__   				=					tf.convert_to_tensor([[2, 268, 9_865]]      ,				dtype=tf.intaa)  # The dog
           # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
           # fmt: off
           A__   				=					[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
           # fmt: on
           A__   				=					model.generate(UpperCAmelCase__      ,				do_sample=UpperCAmelCase__      ,				num_beams=1)
           if verify_outputs:
                  self.assertListEqual(output_ids[0].numpy().tolist()      ,				UpperCAmelCase__)


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[int]:





           '''simple docstring'''

           A__   				=					XGLMTokenizer.from_pretrained('''facebook/xglm-564M''')
           A__   				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')

           tf.random.set_seed(0)
           A__   				=					tokenizer('''Today is a nice day and'''      ,				return_tensors='''tf''')
           A__   				=					tokenized.input_ids
           # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
           with tf.device(''':/CPU:0'''):
                  A__   				=					model.generate(UpperCAmelCase__      ,				do_sample=UpperCAmelCase__      ,				seed=[7, 0])
           A__   				=					tokenizer.decode(output_ids[0]      ,				skip_special_tokens=UpperCAmelCase__)

           A__   				=					(
               '''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
           )
           self.assertEqual(UpperCAmelCase__      ,				UpperCAmelCase__)





    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')
           A__   				=					XGLMTokenizer.from_pretrained('''facebook/xglm-564M''')

           A__   				=					'''left'''

           # use different length sentences to test batching
           A__   				=					[
               '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
               '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
               '''regardless of whether left padding is applied or not. When''',
               '''Hello, my dog is a little''',
           ]

           A__   				=					tokenizer(UpperCAmelCase__      ,				return_tensors='''tf'''      ,				padding=UpperCAmelCase__)
           A__   				=					inputs['''input_ids''']

           A__   				=					model.generate(input_ids=UpperCAmelCase__      ,				attention_mask=inputs['''attention_mask''']      ,				max_new_tokens=12)

           A__   				=					tokenizer(sentences[0]      ,				return_tensors='''tf''').input_ids
           A__   				=					model.generate(input_ids=UpperCAmelCase__      ,				max_new_tokens=12)

           A__   				=					tokenizer(sentences[1]      ,				return_tensors='''tf''').input_ids
           A__   				=					model.generate(input_ids=UpperCAmelCase__      ,				max_new_tokens=12)

           A__   				=					tokenizer.batch_decode(UpperCAmelCase__      ,				skip_special_tokens=UpperCAmelCase__)
           A__   				=					tokenizer.decode(output_non_padded[0]      ,				skip_special_tokens=UpperCAmelCase__)
           A__   				=					tokenizer.decode(output_padded[0]      ,				skip_special_tokens=UpperCAmelCase__)

           A__   				=					[
               '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
               '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
               '''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
               '''a single''',
               '''Hello, my dog is a little bit of a shy one, but he is very friendly''',
           ]
           self.assertListEqual(UpperCAmelCase__      ,				UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__      ,				[non_padded_sentence, padded_sentence])








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        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			int)		->Dict:





           '''simple docstring'''

           A__   				=					jnp.ones((batch_size, length)) / length
           return scores


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->int:





           '''simple docstring'''

           A__   				=					None
           A__   				=					20

           A__   				=					self._get_uniform_logits(batch_size=2      ,				length=UpperCAmelCase__)

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

           # compute softmax
           A__   				=					jax.nn.softmax(UpperCAmelCase__      ,				axis=-1)

           A__   				=					FlaxTemperatureLogitsWarper(temperature=0.5)
           A__   				=					FlaxTemperatureLogitsWarper(temperature=1.3)

           A__   				=					jax.nn.softmax(temp_dist_warper_sharper(UpperCAmelCase__      ,				scores.copy()      ,				cur_len=UpperCAmelCase__)      ,				axis=-1)
           A__   				=					jax.nn.softmax(temp_dist_warper_smoother(UpperCAmelCase__      ,				scores.copy()      ,				cur_len=UpperCAmelCase__)      ,				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  SCREAMING_SNAKE_CASE							(    self   :			str)		->int:





           '''simple docstring'''

           A__   				=					None
           A__   				=					10
           A__   				=					2

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

           A__   				=					FlaxTopKLogitsWarper(3)

           A__   				=					top_k_warp(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)

           # 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__   				=					5
           A__   				=					FlaxTopKLogitsWarper(top_k=1      ,				filter_value=0.0      ,				min_tokens_to_keep=3)

           A__   				=					np.broadcast_to(np.arange(UpperCAmelCase__)[None, :]      ,				(batch_size, length)).copy()
           A__   				=					top_k_warp_safety_check(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)

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


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					None
           A__   				=					10
           A__   				=					2

           # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
           A__   				=					np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]))

           A__   				=					FlaxTopPLogitsWarper(0.8)
           A__   				=					np.exp(top_p_warp(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__))

           # dist should be filtered to keep min num values so that sum is >= top_p
           # exp (-inf) => 0
           A__   				=					np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]])
           self.assertTrue(np.allclose(UpperCAmelCase__      ,				UpperCAmelCase__      ,				atol=1e-3))

           # check edge cases with negative and extreme logits
           A__   				=					np.broadcast_to(np.arange(UpperCAmelCase__)[None, :]      ,				(batch_size, vocab_size)).copy() - (
               vocab_size // 2
           )

           # make ramp_logits more extreme
           A__   				=					ramp_logits[1] * 100.0

           # make sure at least 2 tokens are kept
           A__   				=					FlaxTopPLogitsWarper(0.9      ,				min_tokens_to_keep=2      ,				filter_value=0.0)
           A__   				=					top_p_warp(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)

           # 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  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Tuple:





           '''simple docstring'''

           A__   				=					20
           A__   				=					4
           A__   				=					0

           A__   				=					FlaxMinLengthLogitsProcessor(min_length=10      ,				eos_token_id=UpperCAmelCase__)

           # check that min length is applied at length 5
           A__   				=					ids_tensor((batch_size, 20)      ,				vocab_size=20)
           A__   				=					5
           A__   				=					self._get_uniform_logits(UpperCAmelCase__      ,				UpperCAmelCase__)
           A__   				=					min_dist_processor(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
           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__   				=					self._get_uniform_logits(UpperCAmelCase__      ,				UpperCAmelCase__)
           A__   				=					15
           A__   				=					min_dist_processor(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
           self.assertFalse(jnp.isinf(UpperCAmelCase__).any())


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					20
           A__   				=					4
           A__   				=					0

           A__   				=					FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase__)

           # check that all scores are -inf except the bos_token_id score
           A__   				=					ids_tensor((batch_size, 1)      ,				vocab_size=20)
           A__   				=					1
           A__   				=					self._get_uniform_logits(UpperCAmelCase__      ,				UpperCAmelCase__)
           A__   				=					logits_processor(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
           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__   				=					3
           A__   				=					self._get_uniform_logits(UpperCAmelCase__      ,				UpperCAmelCase__)
           A__   				=					logits_processor(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
           self.assertFalse(jnp.isinf(UpperCAmelCase__).any())


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->str:





           '''simple docstring'''

           A__   				=					20
           A__   				=					4
           A__   				=					0
           A__   				=					5

           A__   				=					FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase__      ,				eos_token_id=UpperCAmelCase__)

           # check that all scores are -inf except the eos_token_id when max_length is reached
           A__   				=					ids_tensor((batch_size, 4)      ,				vocab_size=20)
           A__   				=					4
           A__   				=					self._get_uniform_logits(UpperCAmelCase__      ,				UpperCAmelCase__)
           A__   				=					logits_processor(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
           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__   				=					3
           A__   				=					self._get_uniform_logits(UpperCAmelCase__      ,				UpperCAmelCase__)
           A__   				=					logits_processor(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
           self.assertFalse(jnp.isinf(UpperCAmelCase__).any())


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Tuple:





           '''simple docstring'''

           A__   				=					4
           A__   				=					10
           A__   				=					15
           A__   				=					2
           A__   				=					1
           A__   				=					15

           # dummy input_ids and scores
           A__   				=					ids_tensor((batch_size, sequence_length)      ,				UpperCAmelCase__)
           A__   				=					input_ids.copy()

           A__   				=					self._get_uniform_logits(UpperCAmelCase__      ,				UpperCAmelCase__)
           A__   				=					scores.copy()

           # instantiate all dist processors
           A__   				=					FlaxTemperatureLogitsWarper(temperature=0.5)
           A__   				=					FlaxTopKLogitsWarper(3)
           A__   				=					FlaxTopPLogitsWarper(0.8)

           # instantiate all logits processors
           A__   				=					FlaxMinLengthLogitsProcessor(min_length=10      ,				eos_token_id=UpperCAmelCase__)
           A__   				=					FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase__)
           A__   				=					FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase__      ,				eos_token_id=UpperCAmelCase__)

           A__   				=					10

           # no processor list
           A__   				=					temp_dist_warp(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
           A__   				=					top_k_warp(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
           A__   				=					top_p_warp(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
           A__   				=					min_dist_proc(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
           A__   				=					bos_dist_proc(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
           A__   				=					eos_dist_proc(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)

           # with processor list
           A__   				=					FlaxLogitsProcessorList(
               [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
           A__   				=					processor(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)

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

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





    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->int:





           '''simple docstring'''

           A__   				=					4
           A__   				=					10
           A__   				=					15
           A__   				=					2
           A__   				=					1
           A__   				=					15

           # dummy input_ids and scores
           A__   				=					ids_tensor((batch_size, sequence_length)      ,				UpperCAmelCase__)
           A__   				=					input_ids.copy()

           A__   				=					self._get_uniform_logits(UpperCAmelCase__      ,				UpperCAmelCase__)
           A__   				=					scores.copy()

           # instantiate all dist processors
           A__   				=					FlaxTemperatureLogitsWarper(temperature=0.5)
           A__   				=					FlaxTopKLogitsWarper(3)
           A__   				=					FlaxTopPLogitsWarper(0.8)

           # instantiate all logits processors
           A__   				=					FlaxMinLengthLogitsProcessor(min_length=10      ,				eos_token_id=UpperCAmelCase__)
           A__   				=					FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase__)
           A__   				=					FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase__      ,				eos_token_id=UpperCAmelCase__)

           A__   				=					10

           # no processor list
           def run_no_processor_list(UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			Union[str, Any]):
                  A__   				=					temp_dist_warp(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
                  A__   				=					top_k_warp(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
                  A__   				=					top_p_warp(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
                  A__   				=					min_dist_proc(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
                  A__   				=					bos_dist_proc(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
                  A__   				=					eos_dist_proc(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
                  return scores

           # with processor list
           def run_processor_list(UpperCAmelCase__   :			Any      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			Optional[Any]):
                  A__   				=					FlaxLogitsProcessorList(
                      [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
                  A__   				=					processor(UpperCAmelCase__      ,				UpperCAmelCase__      ,				cur_len=UpperCAmelCase__)
                  return scores

           A__   				=					jax.jit(UpperCAmelCase__)
           A__   				=					jax.jit(UpperCAmelCase__)

           A__   				=					jitted_run_no_processor_list(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__)
           A__   				=					jitted_run_processor_list(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__)

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

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


