




import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import BertGenerationDecoder, BertGenerationEncoder


class        UpperCamelCase_					:

    '''simple docstring'''






    def __init__(    self   :			Optional[int]      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			int=13      ,				UpperCAmelCase__   :			List[Any]=7      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Dict=True      ,				UpperCAmelCase__   :			str=99      ,				UpperCAmelCase__   :			str=32      ,				UpperCAmelCase__   :			int=5      ,				UpperCAmelCase__   :			int=4      ,				UpperCAmelCase__   :			Dict=37      ,				UpperCAmelCase__   :			Optional[int]="gelu"      ,				UpperCAmelCase__   :			Any=0.1      ,				UpperCAmelCase__   :			Any=0.1      ,				UpperCAmelCase__   :			int=50      ,				UpperCAmelCase__   :			Optional[Any]=0.02      ,				UpperCAmelCase__   :			Any=True      ,				UpperCAmelCase__   :			Optional[int]=None      ,				)		->Any:





           '''simple docstring'''

           A__   				=					parent
           A__   				=					batch_size
           A__   				=					seq_length
           A__   				=					is_training
           A__   				=					use_input_mask
           A__   				=					vocab_size
           A__   				=					hidden_size
           A__   				=					num_hidden_layers
           A__   				=					num_attention_heads
           A__   				=					intermediate_size
           A__   				=					hidden_act
           A__   				=					hidden_dropout_prob
           A__   				=					attention_probs_dropout_prob
           A__   				=					max_position_embeddings
           A__   				=					initializer_range
           A__   				=					use_labels
           A__   				=					scope


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->str:





           '''simple docstring'''

           A__   				=					ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size)

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

           if self.use_labels:
                  A__   				=					ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size)

           A__   				=					self.get_config()

           return config, input_ids, input_mask, token_labels


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





           '''simple docstring'''

           return BertGenerationConfig(
               vocab_size=self.vocab_size      ,				hidden_size=self.hidden_size      ,				num_hidden_layers=self.num_hidden_layers      ,				num_attention_heads=self.num_attention_heads      ,				intermediate_size=self.intermediate_size      ,				hidden_act=self.hidden_act      ,				hidden_dropout_prob=self.hidden_dropout_prob      ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,				max_position_embeddings=self.max_position_embeddings      ,				is_decoder=UpperCAmelCase__      ,				initializer_range=self.initializer_range      ,				)


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





           '''simple docstring'''

           (
               (
               A__					
           )				,							(
               A__					
           )				,							(
               A__					
           )				,							(
               A__					
           )				,												
           )   				=					self.prepare_config_and_inputs()

           A__   				=					True
           A__   				=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
           A__   				=					ids_tensor([self.batch_size, self.seq_length]      ,				vocab_size=2)

           return (
               config,
               input_ids,
               input_mask,
               token_labels,
               encoder_hidden_states,
               encoder_attention_mask,
           )


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			Dict      ,				**UpperCAmelCase__   :			List[Any]      ,				)		->Dict:





           '''simple docstring'''

           A__   				=					BertGenerationEncoder(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__   				=					model(UpperCAmelCase__      ,				attention_mask=UpperCAmelCase__)
           A__   				=					model(UpperCAmelCase__)
           self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, self.seq_length, self.hidden_size))


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Any      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			Any      ,				UpperCAmelCase__   :			int      ,				**UpperCAmelCase__   :			Optional[int]      ,				)		->Dict:





           '''simple docstring'''

           A__   				=					True
           A__   				=					BertGenerationEncoder(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__   				=					model(
               UpperCAmelCase__      ,				attention_mask=UpperCAmelCase__      ,				encoder_hidden_states=UpperCAmelCase__      ,				encoder_attention_mask=UpperCAmelCase__      ,				)
           A__   				=					model(
               UpperCAmelCase__      ,				attention_mask=UpperCAmelCase__      ,				encoder_hidden_states=UpperCAmelCase__      ,				)
           self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, self.seq_length, self.hidden_size))


    def  SCREAMING_SNAKE_CASE							(    self   :			Any      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Any      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Any      ,				**UpperCAmelCase__   :			List[Any]      ,				)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					True
           A__   				=					True
           A__   				=					BertGenerationDecoder(config=UpperCAmelCase__).to(UpperCAmelCase__).eval()

           # first forward pass
           A__   				=					model(
               UpperCAmelCase__      ,				attention_mask=UpperCAmelCase__      ,				encoder_hidden_states=UpperCAmelCase__      ,				encoder_attention_mask=UpperCAmelCase__      ,				use_cache=UpperCAmelCase__      ,				)
           A__   				=					outputs.past_key_values

           # create hypothetical multiple next token and extent to next_input_ids
           A__   				=					ids_tensor((self.batch_size, 3)      ,				config.vocab_size)
           A__   				=					ids_tensor((self.batch_size, 3)      ,				vocab_size=2)

           # append to next input_ids and
           A__   				=					torch.cat([input_ids, next_tokens]      ,				dim=-1)
           A__   				=					torch.cat([input_mask, next_mask]      ,				dim=-1)

           A__   				=					model(
               UpperCAmelCase__      ,				attention_mask=UpperCAmelCase__      ,				encoder_hidden_states=UpperCAmelCase__      ,				encoder_attention_mask=UpperCAmelCase__      ,				output_hidden_states=UpperCAmelCase__      ,				)['''hidden_states'''][0]
           A__   				=					model(
               UpperCAmelCase__      ,				attention_mask=UpperCAmelCase__      ,				encoder_hidden_states=UpperCAmelCase__      ,				encoder_attention_mask=UpperCAmelCase__      ,				past_key_values=UpperCAmelCase__      ,				output_hidden_states=UpperCAmelCase__      ,				)['''hidden_states'''][0]

           # select random slice
           A__   				=					ids_tensor((1,)      ,				output_from_past.shape[-1]).item()
           A__   				=					output_from_no_past[:, -3:, random_slice_idx].detach()
           A__   				=					output_from_past[:, :, random_slice_idx].detach()

           self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

           # test that outputs are equal for slice
           self.parent.assertTrue(torch.allclose(UpperCAmelCase__      ,				UpperCAmelCase__      ,				atol=1e-3))


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			Any      ,				*UpperCAmelCase__   :			Union[str, Any]      ,				)		->Tuple:





           '''simple docstring'''

           A__   				=					BertGenerationDecoder(UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__   				=					model(UpperCAmelCase__      ,				attention_mask=UpperCAmelCase__      ,				labels=UpperCAmelCase__)
           self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length, self.vocab_size))





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





           '''simple docstring'''

           A__				,							A__				,							A__				,							A__   				=					self.prepare_config_and_inputs()
           A__   				=					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
           return config, inputs_dict


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

    '''simple docstring'''






    UpperCAmelCase__													=   (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
    UpperCAmelCase__													=   (BertGenerationDecoder,) if is_torch_available() else ()
    UpperCAmelCase__													=   (
        {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
        if is_torch_available()
        else {}
    )


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





           '''simple docstring'''

           A__   				=					BertGenerationEncoderTester(self)
           A__   				=					ConfigTester(self      ,				config_class=UpperCAmelCase__      ,				hidden_size=37)


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Tuple:





           '''simple docstring'''

           self.config_tester.run_common_tests()


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->str:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase__)


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





           '''simple docstring'''

           A__				,							A__				,							A__				,							A__   				=					self.model_tester.prepare_config_and_inputs()
           A__   				=					'''bert'''
           self.model_tester.create_and_check_model(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Any:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*UpperCAmelCase__)


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





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCAmelCase__)


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





           '''simple docstring'''

           (
               (
               A__					
           )				,							(
               A__					
           )				,							(
               A__					
           )				,							(
               A__					
           )				,							(
               A__					
           )				,							(
               A__					
           )				,												
           )   				=					self.model_tester.prepare_config_and_inputs_for_decoder()

           A__   				=					None

           self.model_tester.create_and_check_model_as_decoder(
               UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				)


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





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_for_causal_lm(*UpperCAmelCase__)





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





           '''simple docstring'''

           A__   				=					BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''')
           self.assertIsNotNone(UpperCAmelCase__)


@require_torch
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






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





           '''simple docstring'''

           A__   				=					BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''')
           A__   				=					torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]])
           with torch.no_grad():
                  A__   				=					model(UpperCAmelCase__)[0]
           A__   				=					torch.Size([1, 8, 1_024])
           self.assertEqual(output.shape      ,				UpperCAmelCase__)
           A__   				=					torch.tensor(
               [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]])
           self.assertTrue(torch.allclose(output[:, :3, :3]      ,				UpperCAmelCase__      ,				atol=1e-4))


@require_torch
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






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





           '''simple docstring'''

           A__   				=					BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''')
           A__   				=					torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]])
           with torch.no_grad():
                  A__   				=					model(UpperCAmelCase__)[0]
           A__   				=					torch.Size([1, 8, 50_358])
           self.assertEqual(output.shape      ,				UpperCAmelCase__)
           A__   				=					torch.tensor(
               [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]])
           self.assertTrue(torch.allclose(output[:, :3, :3]      ,				UpperCAmelCase__      ,				atol=1e-4))








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())


