
"""simple docstring"""


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


A_    							=					logging.get_logger(__name__)

A_    							=					{
    """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Any          =    'blenderbot-small'
				a__:      Tuple          =    ['past_key_values']
				a__:      Union[str, Any]          =    {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




				def __init__(				self							,						UpperCAmelCase=5_0265							,						UpperCAmelCase=512							,						UpperCAmelCase=8							,						UpperCAmelCase=2048							,						UpperCAmelCase=16							,						UpperCAmelCase=8							,						UpperCAmelCase=2048							,						UpperCAmelCase=16							,						UpperCAmelCase=0.0							,						UpperCAmelCase=0.0							,						UpperCAmelCase=True							,						UpperCAmelCase=True							,						UpperCAmelCase="gelu"							,						UpperCAmelCase=512							,						UpperCAmelCase=0.1							,						UpperCAmelCase=0.0							,						UpperCAmelCase=0.0							,						UpperCAmelCase=0.0_2							,						UpperCAmelCase=1							,						UpperCAmelCase=False							,						UpperCAmelCase=0							,						UpperCAmelCase=1							,						UpperCAmelCase=2							,						UpperCAmelCase=2							,						**UpperCAmelCase							,						):
							lowerCamelCase_					=  vocab_size
							lowerCamelCase_					=  max_position_embeddings
							lowerCamelCase_					=  d_model
							lowerCamelCase_					=  encoder_ffn_dim
							lowerCamelCase_					=  encoder_layers
							lowerCamelCase_					=  encoder_attention_heads
							lowerCamelCase_					=  decoder_ffn_dim
							lowerCamelCase_					=  decoder_layers
							lowerCamelCase_					=  decoder_attention_heads
							lowerCamelCase_					=  dropout
							lowerCamelCase_					=  attention_dropout
							lowerCamelCase_					=  activation_dropout
							lowerCamelCase_					=  activation_function
							lowerCamelCase_					=  init_std
							lowerCamelCase_					=  encoder_layerdrop
							lowerCamelCase_					=  decoder_layerdrop
							lowerCamelCase_					=  use_cache
							lowerCamelCase_					=  encoder_layers
							lowerCamelCase_					=  scale_embedding  # scale factor will be sqrt(d_model) if True

							super().__init__(
							    pad_token_id=UpperCAmelCase							,						bos_token_id=UpperCAmelCase							,						eos_token_id=UpperCAmelCase							,						is_encoder_decoder=UpperCAmelCase							,						decoder_start_token_id=UpperCAmelCase							,						forced_eos_token_id=UpperCAmelCase							,						**UpperCAmelCase							,						)




class  __lowerCamelCase  ( lowerCAmelCase			):




				@property
				def        UpperCAmelCase__						(				self   ):
							if self.task in ["default", "seq2seq-lm"]:
										lowerCamelCase_					=  OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
										    ]   )

										if self.use_past:
													lowerCamelCase_					=  {0: '''batch'''}
													lowerCamelCase_					=  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
										else:
													lowerCamelCase_					=  {0: '''batch''', 1: '''decoder_sequence'''}
													lowerCamelCase_					=  {0: '''batch''', 1: '''decoder_sequence'''}

										if self.use_past:
													self.fill_with_past_key_values_(UpperCAmelCase							,						direction='''inputs'''   )
							elif self.task == "causal-lm":
										# TODO: figure this case out.
										lowerCamelCase_					=  OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
										    ]   )
										if self.use_past:
													lowerCamelCase_		, lowerCamelCase_					=  self.num_layers
													for i in range(UpperCAmelCase   ):
																lowerCamelCase_					=  {0: '''batch''', 2: '''past_sequence + sequence'''}
																lowerCamelCase_					=  {0: '''batch''', 2: '''past_sequence + sequence'''}
							else:
										lowerCamelCase_					=  OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
										        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
										        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
										    ]   )

							return common_inputs




				@property
				def        UpperCAmelCase__						(				self   ):
							if self.task in ["default", "seq2seq-lm"]:
										lowerCamelCase_					=  super().outputs
							else:
										lowerCamelCase_					=  super(UpperCAmelCase							,						self   ).outputs
										if self.use_past:
													lowerCamelCase_		, lowerCamelCase_					=  self.num_layers
													for i in range(UpperCAmelCase   ):
																lowerCamelCase_					=  {0: '''batch''', 2: '''past_sequence + sequence'''}
																lowerCamelCase_					=  {0: '''batch''', 2: '''past_sequence + sequence'''}
							return common_outputs




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = -1							,						UpperCAmelCase = -1							,						UpperCAmelCase = False							,						UpperCAmelCase = None							,						):
							lowerCamelCase_					=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )

							# Generate decoder inputs
							lowerCamelCase_					=  seq_length if not self.use_past else 1
							lowerCamelCase_					=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
							lowerCamelCase_					=  {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
							lowerCamelCase_					=  dict(**UpperCAmelCase							,						**UpperCAmelCase   )

							if self.use_past:
										if not is_torch_available():
													raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
										else:
													import torch
										lowerCamelCase_		, lowerCamelCase_					=  common_inputs['''input_ids'''].shape
										lowerCamelCase_					=  common_inputs['''decoder_input_ids'''].shape[1]
										lowerCamelCase_		, lowerCamelCase_					=  self.num_attention_heads
										lowerCamelCase_					=  (
										    batch,
										    num_encoder_attention_heads,
										    encoder_seq_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)
										lowerCamelCase_					=  decoder_seq_length + 3
										lowerCamelCase_					=  (
										    batch,
										    num_decoder_attention_heads,
										    decoder_past_length,
										    self._config.hidden_size // num_decoder_attention_heads,
										)

										lowerCamelCase_					=  torch.cat(
										    [common_inputs['''decoder_attention_mask'''], torch.ones(UpperCAmelCase							,						UpperCAmelCase   )]							,						dim=1   )

										lowerCamelCase_					=  []
										# If the number of encoder and decoder layers are present in the model configuration, both are considered
										lowerCamelCase_		, lowerCamelCase_					=  self.num_layers
										lowerCamelCase_					=  min(UpperCAmelCase							,						UpperCAmelCase   )
										lowerCamelCase_					=  max(UpperCAmelCase							,						UpperCAmelCase   ) - min_num_layers
										lowerCamelCase_					=  '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

										for _ in range(UpperCAmelCase   ):
													common_inputs["past_key_values"].append(
													    (
													        torch.zeros(UpperCAmelCase   ),
													        torch.zeros(UpperCAmelCase   ),
													        torch.zeros(UpperCAmelCase   ),
													        torch.zeros(UpperCAmelCase   ),
													    )   )
										# TODO: test this.
										lowerCamelCase_					=  encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
										for _ in range(UpperCAmelCase							,						UpperCAmelCase   ):
													common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase   ), torch.zeros(UpperCAmelCase   ))   )
							return common_inputs




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = -1							,						UpperCAmelCase = -1							,						UpperCAmelCase = False							,						UpperCAmelCase = None							,						):
							lowerCamelCase_					=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )

							if self.use_past:
										if not is_torch_available():
													raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
										else:
													import torch
										lowerCamelCase_		, lowerCamelCase_					=  common_inputs['''input_ids'''].shape
										# Not using the same length for past_key_values
										lowerCamelCase_					=  seqlen + 2
										lowerCamelCase_		, lowerCamelCase_					=  self.num_layers
										lowerCamelCase_		, lowerCamelCase_					=  self.num_attention_heads
										lowerCamelCase_					=  (
										    batch,
										    num_encoder_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)

										lowerCamelCase_					=  common_inputs['''attention_mask'''].dtype
										lowerCamelCase_					=  torch.cat(
										    [common_inputs['''attention_mask'''], torch.ones(UpperCAmelCase							,						UpperCAmelCase							,						dtype=UpperCAmelCase   )]							,						dim=1   )
										lowerCamelCase_					=  [
										    (torch.zeros(UpperCAmelCase   ), torch.zeros(UpperCAmelCase   )) for _ in range(UpperCAmelCase   )
										]
							return common_inputs




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = -1							,						UpperCAmelCase = -1							,						UpperCAmelCase = False							,						UpperCAmelCase = None							,						):
							# Copied from OnnxConfig.generate_dummy_inputs
							# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							lowerCamelCase_					=  compute_effective_axis_dimension(
							    UpperCAmelCase							,						fixed_dimension=OnnxConfig.default_fixed_batch							,						num_token_to_add=0   )

							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							lowerCamelCase_					=  tokenizer.num_special_tokens_to_add(UpperCAmelCase   )
							lowerCamelCase_					=  compute_effective_axis_dimension(
							    UpperCAmelCase							,						fixed_dimension=OnnxConfig.default_fixed_sequence							,						num_token_to_add=UpperCAmelCase   )

							# Generate dummy inputs according to compute batch and sequence
							lowerCamelCase_					=  [''' '''.join([tokenizer.unk_token]   ) * seq_length] * batch_size
							lowerCamelCase_					=  dict(tokenizer(UpperCAmelCase							,						return_tensors=UpperCAmelCase   )   )
							return common_inputs




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = -1							,						UpperCAmelCase = -1							,						UpperCAmelCase = False							,						UpperCAmelCase = None							,						):
							if self.task in ["default", "seq2seq-lm"]:
										lowerCamelCase_					=  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
										    UpperCAmelCase							,						batch_size=UpperCAmelCase							,						seq_length=UpperCAmelCase							,						is_pair=UpperCAmelCase							,						framework=UpperCAmelCase   )

							elif self.task == "causal-lm":
										lowerCamelCase_					=  self._generate_dummy_inputs_for_causal_lm(
										    UpperCAmelCase							,						batch_size=UpperCAmelCase							,						seq_length=UpperCAmelCase							,						is_pair=UpperCAmelCase							,						framework=UpperCAmelCase   )
							else:
										lowerCamelCase_					=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    UpperCAmelCase							,						batch_size=UpperCAmelCase							,						seq_length=UpperCAmelCase							,						is_pair=UpperCAmelCase							,						framework=UpperCAmelCase   )

							return common_inputs





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
							if self.task in ["default", "seq2seq-lm"]:
										lowerCamelCase_					=  super()._flatten_past_key_values_(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
							else:
										lowerCamelCase_					=  super(UpperCAmelCase							,						self   )._flatten_past_key_values_(
										    UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )





"""simple docstring"""


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			):




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  jnp.ones((batch_size, length)   ) / length
							return scores




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  None
							lowerCamelCase_					=  20

							lowerCamelCase_					=  self._get_uniform_logits(batch_size=2							,						length=UpperCAmelCase   )

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

							# compute softmax
							lowerCamelCase_					=  jax.nn.softmax(UpperCAmelCase							,						axis=-1   )

							lowerCamelCase_					=  FlaxTemperatureLogitsWarper(temperature=0.5   )
							lowerCamelCase_					=  FlaxTemperatureLogitsWarper(temperature=1.3   )

							lowerCamelCase_					=  jax.nn.softmax(temp_dist_warper_sharper(UpperCAmelCase							,						scores.copy()							,						cur_len=UpperCAmelCase   )							,						axis=-1   )
							lowerCamelCase_					=  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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  None
							lowerCamelCase_					=  10
							lowerCamelCase_					=  2

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

							lowerCamelCase_					=  FlaxTopKLogitsWarper(3   )

							lowerCamelCase_					=  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
							lowerCamelCase_					=  5
							lowerCamelCase_					=  FlaxTopKLogitsWarper(top_k=1							,						filter_value=0.0							,						min_tokens_to_keep=3   )

							lowerCamelCase_					=  np.broadcast_to(np.arange(UpperCAmelCase   )[None, :]							,						(batch_size, length)   ).copy()
							lowerCamelCase_					=  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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  None
							lowerCamelCase_					=  10
							lowerCamelCase_					=  2

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

							lowerCamelCase_					=  FlaxTopPLogitsWarper(0.8   )
							lowerCamelCase_					=  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
							lowerCamelCase_					=  np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.2_5]]   )
							self.assertTrue(np.allclose(UpperCAmelCase							,						UpperCAmelCase							,						atol=1e-3   )   )

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

							# make ramp_logits more extreme
							lowerCamelCase_					=  ramp_logits[1] * 1_0_0.0

							# make sure at least 2 tokens are kept
							lowerCamelCase_					=  FlaxTopPLogitsWarper(0.9							,						min_tokens_to_keep=2							,						filter_value=0.0   )
							lowerCamelCase_					=  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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  20
							lowerCamelCase_					=  4
							lowerCamelCase_					=  0

							lowerCamelCase_					=  FlaxMinLengthLogitsProcessor(min_length=10							,						eos_token_id=UpperCAmelCase   )

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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  20
							lowerCamelCase_					=  4
							lowerCamelCase_					=  0

							lowerCamelCase_					=  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase   )

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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  20
							lowerCamelCase_					=  4
							lowerCamelCase_					=  0
							lowerCamelCase_					=  5

							lowerCamelCase_					=  FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase							,						eos_token_id=UpperCAmelCase   )

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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  4
							lowerCamelCase_					=  10
							lowerCamelCase_					=  15
							lowerCamelCase_					=  2
							lowerCamelCase_					=  1
							lowerCamelCase_					=  15

							# dummy input_ids and scores
							lowerCamelCase_					=  ids_tensor((batch_size, sequence_length)							,						UpperCAmelCase   )
							lowerCamelCase_					=  input_ids.copy()

							lowerCamelCase_					=  self._get_uniform_logits(UpperCAmelCase							,						UpperCAmelCase   )
							lowerCamelCase_					=  scores.copy()

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

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

							lowerCamelCase_					=  10

							# no processor list
							lowerCamelCase_					=  temp_dist_warp(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
							lowerCamelCase_					=  top_k_warp(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
							lowerCamelCase_					=  top_p_warp(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
							lowerCamelCase_					=  min_dist_proc(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
							lowerCamelCase_					=  bos_dist_proc(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
							lowerCamelCase_					=  eos_dist_proc(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )

							# with processor list
							lowerCamelCase_					=  FlaxLogitsProcessorList(
							    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]   )
							lowerCamelCase_					=  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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  4
							lowerCamelCase_					=  10
							lowerCamelCase_					=  15
							lowerCamelCase_					=  2
							lowerCamelCase_					=  1
							lowerCamelCase_					=  15

							# dummy input_ids and scores
							lowerCamelCase_					=  ids_tensor((batch_size, sequence_length)							,						UpperCAmelCase   )
							lowerCamelCase_					=  input_ids.copy()

							lowerCamelCase_					=  self._get_uniform_logits(UpperCAmelCase							,						UpperCAmelCase   )
							lowerCamelCase_					=  scores.copy()

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

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

							lowerCamelCase_					=  10

							# no processor list
							def run_no_processor_list(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
										lowerCamelCase_					=  temp_dist_warp(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
										lowerCamelCase_					=  top_k_warp(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
										lowerCamelCase_					=  top_p_warp(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
										lowerCamelCase_					=  min_dist_proc(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
										lowerCamelCase_					=  bos_dist_proc(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
										lowerCamelCase_					=  eos_dist_proc(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
										return scores

							# with processor list
							def run_processor_list(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
										lowerCamelCase_					=  FlaxLogitsProcessorList(
										    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]   )
										lowerCamelCase_					=  processor(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
										return scores

							lowerCamelCase_					=  jax.jit(UpperCAmelCase   )
							lowerCamelCase_					=  jax.jit(UpperCAmelCase   )

							lowerCamelCase_					=  jitted_run_no_processor_list(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
							lowerCamelCase_					=  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()   )



