




'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
							import tensorflow as tf

							from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel






@require_tf
class 		a							:
				snake_case_           =						BlenderbotConfig
				snake_case_           =						{}
				snake_case_           =						"gelu"


				def __init__(						self       :   Optional[int]			,							lowercase_       :   str			,							lowercase_       :   Dict=13			,							lowercase_       :   Optional[Any]=7			,							lowercase_       :   Optional[int]=True			,							lowercase_       :   Optional[Any]=False			,							lowercase_       :   List[Any]=99			,							lowercase_       :   Optional[int]=32			,							lowercase_       :   List[Any]=2			,							lowercase_       :   Optional[int]=4			,							lowercase_       :   int=37			,							lowercase_       :   List[str]=0.1			,							lowercase_       :   Optional[int]=0.1			,							lowercase_       :   Dict=20			,							lowercase_       :   Union[str, Any]=2			,							lowercase_       :   int=1			,							lowercase_       :   Optional[int]=0			,							):
									snake_case_		     =							parent
									snake_case_		     =							batch_size
									snake_case_		     =							seq_length
									snake_case_		     =							is_training
									snake_case_		     =							use_labels
									snake_case_		     =							vocab_size
									snake_case_		     =							hidden_size
									snake_case_		     =							num_hidden_layers
									snake_case_		     =							num_attention_heads
									snake_case_		     =							intermediate_size

									snake_case_		     =							hidden_dropout_prob
									snake_case_		     =							attention_probs_dropout_prob
									snake_case_		     =							max_position_embeddings
									snake_case_		     =							eos_token_id
									snake_case_		     =							pad_token_id
									snake_case_		     =							bos_token_id


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							ids_tensor([self.batch_size, self.seq_length - 1]			,							self.vocab_size   )
									snake_case_		     =							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )			,							1   )
									snake_case_		     =							tf.concat([input_ids, eos_tensor]			,							axis=1   )

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

									snake_case_		     =							self.config_cls(
									    vocab_size=self.vocab_size			,							d_model=self.hidden_size			,							encoder_layers=self.num_hidden_layers			,							decoder_layers=self.num_hidden_layers			,							encoder_attention_heads=self.num_attention_heads			,							decoder_attention_heads=self.num_attention_heads			,							encoder_ffn_dim=self.intermediate_size			,							decoder_ffn_dim=self.intermediate_size			,							dropout=self.hidden_dropout_prob			,							attention_dropout=self.attention_probs_dropout_prob			,							max_position_embeddings=self.max_position_embeddings			,							eos_token_ids=[2]			,							bos_token_id=self.bos_token_id			,							pad_token_id=self.pad_token_id			,							decoder_start_token_id=self.pad_token_id			,							**self.config_updates			,							)
									snake_case_		     =							prepare_blenderbot_inputs_dict(lowercase_			,							lowercase_			,							lowercase_   )
									return config, inputs_dict


				def     A_					(						self       :   Tuple			,							lowercase_       :   Any			,							lowercase_       :   Optional[int]   ):
									snake_case_		     =							TFBlenderbotModel(config=lowercase_   ).get_decoder()
									snake_case_		     =							inputs_dict['''input_ids''']

									snake_case_		     =							input_ids[:1, :]
									snake_case_		     =							inputs_dict['''attention_mask'''][:1, :]
									snake_case_		     =							inputs_dict['''head_mask''']
									snake_case_		     =							1

									# first forward pass
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_			,							head_mask=lowercase_			,							use_cache=lowercase_   )

									snake_case_     ,snake_case_		     =							outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									snake_case_		     =							ids_tensor((self.batch_size, 3)			,							config.vocab_size   )
									snake_case_		     =							tf.cast(ids_tensor((self.batch_size, 3)			,							2   )			,							tf.inta   )

									# append to next input_ids and
									snake_case_		     =							tf.concat([input_ids, next_tokens]			,							axis=-1   )
									snake_case_		     =							tf.concat([attention_mask, next_attn_mask]			,							axis=-1   )

									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_   )[0]
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_			,							past_key_values=lowercase_   )[0]

									self.parent.assertEqual(next_tokens.shape[1]			,							output_from_past.shape[1]   )

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

									# test that outputs are equal for slice
									tf.debugging.assert_near(lowercase_			,							lowercase_			,							rtol=1e-3   )







def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=None, __UpperCAmelCase=None, __UpperCAmelCase=None, __UpperCAmelCase=None, __UpperCAmelCase=None, )						->   int:

					'''simple docstring'''

					if attention_mask is None:
										snake_case_		     =							tf.cast(tf.math.not_equal(__UpperCAmelCase, config.pad_token_id      ), tf.inta      )
					if decoder_attention_mask is None:
										snake_case_		     =							tf.concat(
										    [
										        tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.inta      ),
										        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id      ), tf.inta      ),
										    ], axis=-1, )
					if head_mask is None:
										snake_case_		     =							tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
					if decoder_head_mask is None:
										snake_case_		     =							tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
					if cross_attn_head_mask is None:
										snake_case_		     =							tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": decoder_attention_mask,
					    "head_mask": head_mask,
					    "decoder_head_mask": decoder_head_mask,
					    "cross_attn_head_mask": cross_attn_head_mask,
					}






@require_tf
class 		a							(  _lowerCamelCase  ,	_lowerCamelCase  ,	unittest.TestCase	):
				snake_case_           =						(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
				snake_case_           =						(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
				snake_case_           =						(
				    {
				        "conversational": TFBlenderbotForConditionalGeneration,
				        "feature-extraction": TFBlenderbotModel,
				        "summarization": TFBlenderbotForConditionalGeneration,
				        "text2text-generation": TFBlenderbotForConditionalGeneration,
				        "translation": TFBlenderbotForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				snake_case_           =						True
				snake_case_           =						False
				snake_case_           =						False


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							TFBlenderbotModelTester(self   )
									snake_case_		     =							ConfigTester(self			,							config_class=lowercase_   )


				def     A_					(						self       :   List[str]   ):
									self.config_tester.run_common_tests()


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*lowercase_   )







@require_tokenizers
@require_tf
class 		a							(  unittest.TestCase	):
				snake_case_           =						["My friends are cool but they eat too many carbs."]
				snake_case_           =						"facebook/blenderbot-400M-distill"


				@cached_property
				def     A_					(						self       :   Tuple   ):
									return BlenderbotTokenizer.from_pretrained(self.model_name   )


				@cached_property
				def     A_					(						self       :   List[str]   ):
									snake_case_		     =							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
									return model


				@slow
				def     A_					(						self       :   Any   ):
									snake_case_		     =							self.tokenizer(self.src_text			,							return_tensors='''tf'''   )
									snake_case_		     =							self.model.generate(
									    model_inputs.input_ids			,							)
									snake_case_		     =							self.tokenizer.batch_decode(generated_ids.numpy()			,							skip_special_tokens=lowercase_   )[0]
									assert (
									    generated_words
									    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
									)







'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a    :							List[Any]										=    logging.get_logger(__name__)

a    :							List[str]										=    {
    'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}






class 		a							(  _lowerCamelCase	):
				snake_case_           =						"wavlm"


				def __init__(						self       :   Union[str, Any]			,							lowercase_       :   Union[str, Any]=32			,							lowercase_       :   Optional[int]=768			,							lowercase_       :   List[str]=12			,							lowercase_       :   Dict=12			,							lowercase_       :   Any=3072			,							lowercase_       :   List[Any]="gelu"			,							lowercase_       :   int=0.1			,							lowercase_       :   Dict=0.1			,							lowercase_       :   int=0.1			,							lowercase_       :   int=0.0			,							lowercase_       :   List[Any]=0.1			,							lowercase_       :   int=0.1			,							lowercase_       :   Optional[int]=0.02			,							lowercase_       :   Dict=1e-5			,							lowercase_       :   Tuple="group"			,							lowercase_       :   str="gelu"			,							lowercase_       :   Any=(512, 512, 512, 512, 512, 512, 512)			,							lowercase_       :   str=(5, 2, 2, 2, 2, 2, 2)			,							lowercase_       :   Any=(10, 3, 3, 3, 3, 2, 2)			,							lowercase_       :   Tuple=False			,							lowercase_       :   List[Any]=128			,							lowercase_       :   int=16			,							lowercase_       :   Tuple=320			,							lowercase_       :   Union[str, Any]=800			,							lowercase_       :   List[str]=False			,							lowercase_       :   str=True			,							lowercase_       :   List[Any]=0.05			,							lowercase_       :   Optional[Any]=10			,							lowercase_       :   Optional[Any]=2			,							lowercase_       :   Optional[int]=0.0			,							lowercase_       :   List[str]=10			,							lowercase_       :   Union[str, Any]=320			,							lowercase_       :   Optional[Any]=2			,							lowercase_       :   List[str]=0.1			,							lowercase_       :   int=100			,							lowercase_       :   Tuple=256			,							lowercase_       :   Tuple=256			,							lowercase_       :   Dict=0.1			,							lowercase_       :   Any="mean"			,							lowercase_       :   Tuple=False			,							lowercase_       :   Dict=False			,							lowercase_       :   str=256			,							lowercase_       :   Optional[int]=(512, 512, 512, 512, 1500)			,							lowercase_       :   List[str]=(5, 3, 3, 1, 1)			,							lowercase_       :   Any=(1, 2, 3, 1, 1)			,							lowercase_       :   int=512			,							lowercase_       :   List[Any]=80			,							lowercase_       :   Optional[Any]=0			,							lowercase_       :   int=1			,							lowercase_       :   List[str]=2			,							lowercase_       :   List[str]=False			,							lowercase_       :   Dict=3			,							lowercase_       :   List[Any]=2			,							lowercase_       :   Optional[Any]=3			,							lowercase_       :   int=None			,							**lowercase_       :   List[str]			,							):
									super().__init__(**lowercase_			,							pad_token_id=lowercase_			,							bos_token_id=lowercase_			,							eos_token_id=lowercase_   )
									snake_case_		     =							hidden_size
									snake_case_		     =							feat_extract_norm
									snake_case_		     =							feat_extract_activation
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							conv_bias
									snake_case_		     =							num_buckets
									snake_case_		     =							max_bucket_distance
									snake_case_		     =							num_conv_pos_embeddings
									snake_case_		     =							num_conv_pos_embedding_groups
									snake_case_		     =							len(self.conv_dim   )
									snake_case_		     =							num_hidden_layers
									snake_case_		     =							intermediate_size
									snake_case_		     =							hidden_act
									snake_case_		     =							num_attention_heads
									snake_case_		     =							hidden_dropout
									snake_case_		     =							attention_dropout
									snake_case_		     =							activation_dropout
									snake_case_		     =							feat_proj_dropout
									snake_case_		     =							final_dropout
									snake_case_		     =							layerdrop
									snake_case_		     =							layer_norm_eps
									snake_case_		     =							initializer_range
									snake_case_		     =							num_ctc_classes
									snake_case_		     =							vocab_size
									snake_case_		     =							do_stable_layer_norm
									snake_case_		     =							use_weighted_layer_sum
									snake_case_		     =							classifier_proj_size

									if (
									    (len(self.conv_stride   ) != self.num_feat_extract_layers)
									    or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
									    or (len(self.conv_dim   ) != self.num_feat_extract_layers)
									):
														raise ValueError(
														    '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
														    ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
														    F" {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,"
														    F" `len(config.conv_kernel) = {len(self.conv_kernel   )}`."   )

									# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
									snake_case_		     =							apply_spec_augment
									snake_case_		     =							mask_time_prob
									snake_case_		     =							mask_time_length
									snake_case_		     =							mask_time_min_masks
									snake_case_		     =							mask_feature_prob
									snake_case_		     =							mask_feature_length

									# parameters for pretraining with codevector quantized representations
									snake_case_		     =							num_codevectors_per_group
									snake_case_		     =							num_codevector_groups
									snake_case_		     =							contrastive_logits_temperature
									snake_case_		     =							num_negatives
									snake_case_		     =							codevector_dim
									snake_case_		     =							proj_codevector_dim
									snake_case_		     =							diversity_loss_weight

									# ctc loss
									snake_case_		     =							ctc_loss_reduction
									snake_case_		     =							ctc_zero_infinity

									# adapter
									snake_case_		     =							add_adapter
									snake_case_		     =							adapter_kernel_size
									snake_case_		     =							adapter_stride
									snake_case_		     =							num_adapter_layers
									snake_case_		     =							output_hidden_size or hidden_size

									# SequenceClassification-specific parameter. Feel free to ignore for other classes.
									snake_case_		     =							classifier_proj_size

									# XVector-specific parameters. Feel free to ignore for other classes.
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							xvector_output_dim


				@property
				def     A_					(						self       :   Tuple   ):
									return functools.reduce(operator.mul			,							self.conv_stride			,							1   )

