"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class 						__UpperCAmelCase    :







			'''simple docstring'''
			_UpperCamelCase  :						str					     =				MBartConfig
			_UpperCamelCase  :						List[Any]					     =				{}
			_UpperCamelCase  :						Dict					     =				'gelu'





			def __init__(			self   ,      snake_case_   ,      snake_case_=13   ,      snake_case_=7   ,      snake_case_=True   ,      snake_case_=False   ,      snake_case_=99   ,      snake_case_=32   ,      snake_case_=2   ,      snake_case_=4   ,      snake_case_=37   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=20   ,      snake_case_=2   ,      snake_case_=1   ,      snake_case_=0   ,      ):







										'''simple docstring'''


										A__       :     List[Any]								=			parent
										A__       :     Dict								=			batch_size
										A__       :     Tuple								=			seq_length
										A__       :     Any								=			is_training
										A__       :     Any								=			use_labels
										A__       :     Optional[int]								=			vocab_size
										A__       :     Any								=			hidden_size
										A__       :     Optional[int]								=			num_hidden_layers
										A__       :     Dict								=			num_attention_heads
										A__       :     Any								=			intermediate_size
										A__       :     Union[str, Any]								=			hidden_dropout_prob
										A__       :     str								=			attention_probs_dropout_prob
										A__       :     Union[str, Any]								=			max_position_embeddings
										A__       :     Tuple								=			eos_token_id
										A__       :     str								=			pad_token_id
										A__       :     List[Any]								=			bos_token_id





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     int								=			ids_tensor([self.batch_size, self.seq_length - 1]   ,      self.vocab_size							)
										A__       :     Union[str, Any]								=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)   ,      1							)
										A__       :     Any								=			tf.concat([input_ids, eos_tensor]   ,      axis=1							)

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

										A__       :     Union[str, Any]								=			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   ,      )
										A__       :     int								=			prepare_mbart_inputs_dict(snake_case_   ,      snake_case_   ,      snake_case_							)
										return config, inputs_dict





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Optional[int]								=			TFMBartModel(config=snake_case_							).get_decoder()
										A__       :     str								=			inputs_dict["""input_ids"""]

										A__       :     int								=			input_ids[:1, :]
										A__       :     Tuple								=			inputs_dict["""attention_mask"""][:1, :]
										A__       :     str								=			inputs_dict["""head_mask"""]
										A__       :     Dict								=			1

										# first forward pass
										A__       :     Any								=			model(snake_case_   ,      attention_mask=snake_case_   ,      head_mask=snake_case_   ,      use_cache=snake_case_							)

										A__				,							A__       :     Union[str, Any]								=			outputs.to_tuple()
										A__       :     Optional[Any]								=			past_key_values[1]





def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=None			,  lowerCAmelCase=None			,  lowerCAmelCase=None			,  lowerCAmelCase=None			,  lowerCAmelCase=None			,  ):
							if attention_mask is None:
														A__       :     Optional[int]								=			tf.cast(tf.math.not_equal(lowerCAmelCase			,  config.pad_token_id							)			,  tf.inta							)
							if decoder_attention_mask is None:
														A__       :     Dict								=			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:
														A__       :     str								=			tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
							if decoder_head_mask is None:
														A__       :     Union[str, Any]								=			tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
							if cross_attn_head_mask is None:
														A__       :     Optional[Any]								=			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 						__UpperCAmelCase    (__A							,   __A							,   unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
			_UpperCamelCase  :						Optional[int]					     =				(TFMBartForConditionalGeneration,) if is_tf_available() else ()
			_UpperCamelCase  :						str					     =				(
			    {
			        'conversational': TFMBartForConditionalGeneration,
			        'feature-extraction': TFMBartModel,
			        'summarization': TFMBartForConditionalGeneration,
			        'text2text-generation': TFMBartForConditionalGeneration,
			        'translation': TFMBartForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			_UpperCamelCase  :						List[str]					     =				True
			_UpperCamelCase  :						Optional[int]					     =				False
			_UpperCamelCase  :						List[str]					     =				False





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																	# Exception encountered when calling layer '...'
																	return True

										return False





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			TFMBartModelTester(self							)
										A__       :     Union[str, Any]								=			ConfigTester(self   ,      config_class=snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										self.config_tester.run_common_tests()





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*snake_case_							)





@require_sentencepiece
@require_tokenizers
@require_tf
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						str					     =				[
			    ' UN Chief Says There Is No Military Solution in Syria',
			]
			_UpperCamelCase  :						Any					     =				[
			    'Şeful ONU declară că nu există o soluţie militară în Siria',
			]
			_UpperCamelCase  :						Tuple					     =				'facebook/mbart-large-en-ro'





			@cached_property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return AutoTokenizer.from_pretrained(self.model_name							)





			@cached_property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Any								=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
										return model





			def 		lowerCamelCase					(			self   ,      **snake_case_							):







										'''simple docstring'''


										A__       :     Any								=			self.translate_src_text(**snake_case_							)
										self.assertListEqual(self.expected_text   ,      snake_case_							)





			def 		lowerCamelCase					(			self   ,      **snake_case_							):







										'''simple docstring'''


										A__       :     Tuple								=			self.tokenizer(self.src_text   ,      **snake_case_   ,      return_tensors="""tf"""							)
										A__       :     Union[str, Any]								=			self.model.generate(
										    model_inputs.input_ids   ,      attention_mask=model_inputs.attention_mask   ,      num_beams=2							)
										A__       :     Optional[Any]								=			self.tokenizer.batch_decode(snake_case_   ,      skip_special_tokens=snake_case_							)
										return generated_words





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										self._assert_generated_batch_equal_expected()






"""simple docstring"""



import functools
import operator

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


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'sew'





			def __init__(			self   ,      snake_case_=32   ,      snake_case_=768   ,      snake_case_=12   ,      snake_case_=12   ,      snake_case_=3_072   ,      snake_case_=2   ,      snake_case_="gelu"   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.0   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.02   ,      snake_case_=1E-5   ,      snake_case_="group"   ,      snake_case_="gelu"   ,      snake_case_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,      snake_case_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,      snake_case_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,      snake_case_=False   ,      snake_case_=128   ,      snake_case_=16   ,      snake_case_=True   ,      snake_case_=0.05   ,      snake_case_=10   ,      snake_case_=2   ,      snake_case_=0.0   ,      snake_case_=10   ,      snake_case_=0   ,      snake_case_="mean"   ,      snake_case_=False   ,      snake_case_=False   ,      snake_case_=256   ,      snake_case_=0   ,      snake_case_=1   ,      snake_case_=2   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_   ,      pad_token_id=snake_case_   ,      bos_token_id=snake_case_   ,      eos_token_id=snake_case_							)
										A__       :     Dict								=			hidden_size
										A__       :     Dict								=			feat_extract_norm
										A__       :     str								=			feat_extract_activation
										A__       :     Optional[Any]								=			list(snake_case_							)
										A__       :     str								=			list(snake_case_							)
										A__       :     Any								=			list(snake_case_							)
										A__       :     Any								=			conv_bias
										A__       :     Any								=			num_conv_pos_embeddings
										A__       :     Any								=			num_conv_pos_embedding_groups
										A__       :     str								=			len(self.conv_dim							)
										A__       :     Tuple								=			num_hidden_layers
										A__       :     int								=			intermediate_size
										A__       :     Union[str, Any]								=			squeeze_factor
										A__       :     Union[str, Any]								=			hidden_act
										A__       :     List[str]								=			num_attention_heads
										A__       :     List[str]								=			hidden_dropout
										A__       :     Dict								=			attention_dropout
										A__       :     Tuple								=			activation_dropout
										A__       :     Optional[int]								=			feat_proj_dropout
										A__       :     Optional[Any]								=			final_dropout
										A__       :     int								=			layerdrop
										A__       :     List[Any]								=			layer_norm_eps
										A__       :     int								=			initializer_range
										A__       :     Dict								=			vocab_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)`,"""
																	    F'''but is `len(config.conv_dim) = {len(self.conv_dim							)}`, `len(config.conv_stride)'''
																	    F'''= {len(self.conv_stride							)}`, `len(config.conv_kernel) = {len(self.conv_kernel							)}`.'''							)

										# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
										A__       :     Optional[Any]								=			apply_spec_augment
										A__       :     int								=			mask_time_prob
										A__       :     Tuple								=			mask_time_length
										A__       :     Optional[Any]								=			mask_time_min_masks
										A__       :     Any								=			mask_feature_prob
										A__       :     List[Any]								=			mask_feature_length
										A__       :     Any								=			mask_feature_min_masks

										# ctc loss
										A__       :     str								=			ctc_loss_reduction
										A__       :     List[Any]								=			ctc_zero_infinity

										# sequence classification
										A__       :     Union[str, Any]								=			use_weighted_layer_sum
										A__       :     str								=			classifier_proj_size





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return functools.reduce(operator.mul   ,      self.conv_stride   ,      1							)





