




"""simple docstring"""


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


_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}







class 				lowerCAmelCase_				(      __UpperCAmelCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Tuple      =    "speech_to_text"
						_lowerCamelCase:    List[str]      =    ["past_key_values"]
						_lowerCamelCase:    Tuple      =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


						def __init__( self     :      List[str]  ,A_     :      Optional[Any]=1_0000  ,A_     :      List[Any]=12  ,A_     :      List[str]=2048  ,A_     :      int=4  ,A_     :      str=6  ,A_     :      Union[str, Any]=2048  ,A_     :      Optional[Any]=4  ,A_     :      Dict=0.0  ,A_     :      Tuple=0.0  ,A_     :      str=True  ,A_     :      Union[str, Any]=True  ,A_     :      List[Any]="relu"  ,A_     :      Any=256  ,A_     :      int=0.1  ,A_     :      Any=0.0  ,A_     :      Optional[Any]=0.0  ,A_     :      int=0.02  ,A_     :      Optional[int]=2  ,A_     :      Union[str, Any]=True  ,A_     :      Optional[Any]=1  ,A_     :      Optional[int]=0  ,A_     :      int=2  ,A_     :      Tuple=6000  ,A_     :      Optional[int]=1024  ,A_     :      List[str]=2  ,A_     :      List[Any]=(5, 5)  ,A_     :      List[Any]=1024  ,A_     :      Union[str, Any]=80  ,A_     :      Tuple=1  ,**A_     :      Union[str, Any]  ,)     ->   Optional[int]:
							A						   =   vocab_size
							A						   =   d_model
							A						   =   encoder_ffn_dim
							A						   =   encoder_layers
							A						   =   encoder_attention_heads
							A						   =   decoder_ffn_dim
							A						   =   decoder_layers
							A						   =   decoder_attention_heads
							A						   =   dropout
							A						   =   attention_dropout
							A						   =   activation_dropout
							A						   =   activation_function
							A						   =   init_std
							A						   =   encoder_layerdrop
							A						   =   decoder_layerdrop
							A						   =   use_cache
							A						   =   encoder_layers
							A						   =   scale_embedding  # scale factor will be sqrt(d_model) if True
							A						   =   max_source_positions
							A						   =   max_target_positions
							A						   =   num_conv_layers
							A						   =   list(_lowerCamelCase      )
							A						   =   conv_channels
							A						   =   input_feat_per_channel
							A						   =   input_channels

							if len(self.conv_kernel_sizes      ) != self.num_conv_layers:
								raise ValueError(
								    'Configuration for convolutional module is incorrect. '
								    'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
								    F'but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes      )}`, '
								    F'`config.num_conv_layers = {self.num_conv_layers}`.'      )

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

"""simple docstring"""


import unittest

from transformers import XLMConfig, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMWithLMHeadModel,
		)
		from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      Any  ,A_     :      Optional[Any]  ,A_     :      Tuple=13  ,A_     :      Optional[Any]=7  ,A_     :      Dict=True  ,A_     :      Optional[Any]=True  ,A_     :      str=True  ,A_     :      Union[str, Any]=True  ,A_     :      Optional[Any]=True  ,A_     :      Tuple=False  ,A_     :      Optional[int]=False  ,A_     :      str=False  ,A_     :      int=2  ,A_     :      Union[str, Any]=99  ,A_     :      int=0  ,A_     :      Dict=32  ,A_     :      List[str]=5  ,A_     :      Any=4  ,A_     :      str=0.1  ,A_     :      Any=0.1  ,A_     :      int=512  ,A_     :      List[Any]=2  ,A_     :      Union[str, Any]=0.02  ,A_     :      Optional[Any]=2  ,A_     :      List[str]=4  ,A_     :      Optional[int]="last"  ,A_     :      str=True  ,A_     :      List[str]=None  ,A_     :      List[Any]=0  ,)     ->   int:
							A						   =   parent
							A						   =   batch_size
							A						   =   seq_length
							A						   =   is_training
							A						   =   use_input_lengths
							A						   =   use_token_type_ids
							A						   =   use_labels
							A						   =   gelu_activation
							A						   =   sinusoidal_embeddings
							A						   =   causal
							A						   =   asm
							A						   =   n_langs
							A						   =   vocab_size
							A						   =   n_special
							A						   =   hidden_size
							A						   =   num_hidden_layers
							A						   =   num_attention_heads
							A						   =   hidden_dropout_prob
							A						   =   attention_probs_dropout_prob
							A						   =   max_position_embeddings
							A						   =   type_sequence_label_size
							A						   =   initializer_range
							A						   =   num_labels
							A						   =   num_choices
							A						   =   summary_type
							A						   =   use_proj
							A						   =   scope
							A						   =   bos_token_id


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Dict:
							A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size      )
							A						   =   random_attention_mask([self.batch_size, self.seq_length]      )

							A						   =   None
							if self.use_input_lengths:
								A						   =   (
								    ids_tensor([self.batch_size]  ,vocab_size=2      ) + self.seq_length - 2
								)  # small variation of seq_length

							A						   =   None
							if self.use_token_type_ids:
								A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.n_langs      )

							A						   =   None
							A						   =   None
							A						   =   None
							if self.use_labels:
								A						   =   ids_tensor([self.batch_size]  ,self.type_sequence_label_size      )
								A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.num_labels      )
								A						   =   ids_tensor([self.batch_size]  ,2      ).float()
								A						   =   ids_tensor([self.batch_size]  ,self.num_choices      )

							A						   =   self.get_config()

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_lengths,
							    sequence_labels,
							    token_labels,
							    is_impossible_labels,
							    choice_labels,
							    input_mask,
							)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Any:
							return XLMConfig(
							    vocab_size=self.vocab_size  ,n_special=self.n_special  ,emb_dim=self.hidden_size  ,n_layers=self.num_hidden_layers  ,n_heads=self.num_attention_heads  ,dropout=self.hidden_dropout_prob  ,attention_dropout=self.attention_probs_dropout_prob  ,gelu_activation=self.gelu_activation  ,sinusoidal_embeddings=self.sinusoidal_embeddings  ,asm=self.asm  ,causal=self.causal  ,n_langs=self.n_langs  ,max_position_embeddings=self.max_position_embeddings  ,initializer_range=self.initializer_range  ,summary_type=self.summary_type  ,use_proj=self.use_proj  ,num_labels=self.num_labels  ,bos_token_id=self.bos_token_id  ,)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      List[str]  ,A_     :      int  ,A_     :      Dict  ,A_     :      Optional[Any]  ,A_     :      Optional[Any]  ,A_     :      Any  ,A_     :      List[str]  ,A_     :      Optional[int]  ,)     ->   Tuple:
							A						   =   XLMModel(config=A_      )
							model.to(A_      )
							model.eval()
							A						   =   model(A_  ,lengths=A_  ,langs=A_      )
							A						   =   model(A_  ,langs=A_      )
							A						   =   model(A_      )
							self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,A_     :      str  ,A_     :      Union[str, Any]  ,A_     :      Any  ,A_     :      Any  ,A_     :      Any  ,A_     :      Union[str, Any]  ,A_     :      List[str]  ,A_     :      List[str]  ,A_     :      List[str]  ,)     ->   Union[str, Any]:
							A						   =   XLMWithLMHeadModel(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_  ,token_type_ids=A_  ,labels=A_      )
							self.parent.assertEqual(result.loss.shape  ,()      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.vocab_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Tuple  ,A_     :      Tuple  ,A_     :      str  ,A_     :      int  ,A_     :      str  ,A_     :      Optional[Any]  ,A_     :      Any  ,A_     :      Any  ,A_     :      Dict  ,)     ->   List[str]:
							A						   =   XLMForQuestionAnsweringSimple(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_      )

							A						   =   model(A_  ,start_positions=A_  ,end_positions=A_      )
							A						   =   outputs
							self.parent.assertEqual(result.start_logits.shape  ,(self.batch_size, self.seq_length)      )
							self.parent.assertEqual(result.end_logits.shape  ,(self.batch_size, self.seq_length)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Optional[int]  ,A_     :      Dict  ,A_     :      Optional[Any]  ,A_     :      List[Any]  ,A_     :      List[str]  ,A_     :      List[Any]  ,A_     :      Optional[Any]  ,A_     :      str  ,A_     :      Any  ,)     ->   Optional[int]:
							A						   =   XLMForQuestionAnswering(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_      )

							A						   =   model(
							    A_  ,start_positions=A_  ,end_positions=A_  ,cls_index=A_  ,is_impossible=A_  ,p_mask=A_  ,)

							A						   =   model(
							    A_  ,start_positions=A_  ,end_positions=A_  ,cls_index=A_  ,is_impossible=A_  ,)

							((A)   ,      )						   =   result_with_labels.to_tuple()

							A						   =   model(A_  ,start_positions=A_  ,end_positions=A_      )

							((A)   ,      )						   =   result_with_labels.to_tuple()

							self.parent.assertEqual(result_with_labels.loss.shape  ,()      )
							self.parent.assertEqual(result.start_top_log_probs.shape  ,(self.batch_size, model.config.start_n_top)      )
							self.parent.assertEqual(result.start_top_index.shape  ,(self.batch_size, model.config.start_n_top)      )
							self.parent.assertEqual(
							    result.end_top_log_probs.shape  ,(self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
							self.parent.assertEqual(
							    result.end_top_index.shape  ,(self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
							self.parent.assertEqual(result.cls_logits.shape  ,(self.batch_size,)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,A_     :      Union[str, Any]  ,A_     :      Tuple  ,A_     :      int  ,A_     :      Union[str, Any]  ,A_     :      List[Any]  ,A_     :      Optional[Any]  ,A_     :      Tuple  ,A_     :      Union[str, Any]  ,A_     :      str  ,)     ->   List[Any]:
							A						   =   XLMForSequenceClassification(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_      )
							A						   =   model(A_  ,labels=A_      )
							self.parent.assertEqual(result.loss.shape  ,()      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.type_sequence_label_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      str  ,A_     :      Tuple  ,A_     :      List[str]  ,A_     :      Dict  ,A_     :      Dict  ,A_     :      Union[str, Any]  ,A_     :      Dict  ,A_     :      Any  ,)     ->   Any:
							A						   =   self.num_labels
							A						   =   XLMForTokenClassification(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_  ,attention_mask=A_  ,labels=A_      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.num_labels)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      int  ,A_     :      str  ,A_     :      int  ,A_     :      Tuple  ,A_     :      List[Any]  ,A_     :      List[str]  ,A_     :      List[str]  ,A_     :      Optional[Any]  ,A_     :      int  ,)     ->   Tuple:
							A						   =   self.num_choices
							A						   =   XLMForMultipleChoice(config=A_      )
							model.to(A_      )
							model.eval()
							A						   =   input_ids.unsqueeze(1      ).expand(-1  ,self.num_choices  ,-1      ).contiguous()
							A						   =   token_type_ids.unsqueeze(1      ).expand(-1  ,self.num_choices  ,-1      ).contiguous()
							A						   =   input_mask.unsqueeze(1      ).expand(-1  ,self.num_choices  ,-1      ).contiguous()
							A						   =   model(
							    A_  ,attention_mask=A_  ,token_type_ids=A_  ,labels=A_  ,)
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_choices)      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   List[Any]:
							A						   =   self.prepare_config_and_inputs()
							(
							    (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      
							)						   =   config_and_inputs
							A						   =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
							return config, inputs_dict







@require_torch
class 				lowerCAmelCase_				(      _lowercase		,					_lowercase		,					_lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Dict      =    (
						    (
						        XLMModel,
						        XLMWithLMHeadModel,
						        XLMForQuestionAnswering,
						        XLMForSequenceClassification,
						        XLMForQuestionAnsweringSimple,
						        XLMForTokenClassification,
						        XLMForMultipleChoice,
						    )
						    if is_torch_available()
						    else ()
						)
						_lowerCamelCase:    Dict      =    (
						    (XLMWithLMHeadModel,) if is_torch_available() else ()
						)  # TODO (PVP): Check other models whether language generation is also applicable
						_lowerCamelCase:    Optional[Any]      =    (
						    {
						        '''feature-extraction''': XLMModel,
						        '''fill-mask''': XLMWithLMHeadModel,
						        '''question-answering''': XLMForQuestionAnsweringSimple,
						        '''text-classification''': XLMForSequenceClassification,
						        '''text-generation''': XLMWithLMHeadModel,
						        '''token-classification''': XLMForTokenClassification,
						        '''zero-shot''': XLMForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      Tuple  ,A_     :      Any  ,A_     :      str      )     ->   Tuple:
							if (
							    pipeline_test_casse_name == "QAPipelineTests"
							    and tokenizer_name is not None
							    and not tokenizer_name.endswith('Fast'      )
							):
								# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
								# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
								# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
								return True

							return False


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      Tuple  ,A_     :      str=False      )     ->   Dict:
							A						   =   super()._prepare_for_class(A_  ,A_  ,return_labels=A_      )

							if return_labels:
								if model_class.__name__ == "XLMForQuestionAnswering":
									A						   =   torch.zeros(
									    self.model_tester.batch_size  ,dtype=torch.long  ,device=A_      )
									A						   =   torch.zeros(
									    self.model_tester.batch_size  ,dtype=torch.long  ,device=A_      )

							return inputs_dict


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   List[str]:
							A						   =   XLMModelTester(self      )
							A						   =   ConfigTester(self  ,config_class=A_  ,emb_dim=37      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   str:
							self.config_tester.run_common_tests()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[int]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_model(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_lm_head(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_simple_qa(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Tuple:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_qa(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   List[Any]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_sequence_classif(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Tuple:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_token_classif(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_for_multiple_choice(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Dict  ,A_     :      List[Any]  ,A_     :      List[Any]  ,A_     :      Dict  ,A_     :      Union[str, Any]  ,A_     :      List[Any]=False  ,A_     :      Tuple=1      )     ->   List[Any]:
							self.assertIsInstance(A_  ,A_      )
							self.assertListEqual(
							    [isinstance(A_  ,A_      ) for iter_attentions in attentions]  ,[True] * len(A_      )      )
							self.assertEqual(len(A_      )  ,(max_length - min_length) * num_beam_groups      )

							for idx, iter_attentions in enumerate(A_      ):
								# adds PAD dummy token
								A						   =   min_length + idx + 1
								A						   =   min_length + idx + 1

								A						   =   (
								    batch_size * num_beam_groups,
								    config.num_attention_heads,
								    tgt_len,
								    src_len,
								)
								# check attn size
								self.assertListEqual(
								    [layer_attention.shape for layer_attention in iter_attentions]  ,[expected_shape] * len(A_      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[Any]  ,A_     :      List[str]  ,A_     :      Dict  ,A_     :      Optional[Any]  ,A_     :      Dict  ,A_     :      Tuple=False  ,A_     :      Optional[Any]=1      )     ->   List[str]:
							self.assertIsInstance(A_  ,A_      )
							self.assertListEqual(
							    [isinstance(A_  ,A_      ) for iter_hidden_states in hidden_states]  ,[True] * len(A_      )  ,)
							self.assertEqual(len(A_      )  ,(max_length - min_length) * num_beam_groups      )

							for idx, iter_hidden_states in enumerate(A_      ):
								# adds PAD dummy token
								A						   =   min_length + idx + 1
								A						   =   (batch_size * num_beam_groups, seq_len, config.hidden_size)
								# check hidden size
								self.assertListEqual(
								    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]  ,[expected_shape] * len(A_      )  ,)
							pass




						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[str]:
							for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A						   =   XLMModel.from_pretrained(A_      )
								self.assertIsNotNone(A_      )







@require_torch
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Dict:
							A						   =   XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'      )
							model.to(A_      )
							A						   =   torch.tensor([[14, 447]]  ,dtype=torch.long  ,device=A_      )  # the president
							A						   =   [
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							]  # the president the president the president the president the president the president the president the president the president the president
							# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
							A						   =   model.generate(A_  ,do_sample=A_      )
							self.assertListEqual(output_ids[0].cpu().numpy().tolist()  ,A_      )