


import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

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


if is_torch_available():
							import torch

							from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
							    SpeechaTextaDecoder,
							    SpeechaTextaForCausalLM,
							)







@require_torch
class     __lowerCamelCase     :


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase=99						,       UpperCAmelCase=13						,       UpperCAmelCase=16						,       UpperCAmelCase=7						,       UpperCAmelCase=True						,       UpperCAmelCase=True						,       UpperCAmelCase=True						,       UpperCAmelCase=False						,       UpperCAmelCase=True						,       UpperCAmelCase=2						,       UpperCAmelCase=32						,       UpperCAmelCase=4						,       UpperCAmelCase=4						,       UpperCAmelCase=30						,       UpperCAmelCase=0						,       UpperCAmelCase=1						,       UpperCAmelCase=2						,       UpperCAmelCase=None						,       )     ->       Union[str, Any]:






								'''simple docstring'''






								lowercase_			 = parent
								lowercase_			 = batch_size
								lowercase_			 = decoder_seq_length
								# For common tests
								lowercase_			 = self.decoder_seq_length
								lowercase_			 = is_training
								lowercase_			 = use_attention_mask
								lowercase_			 = use_labels

								lowercase_			 = vocab_size
								lowercase_			 = d_model
								lowercase_			 = d_model
								lowercase_			 = decoder_layers
								lowercase_			 = decoder_layers
								lowercase_			 = decoder_ffn_dim
								lowercase_			 = decoder_attention_heads
								lowercase_			 = decoder_attention_heads
								lowercase_			 = eos_token_id
								lowercase_			 = bos_token_id
								lowercase_			 = pad_token_id
								lowercase_			 = decoder_start_token_id
								lowercase_			 = use_cache
								lowercase_			 = max_position_embeddings

								lowercase_			 = None
								lowercase_			 = decoder_seq_length
								lowercase_			 = 2
								lowercase_			 = 1




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






								'''simple docstring'''






								lowercase_			 = ids_tensor([self.batch_size, self.decoder_seq_length]						,       self.vocab_size  )

								lowercase_			 = None
								if self.use_attention_mask:
									lowercase_			 = ids_tensor([self.batch_size, self.decoder_seq_length]						,       vocab_size=2  )

								lowercase_			 = None
								if self.use_labels:
									lowercase_			 = ids_tensor([self.batch_size, self.decoder_seq_length]						,       self.vocab_size  )

								lowercase_			 = SpeechaTextaConfig(
								    vocab_size=self.vocab_size						,       d_model=self.d_model						,       decoder_layers=self.decoder_layers						,       decoder_ffn_dim=self.decoder_ffn_dim						,       decoder_attention_heads=self.decoder_attention_heads						,       eos_token_id=self.eos_token_id						,       bos_token_id=self.bos_token_id						,       use_cache=self.use_cache						,       pad_token_id=self.pad_token_id						,       decoder_start_token_id=self.decoder_start_token_id						,       max_position_embeddings=self.max_position_embeddings						,       )

								return (
								    config,
								    input_ids,
								    attention_mask,
								    lm_labels,
								)




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase						,       )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = True
								lowercase_			 = SpeechaTextaDecoder(config=snake_case_  ).to(snake_case_  ).eval()
								lowercase_			 = input_ids[:2]

								input_ids[input_ids == 0] += 1
								# first forward pass
								lowercase_			 = model(snake_case_						,       use_cache=snake_case_  )
								lowercase_			 = model(snake_case_  )
								lowercase_			 = model(snake_case_						,       use_cache=snake_case_  )

								self.parent.assertTrue(len(snake_case_  ) == len(snake_case_  )  )
								self.parent.assertTrue(len(snake_case_  ) == len(snake_case_  ) + 1  )

								lowercase_			 = outputs['past_key_values']

								# create hypothetical next token and extent to next_input_ids
								lowercase_			 = ids_tensor((2, 1)						,       config.vocab_size - 1  ) + 1

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

								print(snake_case_  )

								lowercase_			 = model(snake_case_  )['last_hidden_state']
								lowercase_			 = model(snake_case_						,       past_key_values=snake_case_  )['last_hidden_state']

								# select random slice
								lowercase_			 = ids_tensor((1,)						,       output_from_past.shape[-1]  ).item()
								lowercase_			 = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
								lowercase_			 = output_from_past[:, 0, random_slice_idx].detach()

								# test that outputs are equal for slice
								assert torch.allclose(snake_case_						,       snake_case_						,       atol=1e-3  )




							def 				A__      (  self  )     ->       Dict:






								'''simple docstring'''






								lowercase_			 = self.prepare_config_and_inputs()
								(
								    lowercase_			
								)			 = config_and_inputs

								lowercase_			 = {
								    'input_ids': input_ids,
								    'attention_mask': attention_mask,
								}
								return config, inputs_dict







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


							"""simple docstring"""

							lowerCAmelCase__          =				(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
							lowerCAmelCase__          =				(SpeechaTextaForCausalLM,) if is_torch_available() else ()
							lowerCAmelCase__          =				{"""text-generation""": SpeechaTextaForCausalLM} if is_torch_available() else {}
							lowerCAmelCase__          =				True
							lowerCAmelCase__          =				False




							def 				A__      (  self						,       )     ->       Dict:






								'''simple docstring'''






								lowercase_			 = SpeechaTextaStandaloneDecoderModelTester(self						,       is_training=snake_case_  )
								lowercase_			 = ConfigTester(self						,       config_class=snake_case_  )




							def 				A__      (  self  )     ->       Dict:






								'''simple docstring'''






								pass




							def 				A__      (  self  )     ->       Optional[int]:






								'''simple docstring'''






								pass




							def 				A__      (  self  )     ->       str:






								'''simple docstring'''






								pass




							def 				A__      (  self  )     ->       Optional[Any]:






								'''simple docstring'''






								self.config_tester.run_common_tests()




							def 				A__      (  self  )     ->       int:






								'''simple docstring'''






								lowercase_			 = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past(*snake_case_  )




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






								'''simple docstring'''






								return








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_	:		Union[str, Any]         =   {
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Any         =   ['CamembertTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		int         =   ['CamembertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Optional[Any]         =   [
       'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'CamembertForCausalLM',
       'CamembertForMaskedLM',
       'CamembertForMultipleChoice',
       'CamembertForQuestionAnswering',
       'CamembertForSequenceClassification',
       'CamembertForTokenClassification',
       'CamembertModel',
       'CamembertPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		int         =   [
       'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFCamembertForCausalLM',
       'TFCamembertForMaskedLM',
       'TFCamembertForMultipleChoice',
       'TFCamembertForQuestionAnswering',
       'TFCamembertForSequenceClassification',
       'TFCamembertForTokenClassification',
       'TFCamembertModel',
       'TFCamembertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert import CamembertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert_fast import CamembertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_camembert import (
          CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CamembertForCausalLM,
          CamembertForMaskedLM,
          CamembertForMultipleChoice,
          CamembertForQuestionAnswering,
          CamembertForSequenceClassification,
          CamembertForTokenClassification,
          CamembertModel,
          CamembertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_camembert import (
          TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCamembertForCausalLM,
          TFCamembertForMaskedLM,
          TFCamembertForMultipleChoice,
          TFCamembertForQuestionAnswering,
          TFCamembertForSequenceClassification,
          TFCamembertForTokenClassification,
          TFCamembertModel,
          TFCamembertPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_	:		int         =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)