
'''simple docstring'''

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 					a_					:




       def __init__(       self				,				snake_case_				,				snake_case_=9_9				,				snake_case_=1_3				,				snake_case_=1_6				,				snake_case_=7				,				snake_case_=True				,				snake_case_=True				,				snake_case_=True				,				snake_case_=False				,				snake_case_=True				,				snake_case_=2				,				snake_case_=3_2				,				snake_case_=4				,				snake_case_=4				,				snake_case_=3_0				,				snake_case_=0				,				snake_case_=1				,				snake_case_=2				,				snake_case_=None				,				):
         _lowerCAmelCase    :					List[str]							=   parent
         _lowerCAmelCase    :					Union[str, Any]							=   batch_size
         _lowerCAmelCase    :					str							=   decoder_seq_length
         # For common tests
         _lowerCAmelCase    :					Any							=   self.decoder_seq_length
         _lowerCAmelCase    :					Union[str, Any]							=   is_training
         _lowerCAmelCase    :					str							=   use_attention_mask
         _lowerCAmelCase    :					str							=   use_labels

         _lowerCAmelCase    :					int							=   vocab_size
         _lowerCAmelCase    :					Union[str, Any]							=   d_model
         _lowerCAmelCase    :					Optional[int]							=   d_model
         _lowerCAmelCase    :					Optional[Any]							=   decoder_layers
         _lowerCAmelCase    :					Optional[int]							=   decoder_layers
         _lowerCAmelCase    :					Optional[int]							=   decoder_ffn_dim
         _lowerCAmelCase    :					Dict							=   decoder_attention_heads
         _lowerCAmelCase    :					List[str]							=   decoder_attention_heads
         _lowerCAmelCase    :					Union[str, Any]							=   eos_token_id
         _lowerCAmelCase    :					str							=   bos_token_id
         _lowerCAmelCase    :					Tuple							=   pad_token_id
         _lowerCAmelCase    :					Optional[Any]							=   decoder_start_token_id
         _lowerCAmelCase    :					Dict							=   use_cache
         _lowerCAmelCase    :					Optional[int]							=   max_position_embeddings

         _lowerCAmelCase    :					Union[str, Any]							=   None
         _lowerCAmelCase    :					List[Any]							=   decoder_seq_length
         _lowerCAmelCase    :					Optional[int]							=   2
         _lowerCAmelCase    :					Optional[Any]							=   1




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					int							=   ids_tensor([self.batch_size, self.decoder_seq_length]				,				self.vocab_size					)

         _lowerCAmelCase    :					Optional[Any]							=   None
         if self.use_attention_mask:
           _lowerCAmelCase    :					int							=   ids_tensor([self.batch_size, self.decoder_seq_length]				,				vocab_size=2					)

         _lowerCAmelCase    :					str							=   None
         if self.use_labels:
           _lowerCAmelCase    :					List[Any]							=   ids_tensor([self.batch_size, self.decoder_seq_length]				,				self.vocab_size					)

         _lowerCAmelCase    :					str							=   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 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				):
         _lowerCAmelCase    :					Optional[Any]							=   True
         _lowerCAmelCase    :					List[str]							=   SpeechaTextaDecoder(config=snake_case_					).to(snake_case_					).eval()
         _lowerCAmelCase    :					str							=   input_ids[:2]

         input_ids[input_ids == 0] += 1
         # first forward pass
         _lowerCAmelCase    :					Any							=   model(snake_case_				,				use_cache=snake_case_					)
         _lowerCAmelCase    :					int							=   model(snake_case_					)
         _lowerCAmelCase    :					str							=   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					)

         _lowerCAmelCase    :					Union[str, Any]							=   outputs["past_key_values"]

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

         # append to next input_ids and
         _lowerCAmelCase    :					List[str]							=   torch.cat([input_ids, next_tokens]				,				dim=-1					)

         print(snake_case_					)

         _lowerCAmelCase    :					Optional[Any]							=   model(snake_case_					)["last_hidden_state"]
         _lowerCAmelCase    :					int							=   model(snake_case_				,				past_key_values=snake_case_					)["last_hidden_state"]

         # select random slice
         _lowerCAmelCase    :					Tuple							=   ids_tensor((1,)				,				output_from_past.shape[-1]					).item()
         _lowerCAmelCase    :					Optional[int]							=   output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
         _lowerCAmelCase    :					Optional[Any]							=   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 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Tuple							=   self.prepare_config_and_inputs()
         (
             _lowerCAmelCase						
         )    :					List[Any]							=   config_and_inputs

         _lowerCAmelCase    :					Union[str, Any]							=   {
             "input_ids": input_ids,
             "attention_mask": attention_mask,
         }
         return config, inputs_dict



@require_torch
class 					a_					(_a							,     _a							,     _a							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Dict      = (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
       __lowerCAmelCase      :			Optional[int]      = (SpeechaTextaForCausalLM,) if is_torch_available() else ()
       __lowerCAmelCase      :			Union[str, Any]      = {'text-generation': SpeechaTextaForCausalLM} if is_torch_available() else {}
       __lowerCAmelCase      :			int      = True
       __lowerCAmelCase      :			Optional[Any]      = False




       def 							__UpperCamelCase    (       self				,				):
         _lowerCAmelCase    :					Union[str, Any]							=   SpeechaTextaStandaloneDecoderModelTester(self				,				is_training=snake_case_					)
         _lowerCAmelCase    :					Optional[int]							=   ConfigTester(self				,				config_class=snake_case_					)




       def 							__UpperCamelCase    (       self					):
         pass




       def 							__UpperCamelCase    (       self					):
         pass




       def 							__UpperCamelCase    (       self					):
         pass




       def 							__UpperCamelCase    (       self					):
         self.config_tester.run_common_tests()




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					int							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past(*snake_case_					)







       def 							__UpperCamelCase    (       self					):
         return




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCamelCase_            =    {"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GPTJForCausalLM""",
         """GPTJForQuestionAnswering""",
         """GPTJForSequenceClassification""",
         """GPTJModel""",
         """GPTJPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """TFGPTJForCausalLM""",
         """TFGPTJForQuestionAnswering""",
         """TFGPTJForSequenceClassification""",
         """TFGPTJModel""",
         """TFGPTJPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """FlaxGPTJForCausalLM""",
         """FlaxGPTJModel""",
         """FlaxGPTJPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     UpperCamelCase_            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


