




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 		lowercase_       :




					def __init__(   self:		Any,     _lowercase:		List[Any],     _lowercase:		List[Any]=99,     _lowercase:		str=13,     _lowercase:		Union[str, Any]=16,     _lowercase:		List[str]=7,     _lowercase:		str=True,     _lowercase:		Dict=True,     _lowercase:		Optional[int]=True,     _lowercase:		Optional[Any]=False,     _lowercase:		int=True,     _lowercase:		Union[str, Any]=2,     _lowercase:		List[str]=32,     _lowercase:		Optional[Any]=4,     _lowercase:		List[str]=4,     _lowercase:		Optional[int]=30,     _lowercase:		Any=0,     _lowercase:		Optional[int]=1,     _lowercase:		List[str]=2,     _lowercase:		Union[str, Any]=None,     ):


									'''simple docstring'''


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

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

									__lowerCAmelCase					=  None
									__lowerCAmelCase					=  decoder_seq_length
									__lowerCAmelCase					=  2
									__lowerCAmelCase					=  1




					def     _lowercase						(   self:		int):


									'''simple docstring'''


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

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

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

									__lowerCAmelCase					=  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     _lowercase						(   self:		Dict,     _lowercase:		int,     _lowercase:		Union[str, Any],     _lowercase:		Tuple,     _lowercase:		Any,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  True
									__lowerCAmelCase					=  SpeechaTextaDecoder(config=_lowercase).to(_lowercase).eval()
									__lowerCAmelCase					=  input_ids[:2]

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

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

									__lowerCAmelCase					=  outputs["""past_key_values"""]

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

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

									print(_lowercase)

									__lowerCAmelCase					=  model(_lowercase)["""last_hidden_state"""]
									__lowerCAmelCase					=  model(_lowercase,     past_key_values=_lowercase)["""last_hidden_state"""]

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

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







					def     _lowercase						(   self:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  self.prepare_config_and_inputs()
									(
									    (
									    __lowerCAmelCase
									)	,				(
									    __lowerCAmelCase
									)	,				(
									    __lowerCAmelCase
									)	,				(
									    __lowerCAmelCase
									)	,				
									)					=  config_and_inputs

									__lowerCAmelCase					=  {
									    """input_ids""": input_ids,
									    """attention_mask""": attention_mask,
									}
									return config, inputs_dict





@require_torch
class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__						,							lowerCAmelCase__						,							unittest.TestCase				):
					__UpperCamelCase     	= (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
					__UpperCamelCase     	= (SpeechaTextaForCausalLM,) if is_torch_available() else ()
					__UpperCamelCase     	= {"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
					__UpperCamelCase     	= True
					__UpperCamelCase     	= False




					def     _lowercase						(   self:		Union[str, Any],     ):


									'''simple docstring'''


									__lowerCAmelCase					=  SpeechaTextaStandaloneDecoderModelTester(self,     is_training=_lowercase)
									__lowerCAmelCase					=  ConfigTester(self,     config_class=_lowercase)




					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									self.config_tester.run_common_tests()




					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past(*_lowercase)







					def     _lowercase						(   self:		str):


									'''simple docstring'''


									return













from typing import TYPE_CHECKING

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


snake_case_   :	Dict							      ={'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[Any]							      =['''T5Tokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Any							      =['''T5TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[int]							      =[
					    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''T5EncoderModel''',
					    '''T5ForConditionalGeneration''',
					    '''T5Model''',
					    '''T5PreTrainedModel''',
					    '''load_tf_weights_in_t5''',
					    '''T5ForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[Any]							      =[
					    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFT5EncoderModel''',
					    '''TFT5ForConditionalGeneration''',
					    '''TFT5Model''',
					    '''TFT5PreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Tuple							      =[
					    '''FlaxT5EncoderModel''',
					    '''FlaxT5ForConditionalGeneration''',
					    '''FlaxT5Model''',
					    '''FlaxT5PreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta import TaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta_fast import TaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ta import (
										    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TaEncoderModel,
										    TaForConditionalGeneration,
										    TaForQuestionAnswering,
										    TaModel,
										    TaPreTrainedModel,
										    load_tf_weights_in_ta,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ta import (
										    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFTaEncoderModel,
										    TFTaForConditionalGeneration,
										    TFTaModel,
										    TFTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_ta import (
										    FlaxTaEncoderModel,
										    FlaxTaForConditionalGeneration,
										    FlaxTaModel,
										    FlaxTaPreTrainedModel,
										)


else:
					import sys

					snake_case_   :	List[Any]							      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

