

"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
	from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

	from .test_feature_extraction_speechta import floats_list


__A	:       Optional[int]        =						get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')





@require_torch
class      __UpperCamelCase					(  unittest.TestCase		):


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   tempfile.mkdtemp()

							A     =   SpeechTaTokenizer(__SCREAMING_SNAKE_CASE)
							tokenizer.save_pretrained(self.tmpdirname)

							A     =   {
							    "feature_size": 1,
							    "padding_value": 0.0,
							    "sampling_rate": 1_6_0_0_0,
							    "do_normalize": False,
							    "num_mel_bins": 8_0,
							    "hop_length": 1_6,
							    "win_length": 6_4,
							    "win_function": "hann_window",
							    "fmin": 8_0,
							    "fmax": 7_6_0_0,
							    "mel_floor": 1E-10,
							    "reduction_factor": 2,
							    "return_attention_mask": True,
							}

							A     =   os.path.join(self.tmpdirname	,	__SCREAMING_SNAKE_CASE)
							with open(self.feature_extraction_file	,	"w"	,	encoding="utf-8") as fp:
												fp.write(json.dumps(__SCREAMING_SNAKE_CASE) + "\n")


		def    SCREAMING_SNAKE_CASE__  (self					:						int	,	**__SCREAMING_SNAKE_CASE					:						int):
							return SpeechTaTokenizer.from_pretrained(self.tmpdirname	,	**__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]	,	**__SCREAMING_SNAKE_CASE					:						Optional[int]):
							return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname	,	**__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							shutil.rmtree(self.tmpdirname)


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   self.get_tokenizer()
							A     =   self.get_feature_extractor()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							processor.save_pretrained(self.tmpdirname)
							A     =   SpeechTaProcessor.from_pretrained(self.tmpdirname)

							self.assertEqual(processor.tokenizer.get_vocab()	,	tokenizer.get_vocab())
							self.assertIsInstance(processor.tokenizer	,	__SCREAMING_SNAKE_CASE)

							self.assertEqual(processor.feature_extractor.to_json_string()	,	feature_extractor.to_json_string())
							self.assertIsInstance(processor.feature_extractor	,	__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   SpeechTaProcessor(tokenizer=self.get_tokenizer()	,	feature_extractor=self.get_feature_extractor())
							processor.save_pretrained(self.tmpdirname)

							A     =   self.get_tokenizer(bos_token="(BOS)"	,	eos_token="(EOS)")
							A     =   self.get_feature_extractor(do_normalize=__SCREAMING_SNAKE_CASE	,	padding_value=1.0)

							A     =   SpeechTaProcessor.from_pretrained(
							    self.tmpdirname	,	bos_token="(BOS)"	,	eos_token="(EOS)"	,	do_normalize=__SCREAMING_SNAKE_CASE	,	padding_value=1.0)

							self.assertEqual(processor.tokenizer.get_vocab()	,	tokenizer_add_kwargs.get_vocab())
							self.assertIsInstance(processor.tokenizer	,	__SCREAMING_SNAKE_CASE)

							self.assertEqual(processor.feature_extractor.to_json_string()	,	feature_extractor_add_kwargs.to_json_string())
							self.assertIsInstance(processor.feature_extractor	,	__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   floats_list((3, 1_0_0_0))

							A     =   feature_extractor(audio=__SCREAMING_SNAKE_CASE	,	return_tensors="np")
							A     =   processor(audio=__SCREAMING_SNAKE_CASE	,	return_tensors="np")

							for key in input_feat_extract.keys():
												self.assertAlmostEqual(input_feat_extract[key].sum()	,	input_processor[key].sum()	,	delta=1E-2)


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   floats_list((3, 1_0_0_0))

							A     =   feature_extractor(audio_target=__SCREAMING_SNAKE_CASE	,	return_tensors="np")
							A     =   processor(audio_target=__SCREAMING_SNAKE_CASE	,	return_tensors="np")

							for key in input_feat_extract.keys():
												self.assertAlmostEqual(input_feat_extract[key].sum()	,	input_processor[key].sum()	,	delta=1E-2)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   "This is a test string"

							A     =   processor(text=__SCREAMING_SNAKE_CASE)
							A     =   tokenizer(__SCREAMING_SNAKE_CASE)

							for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key]	,	encoded_processor[key])


		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   "This is a test string"

							A     =   processor(text_target=__SCREAMING_SNAKE_CASE)
							A     =   tokenizer(__SCREAMING_SNAKE_CASE)

							for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key]	,	encoded_processor[key])


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							A     =   processor.batch_decode(__SCREAMING_SNAKE_CASE)
							A     =   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							self.assertListEqual(
							    processor.model_input_names	,	feature_extractor.model_input_names	,	msg="`processor` and `feature_extractor` model input names do not match"	,	)













'''simple docstring'''





from typing import TYPE_CHECKING

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


__A				:				Union[str, Any]					=		{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Tuple					=		["""GPT2TokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Optional[int]					=		[
     """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPT2DoubleHeadsModel""",
     """GPT2ForQuestionAnswering""",
     """GPT2ForSequenceClassification""",
     """GPT2ForTokenClassification""",
     """GPT2LMHeadModel""",
     """GPT2Model""",
     """GPT2PreTrainedModel""",
     """load_tf_weights_in_gpt2""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Optional[Any]					=		[
     """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFGPT2DoubleHeadsModel""",
     """TFGPT2ForSequenceClassification""",
     """TFGPT2LMHeadModel""",
     """TFGPT2MainLayer""",
     """TFGPT2Model""",
     """TFGPT2PreTrainedModel""",
 ]

try:
 if not is_keras_nlp_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Any					=		["""TFGPT2Tokenizer"""]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Tuple					=		["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
 from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
 from .tokenization_gpta import GPTaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_fast import GPTaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpta import (
      GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTaDoubleHeadsModel,
      GPTaForQuestionAnswering,
      GPTaForSequenceClassification,
      GPTaForTokenClassification,
      GPTaLMHeadModel,
      GPTaModel,
      GPTaPreTrainedModel,
      load_tf_weights_in_gpta,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gpta import (
      TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFGPTaDoubleHeadsModel,
      TFGPTaForSequenceClassification,
      TFGPTaLMHeadModel,
      TFGPTaMainLayer,
      TFGPTaModel,
      TFGPTaPreTrainedModel,
  )

 try:
  if not is_keras_nlp_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_tf import TFGPTaTokenizer

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
 import sys

 __A				:				Tuple					=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

