





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_keras_nlp_available():
	from transformers.models.gpta import TFGPTaTokenizer


_lowerCamelCase 					=      ['gpt2']
_lowerCamelCase 					=      'gpt2'

if is_tf_available():




	class 	UpperCamelCase_							(  tf.Module     ):


								def __init__(				self     :Optional[Any]				,					__A     :Union[str, Any]		) -> List[Any]:



										"""simple docstring"""



										super().__init__()
										SCREAMING_SNAKE_CASE__								=   tokenizer
										SCREAMING_SNAKE_CASE__								=   AutoConfig.from_pretrained(__A		)
										SCREAMING_SNAKE_CASE__								=   TFGPTaLMHeadModel.from_config(__A		)


								@tf.function(input_signature=(tf.TensorSpec((None,)				,					tf.string				,					name="""text"""		),)		)
								def   _snake_case	(				self     :Optional[int]				,					__A     :int		) -> Optional[Any]:



										"""simple docstring"""



										SCREAMING_SNAKE_CASE__								=   self.tokenizer(__A		)
										SCREAMING_SNAKE_CASE__								=   tokenized["""input_ids"""].to_tensor()

										SCREAMING_SNAKE_CASE__								=   tf.cast(input_ids_dense > 0				,					tf.intaa		)
										# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

										SCREAMING_SNAKE_CASE__								=   self.model(input_ids=__A				,					attention_mask=__A		)["""logits"""]

										return outputs




@require_tf
@require_keras_nlp
class 	UpperCamelCase_							(  unittest.TestCase     ):


							def   _snake_case	(				self     :List[Any]		) -> Dict:



									"""simple docstring"""



									super().setUp()

									SCREAMING_SNAKE_CASE__								=   [GPTaTokenizer.from_pretrained(__A		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
									SCREAMING_SNAKE_CASE__								=   [TFGPTaTokenizer.from_pretrained(__A		) for checkpoint in TOKENIZER_CHECKPOINTS]
									assert len(self.tokenizers		) == len(self.tf_tokenizers		)

									SCREAMING_SNAKE_CASE__								=   [
									    """This is a straightforward English test sentence.""",
									    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
									    """Now we're going to add some Chinese: 一 二 三 一二三""",
									    """And some much more rare Chinese: 齉 堃 齉堃""",
									    """Je vais aussi écrire en français pour tester les accents""",
									    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
									]
									SCREAMING_SNAKE_CASE__								=   list(zip(self.test_sentences				,					self.test_sentences[::-1]		)		)


							def   _snake_case	(				self     :Any		) -> Any:



									"""simple docstring"""



									for tokenizer, tf_tokenizer in zip(self.tokenizers				,					self.tf_tokenizers		):
											for test_inputs in self.test_sentences:
													SCREAMING_SNAKE_CASE__								=   tokenizer([test_inputs]				,					return_tensors="""tf"""		)
													SCREAMING_SNAKE_CASE__								=   tf_tokenizer([test_inputs]		)

													for key in python_outputs.keys():
															# convert them to numpy to avoid messing with ragged tensors
															SCREAMING_SNAKE_CASE__								=   python_outputs[key].numpy()
															SCREAMING_SNAKE_CASE__								=   tf_outputs[key].numpy()

															self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape		)		)
															self.assertTrue(tf.reduce_all(tf.cast(__A				,					tf.intaa		) == tf_outputs_values		)		)


							@slow
							def   _snake_case	(				self     :Any		) -> int:



									"""simple docstring"""



									for tf_tokenizer in self.tf_tokenizers:
											SCREAMING_SNAKE_CASE__								=   tf.function(__A		)
											for test_inputs in self.test_sentences:
													SCREAMING_SNAKE_CASE__								=   tf.constant(__A		)
													SCREAMING_SNAKE_CASE__								=   compiled_tokenizer(__A		)
													SCREAMING_SNAKE_CASE__								=   tf_tokenizer(__A		)

													for key in eager_outputs.keys():
															self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)


							@slow
							def   _snake_case	(				self     :List[Any]		) -> Any:



									"""simple docstring"""



									for tf_tokenizer in self.tf_tokenizers:
											SCREAMING_SNAKE_CASE__								=   ModelToSave(tokenizer=__A		)
											SCREAMING_SNAKE_CASE__								=   tf.convert_to_tensor([self.test_sentences[0]]		)
											SCREAMING_SNAKE_CASE__								=   model.serving(__A		)  # Build model with some sample inputs
											with TemporaryDirectory() as tempdir:
													SCREAMING_SNAKE_CASE__								=   Path(__A		) / """saved.model"""
													tf.saved_model.save(__A				,					__A				,					signatures={"""serving_default""": model.serving}		)
													SCREAMING_SNAKE_CASE__								=   tf.saved_model.load(__A		)
											SCREAMING_SNAKE_CASE__								=   loaded_model.signatures["""serving_default"""](__A		)["""output_0"""]
											# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
											self.assertTrue(tf.reduce_all(out == loaded_output		)		)


							@slow
							def   _snake_case	(				self     :Dict		) -> int:



									"""simple docstring"""



									for tf_tokenizer in self.tf_tokenizers:
											SCREAMING_SNAKE_CASE__								=   tf.convert_to_tensor([self.test_sentences[0]]		)
											SCREAMING_SNAKE_CASE__								=   tf_tokenizer(__A		)  # Build model with some sample inputs

											SCREAMING_SNAKE_CASE__								=   tf_tokenizer.get_config()
											SCREAMING_SNAKE_CASE__								=   TFGPTaTokenizer.from_config(__A		)
											SCREAMING_SNAKE_CASE__								=   model_from_config(__A		)

											for key in from_config_output.keys():
													self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]		)		)


							@slow
							def   _snake_case	(				self     :List[Any]		) -> Any:



									"""simple docstring"""



									for tf_tokenizer in self.tf_tokenizers:
											# for the test to run
											SCREAMING_SNAKE_CASE__								=   12_3123

											for max_length in [3, 5, 1024]:
													SCREAMING_SNAKE_CASE__								=   tf.convert_to_tensor([self.test_sentences[0]]		)
													SCREAMING_SNAKE_CASE__								=   tf_tokenizer(__A				,					max_length=__A		)

													SCREAMING_SNAKE_CASE__								=   out["""input_ids"""].numpy().shape[1]

													assert out_length == max_length






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowerCamelCase 					=      {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase 					=      ['SpeechEncoderDecoderModel']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase 					=      ['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
	from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
	import sys

	_lowerCamelCase 					=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)