




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 		_lowerCAmelCase       (							tf.Module	):
					"""simple docstring"""
					def __init__(					self     ,		__SCREAMING_SNAKE_CASE       )		->							List[Any]:





						"""simple docstring"""







						super().__init__()
						snake_case__						:     str		=tokenizer
						snake_case__						:     List[Any]		=AutoConfig.from_pretrained(__A       )
						snake_case__						:     Union[str, Any]		=TFGPTaLMHeadModel.from_config(__A       )


					@tf.function(input_signature=(tf.TensorSpec((None,)     ,		tf.string     ,		name='''text'''       ),)       )
					def 					UpperCAmelCase     (					self     ,		__SCREAMING_SNAKE_CASE       )		->							Optional[Any]:





						"""simple docstring"""







						snake_case__						:     List[Any]		=self.tokenizer(__A       )
						snake_case__						:     Optional[Any]		=tokenized['''input_ids'''].to_tensor()

						snake_case__						:     Optional[int]		=tf.cast(input_ids_dense > 0     ,		tf.intaa       )
						# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

						snake_case__						:     Union[str, Any]		=self.model(input_ids=__A     ,		attention_mask=__A       )['''logits''']

						return outputs






@require_tf
@require_keras_nlp
class 		_lowerCAmelCase       (							unittest.TestCase	):
			"""simple docstring"""
			def 					UpperCAmelCase     (					self       )		->							Dict:





				"""simple docstring"""







				super().setUp()

				snake_case__						:     Union[str, Any]		=[GPTaTokenizer.from_pretrained(__A       ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
				snake_case__						:     List[str]		=[TFGPTaTokenizer.from_pretrained(__A       ) for checkpoint in TOKENIZER_CHECKPOINTS]
				assert len(self.tokenizers       ) == len(self.tf_tokenizers       )

				snake_case__						:     str		=[
				    '''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''',
				]
				snake_case__						:     List[Any]		=list(zip(self.test_sentences     ,		self.test_sentences[::-1]       )       )
			def 					UpperCAmelCase     (					self       )		->							Any:





				"""simple docstring"""







				for tokenizer, tf_tokenizer in zip(self.tokenizers     ,		self.tf_tokenizers       ):
					for test_inputs in self.test_sentences:
						snake_case__						:     Any		=tokenizer([test_inputs]     ,		return_tensors='''tf'''       )
						snake_case__						:     List[Any]		=tf_tokenizer([test_inputs]       )

						for key in python_outputs.keys():
							# convert them to numpy to avoid messing with ragged tensors
							snake_case__						:     List[Any]		=python_outputs[key].numpy()
							snake_case__						:     Tuple		=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 					UpperCAmelCase     (					self       )		->							int:





				"""simple docstring"""







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

						for key in eager_outputs.keys():
							self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]       )       )
			@slow
			def 					UpperCAmelCase     (					self       )		->							Any:





				"""simple docstring"""







				for tf_tokenizer in self.tf_tokenizers:
					snake_case__						:     List[str]		=ModelToSave(tokenizer=__A       )
					snake_case__						:     Union[str, Any]		=tf.convert_to_tensor([self.test_sentences[0]]       )
					snake_case__						:     List[str]		=model.serving(__A       )  # Build model with some sample inputs
					with TemporaryDirectory() as tempdir:
						snake_case__						:     Optional[int]		=Path(__A       ) / '''saved.model'''
						tf.saved_model.save(__A     ,		__A     ,		signatures={'''serving_default''': model.serving}       )
						snake_case__						:     Tuple		=tf.saved_model.load(__A       )
					snake_case__						:     Union[str, Any]		=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 					UpperCAmelCase     (					self       )		->							int:





				"""simple docstring"""







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

					snake_case__						:     List[Any]		=tf_tokenizer.get_config()
					snake_case__						:     Any		=TFGPTaTokenizer.from_config(__A       )
					snake_case__						:     Optional[Any]		=model_from_config(__A       )

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


			@slow
			def 					UpperCAmelCase     (					self       )		->							Any:





				"""simple docstring"""







				for tf_tokenizer in self.tf_tokenizers:
					# for the test to run
					snake_case__						:     Union[str, Any]		=12_3123

					for max_length in [3, 5, 1024]:
						snake_case__						:     Union[str, Any]		=tf.convert_to_tensor([self.test_sentences[0]]       )
						snake_case__						:     Optional[int]		=tf_tokenizer(__A     ,		max_length=__A       )

						snake_case__						:     Optional[int]		=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__)