




'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:   Optional[Any]      		= ['gpt2']
_SCREAMING_SNAKE_CASE							:   str      		= 'gpt2'

if is_tf_available():




		class  _snake_case		(	tf.Module							):




					def __init__(					self				,							a__							)				->				Optional[Any]:

												'''simple docstring'''




												super().__init__()
												snake_case_						=      tokenizer
												snake_case_						=      AutoConfig.from_pretrained(UpperCamelCase__							)
												snake_case_						=      TFGPTaLMHeadModel.from_config(UpperCamelCase__							)




					@tf.function(input_signature=(tf.TensorSpec((None,)				,							tf.string				,							name="text"							),)							)
					def    lowerCAmelCase__				(					self				,							a__							)				->				int:

												'''simple docstring'''




												snake_case_						=      self.tokenizer(UpperCamelCase__							)
												snake_case_						=      tokenized['''input_ids'''].to_tensor()

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

												snake_case_						=      self.model(input_ids=UpperCamelCase__				,							attention_mask=UpperCamelCase__							)['''logits''']

												return outputs




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




			def    lowerCAmelCase__				(					self							)				->				List[str]:

										'''simple docstring'''




										super().setUp()

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

										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''',
										]
										snake_case_						=      list(zip(self.test_sentences				,							self.test_sentences[::-1]							)							)




			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




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

																								for key in python_outputs.keys():
																															# convert them to numpy to avoid messing with ragged tensors
																															snake_case_						=      python_outputs[key].numpy()
																															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(UpperCamelCase__				,							tf.intaa							) == tf_outputs_values							)							)




			@slow
			def    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




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

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




			@slow
			def    lowerCAmelCase__				(					self							)				->				List[str]:

										'''simple docstring'''




										for tf_tokenizer in self.tf_tokenizers:
																	snake_case_						=      ModelToSave(tokenizer=UpperCamelCase__							)
																	snake_case_						=      tf.convert_to_tensor([self.test_sentences[0]]							)
																	snake_case_						=      model.serving(UpperCamelCase__							)  # Build model with some sample inputs
																	with TemporaryDirectory() as tempdir:
																								snake_case_						=      Path(UpperCamelCase__							) / '''saved.model'''
																								tf.saved_model.save(UpperCamelCase__				,							UpperCamelCase__				,							signatures={"serving_default": model.serving}							)
																								snake_case_						=      tf.saved_model.load(UpperCamelCase__							)
																	snake_case_						=      loaded_model.signatures['''serving_default'''](UpperCamelCase__							)['''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    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




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

																	snake_case_						=      tf_tokenizer.get_config()
																	snake_case_						=      TFGPTaTokenizer.from_config(UpperCamelCase__							)
																	snake_case_						=      model_from_config(UpperCamelCase__							)

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




			@slow
			def    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




										for tf_tokenizer in self.tf_tokenizers:
																	# for the test to run
																	snake_case_						=      123_123

																	for max_length in [3, 5, 1_024]:
																								snake_case_						=      tf.convert_to_tensor([self.test_sentences[0]]							)
																								snake_case_						=      tf_tokenizer(UpperCamelCase__				,							max_length=UpperCamelCase__							)

																								snake_case_						=      out['''input_ids'''].numpy().shape[1]

																								assert out_length == max_length





from typing import TYPE_CHECKING

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


__UpperCamelCase		:       List[Any]									=     {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[Any]									=     ['GLPNFeatureExtractor']
					__UpperCamelCase		:       List[str]									=     ['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

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





