





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
			import tensorflow as tf

if is_tensorflow_text_available():
			from transformers.models.bert import TFBertTokenizer


__UpperCamelCase:	Any = ["""bert-base-uncased""", """bert-base-cased"""]
__UpperCamelCase:	Tuple = """hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():


			class       __lowerCAmelCase					(       tf.keras.Model      ):


				'''simple docstring'''





				def __init__(						self:						Any,					lowerCamelCase_:						Union[str, Any]			):
						super().__init__()
						lowercase__					:      Optional[Any]      =		tokenizer
						lowercase__					:      Union[str, Any]      =		AutoConfig.from_pretrained(lowerCamelCase_			)
						lowercase__					:      Union[str, Any]      =		TFAutoModel.from_config(lowerCamelCase_			)





				def    snake_case__(						self:						Dict,					lowerCamelCase_:						Dict			):
						lowercase__					:      List[Any]      =		self.tokenizer(lowerCamelCase_			)
						lowercase__					:      Optional[int]      =		self.bert(**lowerCamelCase_			)
						return out["pooler_output"]


@require_tf
@require_tensorflow_text
class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	def    snake_case__(						self:						Optional[int]			):
			super().setUp()

			lowercase__					:      Dict      =		[
			    BertTokenizer.from_pretrained(lowerCamelCase_			) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
			]  # repeat for when fast_bert_tokenizer=false
			lowercase__					:      Optional[int]      =		[TFBertTokenizer.from_pretrained(lowerCamelCase_			) for checkpoint in TOKENIZER_CHECKPOINTS] + [
			    TFBertTokenizer.from_pretrained(lowerCamelCase_,					use_fast_bert_tokenizer=lowerCamelCase_			)
			    for checkpoint in TOKENIZER_CHECKPOINTS
			]
			assert len(self.tokenizers			) == len(self.tf_tokenizers			)

			lowercase__					:      Optional[int]      =		[
			    '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',
			]
			lowercase__					:      Tuple      =		list(zip(self.test_sentences,					self.test_sentences[::-1]			)			)

	def    snake_case__(						self:						Union[str, Any]			):
			for tokenizer, tf_tokenizer in zip(self.tokenizers,					self.tf_tokenizers			):
					for test_inputs in (self.test_sentences, self.paired_sentences):
							lowercase__					:      str      =		tokenizer(lowerCamelCase_,					return_tensors='tf',					padding='longest'			)
							lowercase__					:      Dict      =		tf_tokenizer(lowerCamelCase_			)

							for key in python_outputs.keys():
									self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape			)			)
									self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key],					tf.intaa			) == tf_outputs[key]			)			)

	@slow
	def    snake_case__(						self:						int			):
			for tf_tokenizer in self.tf_tokenizers:
					lowercase__					:      Union[str, Any]      =		tf_tokenizer(self.paired_sentences			)
					lowercase__					:      int      =		tf_tokenizer(
					    text=[sentence[0] for sentence in self.paired_sentences],					text_pair=[sentence[1] for sentence in self.paired_sentences],					)
					for key in merged_outputs.keys():
							self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key],					tf.intaa			) == separated_outputs[key]			)			)

	@slow
	def    snake_case__(						self:						int			):
			for tf_tokenizer in self.tf_tokenizers:
					lowercase__					:      List[Any]      =		tf.function(lowerCamelCase_			)
					for test_inputs in (self.test_sentences, self.paired_sentences):
							lowercase__					:      Dict      =		tf.constant(lowerCamelCase_			)
							lowercase__					:      Tuple      =		compiled_tokenizer(lowerCamelCase_			)
							lowercase__					:      Dict      =		tf_tokenizer(lowerCamelCase_			)

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





	@slow
	def    snake_case__(						self:						Tuple			):
			for tf_tokenizer in self.tf_tokenizers:
					lowercase__					:      Optional[int]      =		ModelToSave(tokenizer=lowerCamelCase_			)
					lowercase__					:      Tuple      =		tf.convert_to_tensor(self.test_sentences			)
					lowercase__					:      Optional[int]      =		model(lowerCamelCase_			)  # Build model with some sample inputs
					with TemporaryDirectory() as tempdir:
							lowercase__					:      Any      =		Path(lowerCamelCase_			) / 'saved.model'
							model.save(lowerCamelCase_			)
							lowercase__					:      List[Any]      =		tf.keras.models.load_model(lowerCamelCase_			)
					lowercase__					:      Union[str, Any]      =		loaded_model(lowerCamelCase_			)
					# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
					self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output			)			),					1E-5			)









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase:	List[Any] = {
    """configuration_efficientnet""": [
        """EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientNetConfig""",
        """EfficientNetOnnxConfig""",
    ]
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:	Dict = ["""EfficientNetImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:	Dict = [
			    """EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """EfficientNetForImageClassification""",
			    """EfficientNetModel""",
			    """EfficientNetPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_efficientnet import (
			    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    EfficientNetConfig,
			    EfficientNetOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_efficientnet import EfficientNetImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_efficientnet import (
						    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    EfficientNetForImageClassification,
						    EfficientNetModel,
						    EfficientNetPreTrainedModel,
						)

else:
			import sys

			__UpperCamelCase:	List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)


