
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


_A						=			['bert-base-uncased', 'bert-base-cased']
_A						=			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():






					class 	_lowercase       (    tf.keras.Model		):




						def __init__(			self		,			UpperCAmelCase_ )		->							str:
								super().__init__()
								lowerCamelCase					:      List[Any]          =   tokenizer
								lowerCamelCase					:      int          =   AutoConfig.from_pretrained(lowerCAmelCase__ )
								lowerCamelCase					:      List[str]          =   TFAutoModel.from_config(lowerCAmelCase__ )




						def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Tuple:
								lowerCamelCase					:      int          =   self.tokenizer(lowerCAmelCase__ )
								lowerCamelCase					:      List[Any]          =   self.bert(**lowerCAmelCase__ )
								return out["pooler_output"]






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




	def       _UpperCamelCase   (			self )		->							Optional[int]:
			super().setUp()

			lowerCamelCase					:      Union[str, Any]          =   [
			    BertTokenizer.from_pretrained(lowerCAmelCase__ ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
			]  # repeat for when fast_bert_tokenizer=false
			lowerCamelCase					:      List[Any]          =   [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 )

			lowerCamelCase					:      Union[str, Any]          =   [
			    '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',
			]
			lowerCamelCase					:      Optional[Any]          =   list(zip(self.test_sentences		,			self.test_sentences[::-1] ) )




	def       _UpperCamelCase   (			self )		->							Any:
			for tokenizer, tf_tokenizer in zip(self.tokenizers		,			self.tf_tokenizers ):
					for test_inputs in (self.test_sentences, self.paired_sentences):
							lowerCamelCase					:      str          =   tokenizer(lowerCAmelCase__		,			return_tensors='tf'		,			padding='longest' )
							lowerCamelCase					:      Union[str, Any]          =   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       _UpperCamelCase   (			self )		->							Union[str, Any]:
			for tf_tokenizer in self.tf_tokenizers:
					lowerCamelCase					:      List[str]          =   tf_tokenizer(self.paired_sentences )
					lowerCamelCase					:      Union[str, Any]          =   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       _UpperCamelCase   (			self )		->							List[Any]:
			for tf_tokenizer in self.tf_tokenizers:
					lowerCamelCase					:      List[Any]          =   tf.function(lowerCAmelCase__ )
					for test_inputs in (self.test_sentences, self.paired_sentences):
							lowerCamelCase					:      List[str]          =   tf.constant(lowerCAmelCase__ )
							lowerCamelCase					:      Optional[int]          =   compiled_tokenizer(lowerCAmelCase__ )
							lowerCamelCase					:      Dict          =   tf_tokenizer(lowerCAmelCase__ )

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




	@slow
	def       _UpperCamelCase   (			self )		->							Dict:
			for tf_tokenizer in self.tf_tokenizers:
					lowerCamelCase					:      List[str]          =   ModelToSave(tokenizer=lowerCAmelCase__ )
					lowerCamelCase					:      Optional[Any]          =   tf.convert_to_tensor(self.test_sentences )
					lowerCamelCase					:      str          =   model(lowerCAmelCase__ )  # Build model with some sample inputs
					with TemporaryDirectory() as tempdir:
							lowerCamelCase					:      List[str]          =   Path(lowerCAmelCase__ ) / 'saved.model'
							model.save(lowerCAmelCase__ )
							lowerCamelCase					:      Optional[int]          =   tf.keras.models.load_model(lowerCAmelCase__ )
					lowerCamelCase					:      Optional[int]          =   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 )








"""simple docstring"""

import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

_A						=			list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

_A						=			[file for file in filepaths if file != file.lower()]
if upper_files:
					print(F"""{len(upper_files)} files contain uppercase characters:""")
					print('\n'.join(upper_files) + '\n')

_A						=			[file for file in filepaths if ' ' in file]
if space_files:
					print(F"""{len(space_files)} files contain space characters:""")
					print('\n'.join(space_files) + '\n')

_A						=			[file for file in filepaths if '-' in file]
if hyphen_files:
					print(F"""{len(hyphen_files)} files contain hyphen characters:""")
					print('\n'.join(hyphen_files) + '\n')

_A						=			[file for file in filepaths if os.sep not in file]
if nodir_files:
					print(F"""{len(nodir_files)} files are not in a directory:""")
					print('\n'.join(nodir_files) + '\n')

_A						=			len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)






