


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 : Optional[int]		=				['''bert-base-uncased''', '''bert-base-cased''']
a : Union[str, Any]		=				'''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():
	class 	lowerCamelCase_       (				tf.keras.Model				):



						'''simple docstring'''







						def __init__(				self ,       snake_case_						)   ->			List[str]:







									'''simple docstring'''

									super().__init__()
									__lowercase 					=   tokenizer
									__lowercase 					=   AutoConfig.from_pretrained(snake_case_						)
									__lowercase 					=   TFAutoModel.from_config(snake_case_						)






						def A      (				self ,       snake_case_						)   ->			Dict:







									'''simple docstring'''

									__lowercase 					=   self.tokenizer(snake_case_						)
									__lowercase 					=   self.bert(**snake_case_						)
									return out["pooler_output"]
@require_tf
@require_tensorflow_text
class 	lowerCamelCase_       (				unittest.TestCase				):



					'''simple docstring'''







					def A      (				self						)   ->			int:







								'''simple docstring'''

								super().setUp()

								__lowercase 					=   [
								    BertTokenizer.from_pretrained(snake_case_						) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
								]  # repeat for when fast_bert_tokenizer=false
								__lowercase 					=   [TFBertTokenizer.from_pretrained(snake_case_						) for checkpoint in TOKENIZER_CHECKPOINTS] + [
								    TFBertTokenizer.from_pretrained(snake_case_ ,       use_fast_bert_tokenizer=snake_case_						)
								    for checkpoint in TOKENIZER_CHECKPOINTS
								]
								assert len(self.tokenizers						) == len(self.tf_tokenizers						)

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






					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

								for tokenizer, tf_tokenizer in zip(self.tokenizers ,       self.tf_tokenizers						):
											for test_inputs in (self.test_sentences, self.paired_sentences):
														__lowercase 					=   tokenizer(snake_case_ ,       return_tensors='''tf''' ,       padding='''longest'''						)
														__lowercase 					=   tf_tokenizer(snake_case_						)

														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 A      (				self						)   ->			Any:







								'''simple docstring'''

								for tf_tokenizer in self.tf_tokenizers:
											__lowercase 					=   tf_tokenizer(self.paired_sentences						)
											__lowercase 					=   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 A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

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

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






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







								'''simple docstring'''

								for tf_tokenizer in self.tf_tokenizers:
											__lowercase 					=   ModelToSave(tokenizer=snake_case_						)
											__lowercase 					=   tf.convert_to_tensor(self.test_sentences						)
											__lowercase 					=   model(snake_case_						)  # Build model with some sample inputs
											with TemporaryDirectory() as tempdir:
														__lowercase 					=   Path(snake_case_						) / '''saved.model'''
														model.save(snake_case_						)
														__lowercase 					=   tf.keras.models.load_model(snake_case_						)
											__lowercase 					=   loaded_model(snake_case_						)
											# 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						)






# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    '''pipelines_utils''',
    '''0.22.0''',
    '''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.''',
    standard_warn=False,
    stacklevel=3,
)


