





"""simple docstring"""





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


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

if is_tf_available():





						class        _lowerCamelCase      (       tf.keras.Model ):


												def __init__(self   ,							__a		) ->   Any:
														super().__init__()
														UpperCamelCase   =	tokenizer
														UpperCamelCase   =	AutoConfig.from_pretrained(__a		)
														UpperCamelCase   =	TFAutoModel.from_config(__a		)




												def     snake_case_   (self   ,							__a		) ->   int:
														UpperCamelCase   =	self.tokenizer(__a		)
														UpperCamelCase   =	self.bert(**__a		)
														return out["pooler_output"]





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


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

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

								UpperCamelCase   =	[
								    "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",
								]
								UpperCamelCase   =	list(zip(self.test_sentences   ,							self.test_sentences[::-1]		)		)


						def     snake_case_   (self		) ->   Optional[int]:
								for tokenizer, tf_tokenizer in zip(self.tokenizers   ,							self.tf_tokenizers		):
										for test_inputs in (self.test_sentences, self.paired_sentences):
												UpperCamelCase   =	tokenizer(__a   ,							return_tensors="tf"   ,							padding="longest"		)
												UpperCamelCase   =	tf_tokenizer(__a		)

												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		) ->   List[Any]:
								for tf_tokenizer in self.tf_tokenizers:
										UpperCamelCase   =	tf_tokenizer(self.paired_sentences		)
										UpperCamelCase   =	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		) ->   Any:
								for tf_tokenizer in self.tf_tokenizers:
										UpperCamelCase   =	tf.function(__a		)
										for test_inputs in (self.test_sentences, self.paired_sentences):
												UpperCamelCase   =	tf.constant(__a		)
												UpperCamelCase   =	compiled_tokenizer(__a		)
												UpperCamelCase   =	tf_tokenizer(__a		)

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




						@slow
						def     snake_case_   (self		) ->   str:
								for tf_tokenizer in self.tf_tokenizers:
										UpperCamelCase   =	ModelToSave(tokenizer=__a		)
										UpperCamelCase   =	tf.convert_to_tensor(self.test_sentences		)
										UpperCamelCase   =	model(__a		)  # Build model with some sample inputs
										with TemporaryDirectory() as tempdir:
												UpperCamelCase   =	Path(__a		) / "saved.model"
												model.save(__a		)
												UpperCamelCase   =	tf.keras.models.load_model(__a		)
										UpperCamelCase   =	loaded_model(__a		)
										# 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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						from transformers import AutoModelForSeqaSeqLM, AutoTokenizer





@require_torch
@require_sentencepiece
@require_tokenizers
class        _lowerCamelCase      (       unittest.TestCase ):


						@slow
						def     snake_case_   (self		) ->   List[str]:
								UpperCamelCase   =	AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"   ,							return_dict=__a		).to(__a		)
								UpperCamelCase   =	AutoTokenizer.from_pretrained("google/mt5-small"		)

								UpperCamelCase   =	tokenizer("Hello there"   ,							return_tensors="pt"		).input_ids
								UpperCamelCase   =	tokenizer("Hi I am"   ,							return_tensors="pt"		).input_ids

								UpperCamelCase   =	model(input_ids.to(__a		)   ,							labels=labels.to(__a		)		).loss
								UpperCamelCase   =	-(labels.shape[-1] * loss.item())

								UpperCamelCase   =	-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1e-4		)






