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

if is_tf_available():





      class      A							(				tf.keras.Model     ):


         def __init__(self     :						Any       ,  __UpperCAmelCase     :						Tuple		)						->				Optional[Any]:
               """simple docstring"""



               super().__init__()
               UpperCAmelCase__								=  tokenizer
               UpperCAmelCase__								=  AutoConfig.from_pretrained(__UpperCAmelCase		)
               UpperCAmelCase__								=  TFAutoModel.from_config(__UpperCAmelCase		)





         def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				Optional[int]:
               """simple docstring"""



               UpperCAmelCase__								=  self.tokenizer(__UpperCAmelCase		)
               UpperCAmelCase__								=  self.bert(**__UpperCAmelCase		)
               return out["pooler_output"]





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


   def 			lowercase_      (self     :						List[str]		)						->				List[str]:
         """simple docstring"""



         super().setUp()

         UpperCAmelCase__								=  [
             BertTokenizer.from_pretrained(__UpperCAmelCase		) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
         ]  # repeat for when fast_bert_tokenizer=false
         UpperCAmelCase__								=  [TFBertTokenizer.from_pretrained(__UpperCAmelCase		) for checkpoint in TOKENIZER_CHECKPOINTS] + [
             TFBertTokenizer.from_pretrained(__UpperCAmelCase       ,  use_fast_bert_tokenizer=__UpperCAmelCase		)
             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 			lowercase_      (self     :						Any		)						->				int:
         """simple docstring"""



         for tokenizer, tf_tokenizer in zip(self.tokenizers       ,  self.tf_tokenizers		):
               for test_inputs in (self.test_sentences, self.paired_sentences):
                     UpperCAmelCase__								=  tokenizer(__UpperCAmelCase       ,  return_tensors="tf"       ,  padding="longest"		)
                     UpperCAmelCase__								=  tf_tokenizer(__UpperCAmelCase		)

                     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 			lowercase_      (self     :						List[Any]		)						->				List[Any]:
         """simple docstring"""



         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 			lowercase_      (self     :						Union[str, Any]		)						->				Optional[int]:
         """simple docstring"""



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

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





   @slow
   def 			lowercase_      (self     :						str		)						->				List[str]:
         """simple docstring"""



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






# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCamelCase__:   Optional[int]								=			"3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
						import torch

						print("Torch version:", torch.__version__)
						print("Cuda available:", torch.cuda.is_available())
						print("Cuda version:", torch.version.cuda)
						print("CuDNN version:", torch.backends.cudnn.version())
						print("Number of GPUs available:", torch.cuda.device_count())
						print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
						print("Torch version:", None)

try:
						import deepspeed

						print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
						print("DeepSpeed version:", None)

try:
						import tensorflow as tf

						print("TensorFlow version:", tf.__version__)
						print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
						print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
						print("TensorFlow version:", None)





