




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
							import tensorflow as tf

							from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM




@require_tf
@require_sentencepiece
@require_tokenizers
class 		__A      (			unittest.TestCase			):

						@slow
						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	str								=TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small""" )
										__magic_name__	:	str								=AutoTokenizer.from_pretrained("""google/mt5-small""" )

										__magic_name__	:	Tuple								=tokenizer("""Hello there"""    , return_tensors="""tf""" ).input_ids
										__magic_name__	:	Optional[Any]								=tokenizer("""Hi I am"""    , return_tensors="""tf""" ).input_ids

										__magic_name__	:	Optional[Any]								=model(snake_case__    , labels=snake_case__ ).loss
										__magic_name__	:	int								=-tf.math.reduce_mean(snake_case__ ).numpy()

										__magic_name__	:	int								=-21.228168
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2E-4 )





# 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_		:				str									=     "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)




