




# 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)










import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin




class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					BlenderbotSmallTokenizer
						UpperCamelCase     						=					False

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										super().setUp()

										__magic_name__	:	Tuple								=["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
										__magic_name__	:	Optional[Any]								=dict(zip(__snake_case    , range(len(__snake_case ) ) ) )

										__magic_name__	:	List[Any]								=["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
										__magic_name__	:	int								={"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

										__magic_name__	:	Dict								=os.path.join(self.tmpdirname    , VOCAB_FILES_NAMES["""vocab_file"""] )
										__magic_name__	:	int								=os.path.join(self.tmpdirname    , VOCAB_FILES_NAMES["""merges_file"""] )
										with open(self.vocab_file    , """w"""    , encoding="""utf-8""" ) as fp:
														fp.write(json.dumps(__snake_case ) + """\n""" )
										with open(self.merges_file    , """w"""    , encoding="""utf-8""" ) as fp:
														fp.write("""\n""".join(__snake_case ) )

						def     A__						(		self   :Union[str, Any]    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										kwargs.update(self.special_tokens_map )
										return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname    , **__snake_case )

						def     A__						(		self   :str    , __snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Any								="""adapt act apte"""
										__magic_name__	:	str								="""adapt act apte"""
										return input_text, output_text

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=BlenderbotSmallTokenizer(self.vocab_file    , self.merges_file    , **self.special_tokens_map )
										__magic_name__	:	List[str]								="""adapt act apte"""
										__magic_name__	:	Optional[int]								=["""adapt""", """act""", """ap@@""", """te"""]
										__magic_name__	:	List[Any]								=tokenizer.tokenize(__snake_case )
										self.assertListEqual(__snake_case    , __snake_case )

										__magic_name__	:	int								=[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

										__magic_name__	:	Any								=[0, 1, 2, 3, 4, 5]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case )    , __snake_case )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Any								=BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
										assert tok("""sam""" ).input_ids == [13_84]
										__magic_name__	:	Tuple								="""I am a small frog."""
										__magic_name__	:	Tuple								=tok([src_text]    , padding=__snake_case    , truncation=__snake_case )["""input_ids"""]
										__magic_name__	:	Optional[Any]								=tok.batch_decode(__snake_case    , skip_special_tokens=__snake_case    , clean_up_tokenization_spaces=__snake_case )[0]
										assert src_text != decoded  # I wish it did!
										assert decoded == "i am a small frog ."


						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
										__magic_name__	:	int								="""I am a small frog ."""
										__magic_name__	:	List[Any]								="""."""
										__magic_name__	:	List[str]								=tok(__snake_case )["""input_ids"""]
										__magic_name__	:	List[str]								=tok(__snake_case )["""input_ids"""]

										assert encoded[-1] == encoded_dot[0]




