






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     __SCREAMING_SNAKE_CASE					(   __lowercase		,					unittest.TestCase):
						_SCREAMING_SNAKE_CASE	:       List[Any]   =					BlenderbotSmallTokenizer
						_SCREAMING_SNAKE_CASE	:       str   =					False






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												super().setUp()

												lowerCAmelCase__    						=						['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
												lowerCAmelCase__    						=						dict(zip(_UpperCamelCase     ,						range(len(_UpperCamelCase   )   )   )   )

												lowerCAmelCase__    						=						['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
												lowerCAmelCase__    						=						{'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

												lowerCAmelCase__    						=						os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES['vocab_file']   )
												lowerCAmelCase__    						=						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(_UpperCamelCase   ) + '\n'   )
												with open(self.merges_file     ,						'w'     ,						encoding='utf-8'   ) as fp:
																		fp.write('\n'.join(_UpperCamelCase   )   )






						def       UpperCamelCase__      (	self     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												kwargs.update(self.special_tokens_map   )
												return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname     ,						**_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						'adapt act apte'
												lowerCAmelCase__    						=						'adapt act apte'
												return input_text, output_text






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						BlenderbotSmallTokenizer(self.vocab_file     ,						self.merges_file     ,						**self.special_tokens_map   )
												lowerCAmelCase__    						=						'adapt act apte'
												lowerCAmelCase__    						=						['adapt', 'act', 'ap@@', 'te']
												lowerCAmelCase__    						=						tokenizer.tokenize(_UpperCamelCase   )
												self.assertListEqual(_UpperCamelCase     ,						_UpperCamelCase   )

												lowerCAmelCase__    						=						[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

												lowerCAmelCase__    						=						[0, 1, 2, 3, 4, 5]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase   )     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
												assert tok('sam'   ).input_ids == [13_84]
												lowerCAmelCase__    						=						'I am a small frog.'
												lowerCAmelCase__    						=						tok([src_text]     ,						padding=_UpperCamelCase     ,						truncation=_UpperCamelCase   )['input_ids']
												lowerCAmelCase__    						=						tok.batch_decode(_UpperCamelCase     ,						skip_special_tokens=_UpperCamelCase     ,						clean_up_tokenization_spaces=_UpperCamelCase   )[0]
												assert src_text != decoded  # I wish it did!
												assert decoded == "i am a small frog ."







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
												lowerCAmelCase__    						=						'I am a small frog .'
												lowerCAmelCase__    						=						'.'
												lowerCAmelCase__    						=						tok(_UpperCamelCase   )['input_ids']
												lowerCAmelCase__    						=						tok(_UpperCamelCase   )['input_ids']

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













import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Optional[Any]		)  ->   Any:

						"""simple docstring"""



						lowerCAmelCase__    						=						BertConfig.from_json_file(UpperCamelCase_		)
						print(F"Building PyTorch model from configuration: {config}"		)
						lowerCAmelCase__    						=						BertForPreTraining(UpperCamelCase_		)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)

						# Save pytorch-model
						print(F"Save PyTorch model to {pytorch_dump_path}"		)
						torch.save(model.state_dict()    ,       UpperCamelCase_		)


if __name__ == "__main__":
					__snake_case    :    Union[str, Any]         =     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--bert_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					__snake_case    :    List[str]         =     parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





