
"""simple docstring"""


import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Union[str, Any]          =    'M-CLIP'




				def __init__(				self							,						UpperCAmelCase=1024							,						UpperCAmelCase=768							,						**UpperCAmelCase   ):
							lowerCamelCase_					=  transformerDimSize
							lowerCamelCase_					=  imageDimSize
							super().__init__(**UpperCAmelCase   )




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Any          =    MCLIPConfig




				def __init__(				self							,						UpperCAmelCase							,						*UpperCAmelCase							,						**UpperCAmelCase   ):
							super().__init__(UpperCAmelCase							,						*UpperCAmelCase							,						**UpperCAmelCase   )
							lowerCamelCase_					=  XLMRobertaModel(UpperCAmelCase   )
							lowerCamelCase_					=  torch.nn.Linear(
							    in_features=config.transformerDimensions							,						out_features=config.numDims   )





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  self.transformer(input_ids=UpperCAmelCase							,						attention_mask=UpperCAmelCase   )[0]
							lowerCamelCase_					=  (embs * attention_mask.unsqueeze(2   )).sum(dim=1   ) / attention_mask.sum(dim=1   )[:, None]
							return self.LinearTransformation(UpperCAmelCase   ), embs











# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# 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.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async






def     a     (		lowerCamelCase_=None							):






				'''simple docstring'''
				if subparsers is not None:
								lowercase__  					=     subparsers.add_parser('''test'''							)
				else:
								lowercase__  					=     argparse.ArgumentParser('''Accelerate test command'''							)

				parser.add_argument(
				    '''--config_file'''						,  default=lowerCamelCase_						,  help=(
				        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
				        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
				        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
				        '''with \'huggingface\'.'''
				    )						,  )

				if subparsers is not None:
								parser.set_defaults(func=lowerCamelCase_							)
				return parser






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     os.path.sep.join(__file__.split(os.path.sep							)[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']							)

				if args.config_file is None:
								lowercase__  					=     script_name
				else:
								lowercase__  					=     F"""--config_file={args.config_file} {script_name}"""

				lowercase__  					=     ['''accelerate-launch'''] + test_args.split()
				lowercase__  					=     execute_subprocess_async(lowerCamelCase_						,  env=os.environ.copy()							)
				if result.returncode == 0:
								print('''Test is a success! You are ready for your distributed training!'''							)






def     a     (		):






				'''simple docstring'''
				lowercase__  					=     test_command_parser()
				lowercase__  					=     parser.parse_args()
				test_command(lowerCamelCase_							)


if __name__ == "__main__":
							main()




