



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig


A_		:  str										=  logging.get_logger(__name__)



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      '''vision-text-dual-encoder'''
   _UpperCAmelCase  						=      True

   def __init__(   self      :							Dict				, __lowerCAmelCase      :							Any=512				, __lowerCAmelCase      :							Any=2.6_5_9_2				, **__lowerCAmelCase      :							str				)							->					Optional[int]:


    """simple docstring"""






    super().__init__(**__lowerCAmelCase				)

    if "vision_config" not in kwargs:
     raise ValueError("`vision_config` can not be `None`."				)

    if "text_config" not in kwargs:
     raise ValueError("`text_config` can not be `None`."				)

    a							=		kwargs.pop("vision_config"				)
    a							=		kwargs.pop("text_config"				)

    a							=		vision_config.pop("model_type"				)
    a							=		text_config.pop("model_type"				)

    if vision_model_type == "clip":
     a							=		AutoConfig.for_model(__lowerCAmelCase				, **__lowerCAmelCase				).vision_config
    elif vision_model_type == "clip_vision_model":
     a							=		CLIPVisionConfig(**__lowerCAmelCase				)
    else:
     a							=		AutoConfig.for_model(__lowerCAmelCase				, **__lowerCAmelCase				)

    a							=		AutoConfig.for_model(__lowerCAmelCase				, **__lowerCAmelCase				)

    a							=		projection_dim
    a							=		logit_scale_init_value

   @classmethod
   def 		A				(   cls      :							str				, __lowerCAmelCase      :							PretrainedConfig				, __lowerCAmelCase      :							PretrainedConfig				, **__lowerCAmelCase      :							Optional[Any]				)							->					Union[str, Any]:


    """simple docstring"""






    return cls(vision_config=vision_config.to_dict()				, text_config=text_config.to_dict()				, **__lowerCAmelCase				)




   def 		A				(   self      :							str				)							->					int:


    """simple docstring"""






    a							=		copy.deepcopy(self.__dict__				)
    a							=		self.vision_config.to_dict()
    a							=		self.text_config.to_dict()
    a							=		self.__class__.model_type
    return output






# Copyright 2022 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 os
from typing import List
def   UpperCAmelCase							(		A__				,  A__     )							->					Any:
	if name != "training_function" and name != "main":
		raise ValueError(f'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''     )
	_snake_case     , _snake_case					:							Union[str, Any]					     =   [], False
	for line in lines:
		if not found_start and f'''def {name}''' in line:
			_snake_case					:							Tuple					     =   True
			good_lines.append(A__     )
			continue
		if found_start:
			if name == "training_function" and "def main" in line:
				return good_lines
			if name == "main" and "if __name__" in line:
				return good_lines
			good_lines.append(A__     )
def   UpperCAmelCase							(		A__     )							->					Dict:
	return [line for line in lines if not line.lstrip().startswith("""#"""     ) and line != "\n"]


def   UpperCAmelCase							(		A__				,  A__				,  A__				,  A__ = None     )							->					str:
	with open(A__				,  """r"""     ) as f:
		_snake_case					:							Tuple					     =   f.readlines()
	with open(os.path.abspath(os.path.join("""examples"""				,  """nlp_example.py"""     )     )				,  """r"""     ) as f:
		_snake_case					:							Optional[Any]					     =   f.readlines()
	with open(A__				,  """r"""     ) as f:
		_snake_case					:							Optional[Any]					     =   f.readlines()
	if secondary_filename is not None:
		with open(A__				,  """r"""     ) as f:
			_snake_case					:							List[str]					     =   f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
	if parser_only:
		_snake_case					:							Dict					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		_snake_case					:							int					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		_snake_case					:							Optional[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		if secondary_filename is not None:
			_snake_case					:							Optional[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
	else:
		_snake_case					:							str					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		_snake_case					:							List[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		_snake_case					:							Union[str, Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		if secondary_filename is not None:
			_snake_case					:							List[Any]					     =   clean_lines(
			    get_function_contents_by_name(A__				,  """training_function"""     )     )

	_snake_case					:							str					     =   """train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

	# Specific code in our script that differs from the full version, aka what is new
	_snake_case					:							Dict					     =   []
	_snake_case					:							str					     =   []  # We keep track of the idxs just in case it's a repeated statement
	_snake_case					:							Optional[Any]					     =   iter(A__     )
	for i in range(len(A__     ) - 1     ):
		if i not in passed_idxs:
			_snake_case					:							Any					     =   next(A__     )
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_feature_code.append(A__     )
					passed_idxs.append(A__     )
				else:
					# Skip over the `config['num_epochs'] = 2` statement
					_snake_case					:							Optional[int]					     =   next(A__     )

    # Extract out just the new parts from the full_file_training_func
	_snake_case					:							Any					     =   []
	_snake_case					:							Tuple					     =   []  # We keep track of the idxs just in case it's a repeated statement
	for i, line in enumerate(A__     ):
		if i not in passed_idxs:
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_full_example_parts.append(A__     )
					passed_idxs.append(A__     )

    # Finally, get the overall diff
	_snake_case					:							Optional[int]					     =   [line for line in new_feature_code if line not in new_full_example_parts]
	if secondary_filename is not None:
		_snake_case					:							Optional[int]					     =   [line for line in full_file_contents if line not in secondary_file_func]
		_snake_case					:							int					     =   [line for line in diff_from_example if line not in diff_from_two]

	return diff_from_example





