




'''simple docstring'''

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a  :				Optional[int]									=  logging.get_logger(__name__)

a  :				Dict									=  {
    'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json',
    'Salesforce/blip-vqa-capfit-large': (
        'https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-base': (
        'https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-large': (
        'https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'
    ),
    'Salesforce/blip-itm-base-coco': 'https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json',
    'Salesforce/blip-itm-large-coco': 'https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json',
    'Salesforce/blip-itm-base-flikr': 'https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json',
    'Salesforce/blip-itm-large-flikr': (
        'https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'
    ),
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"blip_text_model"


				def __init__(		self    :					Dict       ,				lowercase_    :					Tuple=3_0524       ,				lowercase_    :					Any=768       ,				lowercase_    :					Optional[Any]=768       ,				lowercase_    :					int=3072       ,				lowercase_    :					Tuple=768       ,				lowercase_    :					List[Any]=12       ,				lowercase_    :					Union[str, Any]=8       ,				lowercase_    :					Optional[Any]=512       ,				lowercase_    :					Optional[int]="gelu"       ,				lowercase_    :					int=1e-12       ,				lowercase_    :					Union[str, Any]=0.0       ,				lowercase_    :					str=0.0       ,				lowercase_    :					int=0.02       ,				lowercase_    :					int=3_0522       ,				lowercase_    :					Any=2       ,				lowercase_    :					Any=0       ,				lowercase_    :					int=102       ,				lowercase_    :					List[Any]=True       ,				lowercase_    :					Optional[int]=True       ,				**lowercase_    :					Optional[int]       ,				):
									super().__init__(
									    pad_token_id=lowercase_       ,				bos_token_id=lowercase_       ,				eos_token_id=lowercase_       ,				sep_token_id=lowercase_       ,				**lowercase_       ,				)

									snake_case_									=				vocab_size
									snake_case_									=				hidden_size
									snake_case_									=				encoder_hidden_size
									snake_case_									=				intermediate_size
									snake_case_									=				projection_dim
									snake_case_									=				hidden_dropout_prob
									snake_case_									=				num_hidden_layers
									snake_case_									=				num_attention_heads
									snake_case_									=				max_position_embeddings
									snake_case_									=				layer_norm_eps
									snake_case_									=				hidden_act
									snake_case_									=				initializer_range
									snake_case_									=				attention_probs_dropout_prob
									snake_case_									=				is_decoder
									snake_case_									=				use_cache


				@classmethod
				def   A_ (		cls    :					str       ,				lowercase_    :					Union[str, os.PathLike]       ,				**lowercase_    :					int					):
									cls._set_token_in_kwargs(lowercase_					)

									snake_case_			,snake_case_									=				cls.get_config_dict(lowercase_       ,				**lowercase_					)

									# get the text config dict if we are loading from BlipConfig
									if config_dict.get('''model_type'''					) == "blip":
														snake_case_									=				config_dict['''text_config''']

									if "model_type" in config_dict and hasattr(cls       ,				'''model_type'''					) and config_dict["model_type"] != cls.model_type:
														logger.warning(
														    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
														    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."					)

									return cls.from_dict(lowercase_       ,				**lowercase_					)







class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"blip_vision_model"


				def __init__(		self    :					int       ,				lowercase_    :					Dict=768       ,				lowercase_    :					Tuple=3072       ,				lowercase_    :					Optional[Any]=512       ,				lowercase_    :					Union[str, Any]=12       ,				lowercase_    :					Optional[Any]=12       ,				lowercase_    :					Any=384       ,				lowercase_    :					str=16       ,				lowercase_    :					Union[str, Any]="gelu"       ,				lowercase_    :					Tuple=1e-5       ,				lowercase_    :					Optional[int]=0.0       ,				lowercase_    :					Optional[int]=1e-10       ,				**lowercase_    :					Optional[Any]       ,				):
									super().__init__(**lowercase_					)

									snake_case_									=				hidden_size
									snake_case_									=				intermediate_size
									snake_case_									=				projection_dim
									snake_case_									=				num_hidden_layers
									snake_case_									=				num_attention_heads
									snake_case_									=				patch_size
									snake_case_									=				image_size
									snake_case_									=				initializer_range
									snake_case_									=				attention_dropout
									snake_case_									=				layer_norm_eps
									snake_case_									=				hidden_act


				@classmethod
				def   A_ (		cls    :					Optional[Any]       ,				lowercase_    :					Union[str, os.PathLike]       ,				**lowercase_    :					int					):
									cls._set_token_in_kwargs(lowercase_					)

									snake_case_			,snake_case_									=				cls.get_config_dict(lowercase_       ,				**lowercase_					)

									# get the vision config dict if we are loading from BlipConfig
									if config_dict.get('''model_type'''					) == "blip":
														snake_case_									=				config_dict['''vision_config''']

									if "model_type" in config_dict and hasattr(cls       ,				'''model_type'''					) and config_dict["model_type"] != cls.model_type:
														logger.warning(
														    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
														    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."					)

									return cls.from_dict(lowercase_       ,				**lowercase_					)







class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"blip"
				snake_case_      =		True


				def __init__(		self    :					str       ,				lowercase_    :					str=None       ,				lowercase_    :					List[str]=None       ,				lowercase_    :					List[str]=512       ,				lowercase_    :					int=2.6592       ,				lowercase_    :					List[Any]=256       ,				**lowercase_    :					List[str]       ,				):
									super().__init__(**lowercase_					)

									if text_config is None:
														snake_case_									=				{}
														logger.info('''`text_config` is `None`. Initializing the `BlipTextConfig` with default values.'''					)

									if vision_config is None:
														snake_case_									=				{}
														logger.info('''`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.'''					)

									snake_case_									=				BlipTextConfig(**lowercase_					)
									snake_case_									=				BlipVisionConfig(**lowercase_					)

									snake_case_									=				self.vision_config.hidden_size

									snake_case_									=				projection_dim
									snake_case_									=				logit_scale_init_value
									snake_case_									=				1.0
									snake_case_									=				0.02
									snake_case_									=				image_text_hidden_size


				@classmethod
				def   A_ (		cls    :					int       ,				lowercase_    :					BlipTextConfig       ,				lowercase_    :					BlipVisionConfig       ,				**lowercase_    :					List[str]					):
									return cls(text_config=text_config.to_dict()       ,				vision_config=vision_config.to_dict()       ,				**lowercase_					)


				def   A_ (		self    :					str					):
									snake_case_									=				copy.deepcopy(self.__dict__					)
									snake_case_									=				self.text_config.to_dict()
									snake_case_									=				self.vision_config.to_dict()
									snake_case_									=				self.__class__.model_type
									return output







'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					snake_case_									=				[
					    '''encoder.version''',
					    '''decoder.version''',
					    '''model.encoder.version''',
					    '''model.decoder.version''',
					    '''decoder.output_projection.weight''',
					    '''_float_tensor''',
					    '''encoder.embed_positions._float_tensor''',
					    '''decoder.embed_positions._float_tensor''',
					]
					for k in ignore_keys:
										state_dict.pop(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					snake_case_									=				list(s_dict.keys()	)
					for key in keys:
										if "transformer_layers" in key:
															snake_case_									=				s_dict.pop(__UpperCAmelCase	)
										elif "subsample" in key:
															snake_case_									=				s_dict.pop(__UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_			,snake_case_									=				emb.weight.shape
					snake_case_									=				nn.Linear(__UpperCAmelCase,      __UpperCAmelCase,      bias=__UpperCAmelCase	)
					snake_case_									=				emb.weight.data
					return lin_layer


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Dict:

					'''simple docstring'''

					snake_case_									=				torch.load(__UpperCAmelCase,      map_location='''cpu'''	)
					snake_case_									=				mam_aaa['''args''']
					snake_case_									=				mam_aaa['''model''']
					snake_case_									=				state_dict['''decoder.output_projection.weight''']

					remove_ignore_keys_(__UpperCAmelCase	)
					rename_keys(__UpperCAmelCase	)

					snake_case_									=				state_dict['''decoder.embed_tokens.weight'''].shape[0]

					snake_case_									=				args.share_decoder_input_output_embed

					snake_case_									=				[int(__UpperCAmelCase	) for i in args.conv_kernel_sizes.split(''','''	)]
					snake_case_									=				SpeechaTextConfig(
					    vocab_size=__UpperCAmelCase,      max_source_positions=args.max_source_positions,      max_target_positions=args.max_target_positions,      encoder_layers=args.encoder_layers,      decoder_layers=args.decoder_layers,      encoder_attention_heads=args.encoder_attention_heads,      decoder_attention_heads=args.decoder_attention_heads,      encoder_ffn_dim=args.encoder_ffn_embed_dim,      decoder_ffn_dim=args.decoder_ffn_embed_dim,      d_model=args.encoder_embed_dim,      dropout=args.dropout,      attention_dropout=args.attention_dropout,      activation_dropout=args.activation_dropout,      activation_function='''relu''',      num_conv_layers=len(__UpperCAmelCase	),      conv_channels=args.conv_channels,      conv_kernel_sizes=__UpperCAmelCase,      input_feat_per_channel=args.input_feat_per_channel,      input_channels=args.input_channels,      tie_word_embeddings=__UpperCAmelCase,      num_beams=5,      max_length=200,      use_cache=__UpperCAmelCase,      decoder_start_token_id=2,      early_stopping=__UpperCAmelCase,      )

					snake_case_									=				SpeechaTextForConditionalGeneration(__UpperCAmelCase	)
					snake_case_			,snake_case_									=				model.model.load_state_dict(__UpperCAmelCase,      strict=__UpperCAmelCase	)
					if len(__UpperCAmelCase	) > 0 and not set(__UpperCAmelCase	) <= {
					    "encoder.embed_positions.weights",
					    "decoder.embed_positions.weights",
					}:
										raise ValueError(
										    '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
										    F" but all the following weights are missing {missing}"	)

					if tie_embeds:
										snake_case_									=				make_linear_from_emb(model.model.decoder.embed_tokens	)
					else:
										snake_case_									=				lm_head_weights

					model.save_pretrained(__UpperCAmelCase	)


if __name__ == "__main__":
							a  :				Any									=  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							a  :				List[Any]									=  parser.parse_args()
							convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

