"""simple docstring"""






import copy
import os
from typing import Union

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


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

lowerCAmelCase__  :  List[str]						     =	{
    '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 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "blip_text_model"







					def __init__(				self   :	Tuple    ,lowerCamelCase__   :	str=30_524    ,lowerCamelCase__   :	Tuple=768    ,lowerCamelCase__   :	Union[str, Any]=768    ,lowerCamelCase__   :	Union[str, Any]=3_072    ,lowerCamelCase__   :	str=768    ,lowerCamelCase__   :	Optional[int]=12    ,lowerCamelCase__   :	Any=8    ,lowerCamelCase__   :	List[Any]=512    ,lowerCamelCase__   :	Tuple="gelu"    ,lowerCamelCase__   :	Dict=1e-12    ,lowerCamelCase__   :	Dict=0.0    ,lowerCamelCase__   :	Union[str, Any]=0.0    ,lowerCamelCase__   :	int=0.0_2    ,lowerCamelCase__   :	List[str]=30_522    ,lowerCamelCase__   :	Tuple=2    ,lowerCamelCase__   :	Tuple=0    ,lowerCamelCase__   :	str=102    ,lowerCamelCase__   :	Tuple=True    ,lowerCamelCase__   :	str=True    ,**lowerCamelCase__   :	Optional[Any]    ,):
									super().__init__(
									    pad_token_id=lowerCamelCase__    ,bos_token_id=lowerCamelCase__    ,eos_token_id=lowerCamelCase__    ,sep_token_id=lowerCamelCase__    ,**lowerCamelCase__    ,)

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







					@classmethod
					def 		__lowerCAmelCase						(				cls   :	Union[str, Any]    ,lowerCamelCase__   :	Union[str, os.PathLike]    ,**lowerCamelCase__   :	List[Any]			):
									cls._set_token_in_kwargs(lowerCamelCase__			)

									UpperCAmelCase__      ,	UpperCAmelCase__             =							cls.get_config_dict(lowerCamelCase__    ,**lowerCamelCase__			)

									# get the text config dict if we are loading from BlipConfig
									if config_dict.get('model_type'			) == "blip":
													UpperCAmelCase__             =							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(lowerCamelCase__    ,**lowerCamelCase__			)






class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "blip_vision_model"







					def __init__(				self   :	Tuple    ,lowerCamelCase__   :	List[str]=768    ,lowerCamelCase__   :	Any=3_072    ,lowerCamelCase__   :	List[Any]=512    ,lowerCamelCase__   :	Optional[Any]=12    ,lowerCamelCase__   :	Any=12    ,lowerCamelCase__   :	List[str]=384    ,lowerCamelCase__   :	Any=16    ,lowerCamelCase__   :	Any="gelu"    ,lowerCamelCase__   :	Any=1e-5    ,lowerCamelCase__   :	Any=0.0    ,lowerCamelCase__   :	Optional[int]=1e-10    ,**lowerCamelCase__   :	Union[str, Any]    ,):
									super().__init__(**lowerCamelCase__			)

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







					@classmethod
					def 		__lowerCAmelCase						(				cls   :	int    ,lowerCamelCase__   :	Union[str, os.PathLike]    ,**lowerCamelCase__   :	List[str]			):
									cls._set_token_in_kwargs(lowerCamelCase__			)

									UpperCAmelCase__      ,	UpperCAmelCase__             =							cls.get_config_dict(lowerCamelCase__    ,**lowerCamelCase__			)

									# get the vision config dict if we are loading from BlipConfig
									if config_dict.get('model_type'			) == "blip":
													UpperCAmelCase__             =							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(lowerCamelCase__    ,**lowerCamelCase__			)






class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "blip"
					snake_case__				    = True







					def __init__(				self   :	Any    ,lowerCamelCase__   :	List[Any]=None    ,lowerCamelCase__   :	List[Any]=None    ,lowerCamelCase__   :	Dict=512    ,lowerCamelCase__   :	List[str]=2.6_5_9_2    ,lowerCamelCase__   :	List[Any]=256    ,**lowerCamelCase__   :	int    ,):
									super().__init__(**lowerCamelCase__			)

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

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

									UpperCAmelCase__             =							BlipTextConfig(**lowerCamelCase__			)
									UpperCAmelCase__             =							BlipVisionConfig(**lowerCamelCase__			)

									UpperCAmelCase__             =							self.vision_config.hidden_size

									UpperCAmelCase__             =							projection_dim
									UpperCAmelCase__             =							logit_scale_init_value
									UpperCAmelCase__             =							1.0
									UpperCAmelCase__             =							0.0_2
									UpperCAmelCase__             =							image_text_hidden_size







					@classmethod
					def 		__lowerCAmelCase						(				cls   :	List[Any]    ,lowerCamelCase__   :	BlipTextConfig    ,lowerCamelCase__   :	BlipVisionConfig    ,**lowerCamelCase__   :	int			):
									return cls(text_config=text_config.to_dict()    ,vision_config=vision_config.to_dict()    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							copy.deepcopy(self.__dict__			)
									UpperCAmelCase__             =							self.text_config.to_dict()
									UpperCAmelCase__             =							self.vision_config.to_dict()
									UpperCAmelCase__             =							self.__class__.model_type
									return output




"""simple docstring"""






import os

import numpy
import onnx



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							a.name
				UpperCAmelCase__             =							b.name

				UpperCAmelCase__             =							''
				UpperCAmelCase__             =							''

				UpperCAmelCase__             =							a == b

				UpperCAmelCase__             =							name_a
				UpperCAmelCase__             =							name_b

				return res



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				for i, input_name in enumerate(node_proto.input       ):
								if input_name == name:
												node_proto.input.insert(lowerCamelCase					,			lowerCamelCase       )
												node_proto.input.pop(i + 1       )

				if node_proto.op_type == "If":
								_graph_replace_input_with(node_proto.attribute[0].g					,			lowerCamelCase					,			lowerCamelCase       )
								_graph_replace_input_with(node_proto.attribute[1].g					,			lowerCamelCase					,			lowerCamelCase       )
				if node_proto.op_type == "Loop":
								_graph_replace_input_with(node_proto.attribute[0].g					,			lowerCamelCase					,			lowerCamelCase       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				for n in graph_proto.node:
								_node_replace_input_with(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							list(model.graph.initializer       )
				UpperCAmelCase__             =							list(model_without_ext.graph.initializer       )
				for i, ref_i in ind_to_replace:
								assert inits_with_data[i].name == inits[i].name
								assert inits_with_data[ref_i].name == inits[ref_i].name
								assert i > ref_i

								UpperCAmelCase__             =							inits[i].name
								UpperCAmelCase__             =							inits[ref_i].name

								model_without_ext.graph.initializer.remove(inits[i]       )

								# for n in model.graph.node:
								_graph_replace_input_with(model_without_ext.graph					,			lowerCamelCase					,			lowerCamelCase       )







def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							os.path.dirname(lowerCamelCase       )
				UpperCAmelCase__             =							os.path.basename(lowerCamelCase       )

				UpperCAmelCase__             =							onnx.load(os.path.join(lowerCamelCase					,			lowerCamelCase       )       )

				UpperCAmelCase__             =							list(model.graph.initializer       )

				UpperCAmelCase__             =							set()
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							[]

				UpperCAmelCase__             =							0

				for i in range(len(lowerCamelCase       )       ):
								if i in dup_set:
												continue

								for j in range(i + 1					,			len(lowerCamelCase       )       ):
												if j in dup_set:
																continue
												if _is_equal_tensor_proto(inits[i]					,			inits[j]       ):
																dup_set.add(lowerCamelCase       )
																dup_set.add(lowerCamelCase       )

																UpperCAmelCase__             =							inits[j].data_type
																UpperCAmelCase__             =							numpy.prod(inits[j].dims       )
																if dtype == 1:
																				mem_size *= 4
																elif dtype == 6:
																				mem_size *= 4
																elif dtype == 7 or dtype == 1_1:
																				mem_size *= 8
																else:
																				print('unexpected data type: '					,			lowerCamelCase       )
																total_reduced_size += mem_size

																UpperCAmelCase__             =							inits[i].name
																UpperCAmelCase__             =							inits[j].name

																if name_i in dup_map:
																				dup_map[name_i].append(lowerCamelCase       )
																else:
																				UpperCAmelCase__             =							[name_j]
																ind_to_replace.append((j, i)       )

				print('total reduced size: '					,			total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4					,			'GB'       )

				UpperCAmelCase__             =							sorted(lowerCamelCase       )
				_remove_dup_initializers_from_model(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

				UpperCAmelCase__             =							'optimized_' + model_file_name
				UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			lowerCamelCase       )
				onnx.save(lowerCamelCase					,			lowerCamelCase       )

				return new_model



