



import os

import numpy
import onnx






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							str    =    a.name
							_lowerCAmelCase     :							Union[str, Any]    =    b.name

							_lowerCAmelCase     :							Optional[Any]    =    ""
							_lowerCAmelCase     :							Union[str, Any]    =    ""

							_lowerCAmelCase     :							Any    =    a == b

							_lowerCAmelCase     :							List[Any]    =    name_a
							_lowerCAmelCase     :							List[Any]    =    name_b

							return res






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




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

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






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							for n in graph_proto.node:
														_node_replace_input_with(SCREAMING_SNAKE_CASE_				, SCREAMING_SNAKE_CASE_				, SCREAMING_SNAKE_CASE_  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[Any]    =    list(model.graph.initializer  )
							_lowerCAmelCase     :							List[str]    =    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

														_lowerCAmelCase     :							Any    =    inits[i].name
														_lowerCAmelCase     :							Any    =    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				, SCREAMING_SNAKE_CASE_				, SCREAMING_SNAKE_CASE_  )






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[int]    =    os.path.dirname(SCREAMING_SNAKE_CASE_  )
							_lowerCAmelCase     :							int    =    os.path.basename(SCREAMING_SNAKE_CASE_  )

							_lowerCAmelCase     :							List[Any]    =    onnx.load(os.path.join(SCREAMING_SNAKE_CASE_				, SCREAMING_SNAKE_CASE_  )  )

							_lowerCAmelCase     :							Tuple    =    list(model.graph.initializer  )

							_lowerCAmelCase     :							Dict    =    set()
							_lowerCAmelCase     :							List[str]    =    {}
							_lowerCAmelCase     :							List[Any]    =    []

							_lowerCAmelCase     :							Any    =    0

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

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

																												_lowerCAmelCase     :							Union[str, Any]    =    inits[j].data_type
																												_lowerCAmelCase     :							List[Any]    =    numpy.prod(inits[j].dims  )
																												if dtype == 1:
																																			mem_size *= 4
																												elif dtype == 6:
																																			mem_size *= 4
																												elif dtype == 7 or dtype == 11:
																																			mem_size *= 8
																												else:
																																			print("unexpected data type: "				, SCREAMING_SNAKE_CASE_  )
																												total_reduced_size += mem_size

																												_lowerCAmelCase     :							Optional[Any]    =    inits[i].name
																												_lowerCAmelCase     :							int    =    inits[j].name

																												if name_i in dup_map:
																																			dup_map[name_i].append(SCREAMING_SNAKE_CASE_  )
																												else:
																																			_lowerCAmelCase     :							str    =    [name_j]
																												ind_to_replace.append((j, i)  )

							print("total reduced size: "				, total_reduced_size / 1_024 / 1_024 / 1_024				, "GB"  )

							_lowerCAmelCase     :							Optional[int]    =    sorted(SCREAMING_SNAKE_CASE_  )
							_remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE_				, SCREAMING_SNAKE_CASE_				, SCREAMING_SNAKE_CASE_  )

							_lowerCAmelCase     :							Optional[Any]    =    "optimized_" + model_file_name
							_lowerCAmelCase     :							Optional[Any]    =    os.path.join(SCREAMING_SNAKE_CASE_				, SCREAMING_SNAKE_CASE_  )
							onnx.save(SCREAMING_SNAKE_CASE_				, SCREAMING_SNAKE_CASE_  )

							return new_model




"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase						=      {
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      ['''BlipImageProcessor''']


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      [
	    '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlipModel''',
	    '''BlipPreTrainedModel''',
	    '''BlipForConditionalGeneration''',
	    '''BlipForQuestionAnswering''',
	    '''BlipVisionModel''',
	    '''BlipTextModel''',
	    '''BlipForImageTextRetrieval''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      [
	    '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFBlipModel''',
	    '''TFBlipPreTrainedModel''',
	    '''TFBlipForConditionalGeneration''',
	    '''TFBlipForQuestionAnswering''',
	    '''TFBlipVisionModel''',
	    '''TFBlipTextModel''',
	    '''TFBlipForImageTextRetrieval''',
	]

if TYPE_CHECKING:
	from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
	from .processing_blip import BlipProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_blip import BlipImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blip import (
		    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlipForConditionalGeneration,
		    BlipForImageTextRetrieval,
		    BlipForQuestionAnswering,
		    BlipModel,
		    BlipPreTrainedModel,
		    BlipTextModel,
		    BlipVisionModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blip import (
		    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFBlipForConditionalGeneration,
		    TFBlipForImageTextRetrieval,
		    TFBlipForQuestionAnswering,
		    TFBlipModel,
		    TFBlipPreTrainedModel,
		    TFBlipTextModel,
		    TFBlipVisionModel,
		)

else:
	import sys

	__UpperCamelCase						=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


