
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 			__A( a			):
				snake_case_						   =    ['''image_processor''', '''tokenizer''']
				snake_case_						   =    '''BlipImageProcessor'''
				snake_case_						   =    ('''BertTokenizer''', '''BertTokenizerFast''')




				def __init__(     self		,     _snake_case		,     _snake_case				)    ->      Dict:


								'''simple docstring'''




								__a										=    False
								super().__init__(_snake_case		,     _snake_case				)
								__a										=    self.image_processor




				def __call__(     self		,     _snake_case = None		,     _snake_case = None		,     _snake_case = True		,     _snake_case = False		,     _snake_case = None		,     _snake_case = None		,     _snake_case = 0		,     _snake_case = None		,     _snake_case = None		,     _snake_case = False		,     _snake_case = False		,     _snake_case = False		,     _snake_case = False		,     _snake_case = False		,     _snake_case = True		,     _snake_case = None		,     **_snake_case		,     )    ->      BatchEncoding:


								'''simple docstring'''




								if images is None and text is None:
												raise ValueError('''You have to specify either images or text.'''				)

								# Get only text
								if images is None:
												__a										=    self.tokenizer
												__a										=    self.tokenizer(
												    text=_snake_case		,     add_special_tokens=_snake_case		,     padding=_snake_case		,     truncation=_snake_case		,     max_length=_snake_case		,     stride=_snake_case		,     pad_to_multiple_of=_snake_case		,     return_attention_mask=_snake_case		,     return_overflowing_tokens=_snake_case		,     return_special_tokens_mask=_snake_case		,     return_offsets_mapping=_snake_case		,     return_token_type_ids=_snake_case		,     return_length=_snake_case		,     verbose=_snake_case		,     return_tensors=_snake_case		,     **_snake_case		,     )
												return text_encoding

								# add pixel_values
								__a										=    self.image_processor(_snake_case		,     return_tensors=_snake_case				)

								if text is not None:
												__a										=    self.tokenizer(
												    text=_snake_case		,     add_special_tokens=_snake_case		,     padding=_snake_case		,     truncation=_snake_case		,     max_length=_snake_case		,     stride=_snake_case		,     pad_to_multiple_of=_snake_case		,     return_attention_mask=_snake_case		,     return_overflowing_tokens=_snake_case		,     return_special_tokens_mask=_snake_case		,     return_offsets_mapping=_snake_case		,     return_token_type_ids=_snake_case		,     return_length=_snake_case		,     verbose=_snake_case		,     return_tensors=_snake_case		,     **_snake_case		,     )
								else:
												__a										=    None

								if text_encoding is not None:
												encoding_image_processor.update(_snake_case				)

								return encoding_image_processor




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      int:


								'''simple docstring'''




								return self.tokenizer.batch_decode(*_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								return self.tokenizer.decode(*_snake_case		,     **_snake_case				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    self.tokenizer.model_input_names
								__a										=    self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)

import os

import numpy
import onnx







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     int       	=      a.name
       lowercase							:     Any       	=      b.name

       lowercase							:     Optional[Any]       	=      """"""
       lowercase							:     Dict       	=      """"""

       lowercase							:     int       	=      a == b

       lowercase							:     int       	=      name_a
       lowercase							:     List[str]       	=      name_b

       return res







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       for n in graph_proto.node:
              _node_replace_input_with(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     Any       	=      list(model.graph.initializer				)
       lowercase							:     Dict       	=      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

              lowercase							:     Union[str, Any]       	=      inits[i].name
              lowercase							:     Dict       	=      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 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     Union[str, Any]       	=      os.path.dirname(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Dict       	=      os.path.basename(SCREAMING_SNAKE_CASE__				)

       lowercase							:     str       	=      onnx.load(os.path.join(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)				)

       lowercase							:     List[str]       	=      list(model.graph.initializer				)

       lowercase							:     Tuple       	=      set()
       lowercase							:     int       	=      {}
       lowercase							:     Optional[Any]       	=      []

       lowercase							:     Dict       	=      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__				)

                            lowercase							:     int       	=      inits[j].data_type
                            lowercase							:     Optional[int]       	=      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

                            lowercase							:     Tuple       	=      inits[i].name
                            lowercase							:     int       	=      inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(SCREAMING_SNAKE_CASE__				)
                            else:
                                   lowercase							:     List[str]       	=      [name_j]
                            ind_to_replace.append((j, i)				)

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

       lowercase							:     str       	=      sorted(SCREAMING_SNAKE_CASE__				)
       _remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       lowercase							:     Optional[Any]       	=      """optimized_""" + model_file_name
       lowercase							:     Dict       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       onnx.save(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       return new_model
