


"""simple docstring"""

from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image


UpperCAmelCase__  							=["text", "image", "audio"]


def lowerCAmelCase_      (				UpperCamelCase__	: List[str] ):






 """simple docstring"""







 __lowercase									=	[]

 for input_type in input_types:
  if input_type == "text":
   inputs.append("""Text input""" )
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO""" ) ) / """000000039769.png""" ).resize((512, 512) ) )
  elif input_type == "audio":
   inputs.append(torch.ones(3000 ) )
  elif isinstance(UpperCamelCase__				,   UpperCamelCase__ ):
   inputs.append(create_inputs(UpperCamelCase__ ) )
  else:
   raise ValueError(f'''Invalid type requested: {input_type}''' )

 return inputs


def lowerCAmelCase_      (				UpperCamelCase__	: List ):






 """simple docstring"""







 __lowercase									=	[]

 for output in outputs:
  if isinstance(UpperCamelCase__				,   (str, AgentText) ):
   output_types.append("""text""" )
  elif isinstance(UpperCamelCase__				,   (Image.Image, AgentImage) ):
   output_types.append("""image""" )
  elif isinstance(UpperCamelCase__				,   (torch.Tensor, AgentAudio) ):
   output_types.append("""audio""" )
  else:
   raise ValueError(f'''Invalid output: {output}''' )

 return output_types





@is_tool_test
class    lowerCamelCase__						:
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       self.assertTrue(hasattr(self.tool      ,       """inputs"""							)							)
       self.assertTrue(hasattr(self.tool      ,       """outputs"""							)							)

       __lowercase									=	self.tool.inputs
       for _input in inputs:
        if isinstance(_input      ,       A_							):
         for __input in _input:
          self.assertTrue(__input in authorized_types							)
        else:
         self.assertTrue(_input in authorized_types							)

       __lowercase									=	self.tool.outputs
       for _output in outputs:
        self.assertTrue(_output in authorized_types							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	create_inputs(self.tool.inputs							)
       __lowercase									=	self.tool(*A_							)

       # There is a single output
       if len(self.tool.outputs							) == 1:
        __lowercase									=	[outputs]

       self.assertListEqual(output_types(A_							)      ,       self.tool.outputs							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       self.assertTrue(hasattr(self.tool      ,       """description"""							)							)
       self.assertTrue(hasattr(self.tool      ,       """default_checkpoint"""							)							)
       self.assertTrue(self.tool.description.startswith("""This is a tool that"""							)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	create_inputs(self.tool.inputs							)
       __lowercase									=	self.tool(*A_							)

       if not isinstance(A_      ,       A_							):
        __lowercase									=	[outputs]

       self.assertEqual(len(A_							)      ,       len(self.tool.outputs							)							)

       for output, output_type in zip(A_      ,       self.tool.outputs							):
        __lowercase									=	AGENT_TYPE_MAPPING[output_type]
        self.assertTrue(isinstance(A_      ,       A_							)							)






      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	create_inputs(self.tool.inputs							)

       __lowercase									=	[]

       for _input, input_type in zip(A_      ,       self.tool.inputs							):
        if isinstance(A_      ,       A_							):
         _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input							) for _input_type in input_type]							)
        else:
         _inputs.append(AGENT_TYPE_MAPPING[input_type](_input							)							)

        # Should not raise an error
       __lowercase									=	self.tool(*A_							)

       if not isinstance(A_      ,       A_							):
        __lowercase									=	[outputs]

       self.assertEqual(len(A_							)      ,       len(self.tool.outputs							)							)




"""simple docstring"""

import os

import numpy
import onnx


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: List[str] ):






 """simple docstring"""







 __lowercase									=	a.name
 __lowercase									=	b.name

 __lowercase									=	""""""
 __lowercase									=	""""""

 __lowercase									=	a == b

 __lowercase									=	name_a
 __lowercase									=	name_b

 return res


def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Dict ):






 """simple docstring"""







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

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


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: str ):






 """simple docstring"""







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


def lowerCAmelCase_      (				UpperCamelCase__	: Tuple				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Any ):






 """simple docstring"""







 __lowercase									=	list(model.graph.initializer )
 __lowercase									=	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									=	inits[i].name
  __lowercase									=	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				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: List[Any] ):






 """simple docstring"""







 __lowercase									=	os.path.dirname(UpperCamelCase__ )
 __lowercase									=	os.path.basename(UpperCamelCase__ )

 __lowercase									=	onnx.load(os.path.join(UpperCamelCase__				,   UpperCamelCase__ ) )

 __lowercase									=	list(model.graph.initializer )

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

 __lowercase									=	0

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

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

    __lowercase									=	inits[j].data_type
    __lowercase									=	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: """				,   UpperCamelCase__ )
    total_reduced_size += mem_size

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

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

 print("""total reduced size: """				,   total_reduced_size / 1024 / 1024 / 1024				,   """GB""" )

 __lowercase									=	sorted(UpperCamelCase__ )
 _remove_dup_initializers_from_model(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )

 __lowercase									=	"""optimized_""" + model_file_name
 __lowercase									=	os.path.join(UpperCamelCase__				,   UpperCamelCase__ )
 onnx.save(UpperCamelCase__				,   UpperCamelCase__ )

 return new_model
