






'''simple docstring'''
import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase__ =			'.'

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
UpperCamelCase__ =			[
    'Assert',
    'AssignVariableOp',
    'EmptyTensorList',
    'MergeV2Checkpoints',
    'ReadVariableOp',
    'ResourceGather',
    'RestoreV2',
    'SaveV2',
    'ShardedFilename',
    'StatefulPartitionedCall',
    'StaticRegexFullMatch',
    'VarHandleOp',
]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     SavedModel()
   lowercase_	:    Union[str, Any]           =     []

   with open(os.path.join(_UpperCamelCase						,		"utils"						,		"tf_ops"						,		"onnx.json"							)							) as f:
      lowercase_	:    int           =     json.load(_UpperCamelCase							)["opsets"]

   for i in range(1						,		opset + 1							):
      onnx_ops.extend(onnx_opsets[str(_UpperCamelCase							)]							)

   with open(_UpperCamelCase						,		"rb"							) as f:
      saved_model.ParseFromString(f.read()							)

   lowercase_	:    Optional[int]           =     set()

   # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
   for meta_graph in saved_model.meta_graphs:
      # Add operations in the graph definition
      model_op_names.update(node.op for node in meta_graph.graph_def.node							)

      # Go through the functions in the graph definition
      for func in meta_graph.graph_def.library.function:
         # Add operations in each function
         model_op_names.update(node.op for node in func.node_def							)

    # Convert to list, sorted if you want
   lowercase_	:    Optional[int]           =     sorted(_UpperCamelCase							)
   lowercase_	:    str           =     []

   for op in model_op_names:
      if op not in onnx_ops and op not in INTERNAL_OPS:
         incompatible_ops.append(_UpperCamelCase							)

   if strict and len(_UpperCamelCase							) > 0:
      raise Exception(F"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops							)
   elif len(_UpperCamelCase							) > 0:
      print(F"""Found the following incompatible ops for the opset {opset}:"""							)
      print(*_UpperCamelCase						,		sep="\n"							)
   else:
      print(F"""The saved model {saved_model_path} can properly be converted with ONNX."""							)


if __name__ == "__main__":
 UpperCamelCase__ =			argparse.ArgumentParser()
 parser.add_argument('--saved_model_path', help='Path of the saved model to check (the .pb file).')
 parser.add_argument(
     '--opset', default=12, type=int, help='The ONNX opset against which the model has to be tested.'
 )
 parser.add_argument(
     '--framework', choices=['onnx'], default='onnx', help='Frameworks against which to test the saved model.'
 )
 parser.add_argument(
     '--strict', action='store_true', help='Whether make the checking strict (raise errors) or not (raise warnings)'
 )
 UpperCamelCase__ =			parser.parse_args()

 if args.framework == "onnx":
  onnx_compliancy(args.saved_model_path, args.strict, args.opset)








'''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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     []

   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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     []

   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        _UpperCAmelCase						:




    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






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

       lowercase_	:    Optional[Any]           =     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_	:    Any           =     self.tool.outputs
       for _output in outputs:
          self.assertTrue(_output in authorized_types			)




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     create_inputs(self.tool.inputs			)
       lowercase_	:    List[str]           =     self.tool(*a			)

       # There is a single output
       if len(self.tool.outputs			) == 1:
          lowercase_	:    Union[str, Any]           =     [outputs]

       self.assertListEqual(output_types(a			)  ,     self.tool.outputs			)




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''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 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    Any           =     create_inputs(self.tool.inputs			)
       lowercase_	:    str           =     self.tool(*a			)

       if not isinstance(a  ,     a			):
          lowercase_	:    List[Any]           =     [outputs]

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

       for output, output_type in zip(a  ,     self.tool.outputs			):
          lowercase_	:    int           =     AGENT_TYPE_MAPPING[output_type]
          self.assertTrue(isinstance(a  ,     a			)			)





    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    Dict           =     create_inputs(self.tool.inputs			)

       lowercase_	:    Optional[int]           =     []

       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_	:    Any           =     self.tool(*a			)

       if not isinstance(a  ,     a			):
          lowercase_	:    Any           =     [outputs]

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




