






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			List[str]							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    tempfile.mkdtemp()

         _UpperCAmelCase							=    BlipImageProcessor()
         _UpperCAmelCase							=    GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""							)

         _UpperCAmelCase							=    BlipaProcessor(lowerCamelCase       ,				lowerCamelCase							)

         processor.save_pretrained(self.tmpdirname							)


  def  lowerCamelCase       (      self		:			Tuple       ,				**lowerCamelCase		:			Tuple							)						->	str:


         """simple docstring"""
         return AutoProcessor.from_pretrained(self.tmpdirname       ,				**lowerCamelCase							).tokenizer


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				**lowerCamelCase		:			Any							)						->	Optional[int]:


         """simple docstring"""
         return AutoProcessor.from_pretrained(self.tmpdirname       ,				**lowerCamelCase							).image_processor


  def  lowerCamelCase       (      self		:			Any							)						->	Union[str, Any]:


         """simple docstring"""
         shutil.rmtree(self.tmpdirname							)


  def  lowerCamelCase       (      self		:			Dict							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    [np.random.randint(255       ,				size=(3, 30, 400)       ,				dtype=np.uinta							)]

         _UpperCAmelCase							=    [Image.fromarray(np.moveaxis(lowerCamelCase       ,				0       ,				-1							)							) for x in image_inputs]

         return image_inputs


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    BlipaProcessor(tokenizer=self.get_tokenizer()       ,				image_processor=self.get_image_processor()							)
         processor.save_pretrained(self.tmpdirname							)

         _UpperCAmelCase							=    self.get_tokenizer(bos_token="""(BOS)"""       ,				eos_token="""(EOS)"""							)
         _UpperCAmelCase							=    self.get_image_processor(do_normalize=lowerCamelCase       ,				padding_value=1.0							)

         _UpperCAmelCase							=    BlipaProcessor.from_pretrained(
             self.tmpdirname       ,				bos_token="""(BOS)"""       ,				eos_token="""(EOS)"""       ,				do_normalize=lowerCamelCase       ,				padding_value=1.0							)

         self.assertEqual(processor.tokenizer.get_vocab()       ,				tokenizer_add_kwargs.get_vocab()							)
         self.assertIsInstance(processor.tokenizer       ,				lowerCamelCase							)

         self.assertEqual(processor.image_processor.to_json_string()       ,				image_processor_add_kwargs.to_json_string()							)
         self.assertIsInstance(processor.image_processor       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_image_processor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    BlipaProcessor(tokenizer=lowerCamelCase       ,				image_processor=lowerCamelCase							)

         _UpperCAmelCase							=    self.prepare_image_inputs()

         _UpperCAmelCase							=    image_processor(lowerCamelCase       ,				return_tensors="""np"""							)
         _UpperCAmelCase							=    processor(images=lowerCamelCase       ,				return_tensors="""np"""							)

         for key in input_feat_extract.keys():
                self.assertAlmostEqual(input_feat_extract[key].sum()       ,				input_processor[key].sum()       ,				delta=1E-2							)


  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_image_processor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    BlipaProcessor(tokenizer=lowerCamelCase       ,				image_processor=lowerCamelCase							)

         _UpperCAmelCase							=    """lower newer"""

         _UpperCAmelCase							=    processor(text=lowerCamelCase							)

         _UpperCAmelCase							=    tokenizer(lowerCamelCase       ,				return_token_type_ids=lowerCamelCase							)

         for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]       ,				encoded_processor[key]							)


  def  lowerCamelCase       (      self		:			Any							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_image_processor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    BlipaProcessor(tokenizer=lowerCamelCase       ,				image_processor=lowerCamelCase							)

         _UpperCAmelCase							=    """lower newer"""
         _UpperCAmelCase							=    self.prepare_image_inputs()

         _UpperCAmelCase							=    processor(text=lowerCamelCase       ,				images=lowerCamelCase							)

         self.assertListEqual(list(inputs.keys()							)       ,				["""pixel_values""", """input_ids""", """attention_mask"""]							)

         # test if it raises when no input is passed
         with pytest.raises(lowerCamelCase							):
                processor()


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_image_processor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    BlipaProcessor(tokenizer=lowerCamelCase       ,				image_processor=lowerCamelCase							)

         _UpperCAmelCase							=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         _UpperCAmelCase							=    processor.batch_decode(lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.batch_decode(lowerCamelCase							)

         self.assertListEqual(lowerCamelCase       ,				lowerCamelCase							)





  def  lowerCamelCase       (      self		:			Any							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_image_processor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    BlipaProcessor(tokenizer=lowerCamelCase       ,				image_processor=lowerCamelCase							)

         _UpperCAmelCase							=    """lower newer"""
         _UpperCAmelCase							=    self.prepare_image_inputs()

         _UpperCAmelCase							=    processor(text=lowerCamelCase       ,				images=lowerCamelCase							)

         # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
         self.assertListEqual(list(inputs.keys()							)       ,				["""pixel_values""", """input_ids""", """attention_mask"""]							)





'''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
A_			      =		"."

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






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      snake_case__       :   str	      =    SavedModel()
      snake_case__       :   Union[str, Any]	      =    []

      with open(os.path.join(__SCREAMING_SNAKE_CASE				,      'utils'				,      'tf_ops'				,      'onnx.json'		)		) as f:
            snake_case__       :   Optional[Any]	      =    json.load(__SCREAMING_SNAKE_CASE		)['opsets']

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

      with open(__SCREAMING_SNAKE_CASE				,      'rb'		) as f:
            saved_model.ParseFromString(f.read()		)

      snake_case__       :   List[str]	      =    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
      snake_case__       :   Optional[int]	      =    sorted(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Optional[Any]	      =    []

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

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


if __name__ == "__main__":
      A_			      =		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)"
      )
      A_			      =		parser.parse_args()

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