


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
lowerCAmelCase__      		= '.'

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
lowerCAmelCase__      		= [
    'Assert',
    'AssignVariableOp',
    'EmptyTensorList',
    'MergeV2Checkpoints',
    'ReadVariableOp',
    'ResourceGather',
    'RestoreV2',
    'SaveV2',
    'ShardedFilename',
    'StatefulPartitionedCall',
    'StaticRegexFullMatch',
    'VarHandleOp',
]
def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Tuple				,							UpperCamelCase__     :  int						):
       _A      :       Dict				    = SavedModel()
       _A      :       int				    = []

       with open(os.path.join(UpperCamelCase__				,							"utils"				,							"tf_ops"				,							"onnx.json"						)						) as f:
              _A      :       Optional[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()						)

       _A      :       List[Any]				    = 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
       _A      :       Any				    = sorted(UpperCamelCase__						)
       _A      :       Any				    = []

       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__":
    lowerCAmelCase__      		= 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)'
    )
    lowerCAmelCase__      		= parser.parse_args()

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




import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test







class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     @parameterized.expand([(None,), ("foo.json",)])
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				List[str]:
            _A      :       str				    = GenerationConfig(
                do_sample=__lowerCamelCase ,						temperature=0.7 ,						length_penalty=1.0 ,						bad_words_ids=[[1, 2, 3], [4, 5]] ,						)
            with tempfile.TemporaryDirectory() as tmp_dir:
                   config.save_pretrained(__lowerCamelCase ,						config_name=__lowerCamelCase)
                   _A      :       Tuple				    = GenerationConfig.from_pretrained(__lowerCamelCase ,						config_name=__lowerCamelCase)

            # Checks parameters that were specified
            self.assertEqual(loaded_config.do_sample ,						__lowerCamelCase)
            self.assertEqual(loaded_config.temperature ,						0.7)
            self.assertEqual(loaded_config.length_penalty ,						1.0)
            self.assertEqual(loaded_config.bad_words_ids ,						[[1, 2, 3], [4, 5]])

            # Checks parameters that were not specified (defaults)
            self.assertEqual(loaded_config.top_k ,						5_0)
            self.assertEqual(loaded_config.max_length ,						2_0)
            self.assertEqual(loaded_config.max_time ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       Optional[int]				    = AutoConfig.from_pretrained("gpt2")
            _A      :       int				    = GenerationConfig.from_model_config(__lowerCamelCase)
            _A      :       List[Any]				    = GenerationConfig()

            # The generation config has loaded a few non-default parameters from the model config
            self.assertNotEqual(__lowerCamelCase ,						__lowerCamelCase)

            # One of those parameters is eos_token_id -- check if it matches
            self.assertNotEqual(generation_config_from_model.eos_token_id ,						default_generation_config.eos_token_id)
            self.assertEqual(generation_config_from_model.eos_token_id ,						model_config.eos_token_id)


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       Optional[Any]				    = GenerationConfig()
            _A      :       List[Any]				    = {
                "max_new_tokens": 1_0_2_4,
                "foo": "bar",
            }
            _A      :       List[str]				    = copy.deepcopy(__lowerCamelCase)
            _A      :       int				    = generation_config.update(**__lowerCamelCase)

            # update_kwargs was not modified (no side effects)
            self.assertEqual(__lowerCamelCase ,						__lowerCamelCase)

            # update_kwargs was used to update the config on valid attributes
            self.assertEqual(generation_config.max_new_tokens ,						1_0_2_4)

            # `.update()` returns a dictionary of unused kwargs
            self.assertEqual(__lowerCamelCase ,						{"foo": "bar"})


     def  _lowerCamelCase (						self)			->				Any:
            _A      :       int				    = GenerationConfig()
            _A      :       int				    = "bar"

            with tempfile.TemporaryDirectory("test-generation-config") as tmp_dir:
                   generation_config.save_pretrained(__lowerCamelCase)

                   _A      :       Any				    = GenerationConfig.from_pretrained(__lowerCamelCase)
            # update_kwargs was used to update the config on valid attributes
            self.assertEqual(new_config.foo ,						"bar")

            _A      :       Optional[Any]				    = GenerationConfig.from_model_config(__lowerCamelCase)
            assert not hasattr(__lowerCamelCase ,						"foo")  # no new kwargs should be initialized if from config


     def  _lowerCamelCase (						self)			->				List[str]:
            _A      :       Union[str, Any]				    = GenerationConfig()
            self.assertEqual(default_config.temperature ,						1.0)
            self.assertEqual(default_config.do_sample ,						__lowerCamelCase)
            self.assertEqual(default_config.num_beams ,						1)

            _A      :       Optional[int]				    = GenerationConfig(
                do_sample=__lowerCamelCase ,						temperature=0.7 ,						length_penalty=1.0 ,						bad_words_ids=[[1, 2, 3], [4, 5]] ,						)
            self.assertEqual(config.temperature ,						0.7)
            self.assertEqual(config.do_sample ,						__lowerCamelCase)
            self.assertEqual(config.num_beams ,						1)

            with tempfile.TemporaryDirectory() as tmp_dir:
                   config.save_pretrained(__lowerCamelCase)
                   _A      :       Optional[int]				    = GenerationConfig.from_pretrained(__lowerCamelCase ,						temperature=1.0)

            self.assertEqual(loaded_config.temperature ,						1.0)
            self.assertEqual(loaded_config.do_sample ,						__lowerCamelCase)
            self.assertEqual(loaded_config.num_beams ,						1)  # default value







@is_staging_test
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     @classmethod
     def  _lowerCamelCase (						cls)			->				Optional[int]:
            _A      :       Dict				    = TOKEN
            HfFolder.save_token(__lowerCamelCase)


     @classmethod
     def  _lowerCamelCase (						cls)			->				List[Any]:
            try:
                   delete_repo(token=cls._token ,						repo_id="test-generation-config")
            except HTTPError:
                   pass

            try:
                   delete_repo(token=cls._token ,						repo_id="valid_org/test-generation-config-org")
            except HTTPError:
                   pass


     def  _lowerCamelCase (						self)			->				Any:
            _A      :       Optional[int]				    = GenerationConfig(
                do_sample=__lowerCamelCase ,						temperature=0.7 ,						length_penalty=1.0 ,						)
            config.push_to_hub("test-generation-config" ,						use_auth_token=self._token)

            _A      :       Union[str, Any]				    = GenerationConfig.from_pretrained(F"{USER}/test-generation-config")
            for k, v in config.to_dict().items():
                   if k != "transformers_version":
                          self.assertEqual(__lowerCamelCase ,						getattr(__lowerCamelCase ,						__lowerCamelCase))

        # Reset repo
            delete_repo(token=self._token ,						repo_id="test-generation-config")

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                   config.save_pretrained(
                       __lowerCamelCase ,						repo_id="test-generation-config" ,						push_to_hub=__lowerCamelCase ,						use_auth_token=self._token)

            _A      :       Optional[Any]				    = GenerationConfig.from_pretrained(F"{USER}/test-generation-config")
            for k, v in config.to_dict().items():
                   if k != "transformers_version":
                          self.assertEqual(__lowerCamelCase ,						getattr(__lowerCamelCase ,						__lowerCamelCase))


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Union[str, Any]				    = GenerationConfig(
                do_sample=__lowerCamelCase ,						temperature=0.7 ,						length_penalty=1.0 ,						)
            config.push_to_hub("valid_org/test-generation-config-org" ,						use_auth_token=self._token)

            _A      :       int				    = GenerationConfig.from_pretrained("valid_org/test-generation-config-org")
            for k, v in config.to_dict().items():
                   if k != "transformers_version":
                          self.assertEqual(__lowerCamelCase ,						getattr(__lowerCamelCase ,						__lowerCamelCase))

        # Reset repo
            delete_repo(token=self._token ,						repo_id="valid_org/test-generation-config-org")

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                   config.save_pretrained(
                       __lowerCamelCase ,						repo_id="valid_org/test-generation-config-org" ,						push_to_hub=__lowerCamelCase ,						use_auth_token=self._token)

            _A      :       Optional[int]				    = GenerationConfig.from_pretrained("valid_org/test-generation-config-org")
            for k, v in config.to_dict().items():
                   if k != "transformers_version":
                          self.assertEqual(__lowerCamelCase ,						getattr(__lowerCamelCase ,						__lowerCamelCase))
