






'''simple docstring'''

import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow




class      A     :






 """simple docstring"""





 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       return None






class      A     :






 """simple docstring"""





 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       return None






class      A     (   unittest.TestCase    ):






 """simple docstring"""





 __a      :    Any			  =    [
     # (model_name, model_kwargs)
     ('''bert-base-cased''', {}),
     ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
 ]
 @require_tf
 @slow
 def 	_UpperCAmelCase							(				self			):







       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             self._test_export(SCREAMING_SNAKE_CASE_      ,  """tf"""      ,  12      ,  **SCREAMING_SNAKE_CASE_			)
 @require_torch
 @slow
 def 	_UpperCAmelCase							(				self			):







       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             self._test_export(SCREAMING_SNAKE_CASE_      ,  """pt"""      ,  12      ,  **SCREAMING_SNAKE_CASE_			)
 @require_torch
 @slow
 def 	_UpperCAmelCase							(				self			):
       from transformers import BertModel

       UpperCamelCase_					: Union[str, Any]            =				["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
       with NamedTemporaryFile(mode="""w+t"""			) as vocab_file:
             vocab_file.write("""\n""".join(SCREAMING_SNAKE_CASE_			)			)
             vocab_file.flush()
             UpperCamelCase_					: int            =				BertTokenizerFast(vocab_file.name			)

       with TemporaryDirectory() as bert_save_dir:
             UpperCamelCase_					: Tuple            =				BertModel(BertConfig(vocab_size=len(SCREAMING_SNAKE_CASE_			)			)			)
             model.save_pretrained(SCREAMING_SNAKE_CASE_			)
             self._test_export(SCREAMING_SNAKE_CASE_      ,  """pt"""      ,  12      ,  SCREAMING_SNAKE_CASE_			)
 @require_tf
 @slow
 def 	_UpperCAmelCase							(				self			):







       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             UpperCamelCase_					: List[str]            =				self._test_export(SCREAMING_SNAKE_CASE_      ,  """tf"""      ,  12      ,  **SCREAMING_SNAKE_CASE_			)
             UpperCamelCase_					: str            =				quantize(Path(SCREAMING_SNAKE_CASE_			)			)

             # Ensure the actual quantized model is not bigger than the original one
             if quantized_path.stat().st_size >= Path(SCREAMING_SNAKE_CASE_			).stat().st_size:
                   self.fail("""Quantized model is bigger than initial ONNX model"""			)
 @require_torch
 @slow
 def 	_UpperCAmelCase							(				self			):







       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             UpperCamelCase_					: Dict            =				self._test_export(SCREAMING_SNAKE_CASE_      ,  """pt"""      ,  12      ,  **SCREAMING_SNAKE_CASE_			)
             UpperCamelCase_					: List[str]            =				quantize(SCREAMING_SNAKE_CASE_			)

             # Ensure the actual quantized model is not bigger than the original one
             if quantized_path.stat().st_size >= Path(SCREAMING_SNAKE_CASE_			).stat().st_size:
                   self.fail("""Quantized model is bigger than initial ONNX model"""			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase=None      ,  **__lowerCAmelCase			):







       try:
             # Compute path
             with TemporaryDirectory() as tempdir:
                   UpperCamelCase_					: List[str]            =				Path(SCREAMING_SNAKE_CASE_			).joinpath("""model.onnx"""			)

             # Remove folder if exists
             if path.parent.exists():
                   path.parent.rmdir()

             # Export
             convert(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  **SCREAMING_SNAKE_CASE_			)

             return path
       except Exception as e:
             self.fail(SCREAMING_SNAKE_CASE_			)
 @require_torch
 @require_tokenizers
 @slow
 def 	_UpperCAmelCase							(				self			):
       from transformers import BertModel

       UpperCamelCase_					: Tuple            =				BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""			)			)
       UpperCamelCase_					: Optional[int]            =				BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""			)
       self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  """pt"""			)
 @require_tf
 @require_tokenizers
 @slow
 def 	_UpperCAmelCase							(				self			):
       from transformers import TFBertModel

       UpperCamelCase_					: Optional[int]            =				TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""			)			)
       UpperCamelCase_					: Optional[Any]            =				BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""			)
       self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  """tf"""			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: int            =				FeatureExtractionPipeline(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_			)

       UpperCamelCase_					: Dict            =				["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
       UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Union[str, Any]            =				infer_shapes(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_			)

       # Assert all variables are present
       self.assertEqual(len(SCREAMING_SNAKE_CASE_			)      ,  len(SCREAMING_SNAKE_CASE_			)			)
       self.assertTrue(all(var_name in shapes for var_name in variable_names			)			)
       self.assertSequenceEqual(variable_names[:3]      ,  SCREAMING_SNAKE_CASE_			)
       self.assertSequenceEqual(variable_names[3:]      ,  SCREAMING_SNAKE_CASE_			)

       # Assert inputs are {0: batch, 1: sequence}
       for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
             self.assertDictEqual(shapes[var_name]      ,  {0: """batch""", 1: """sequence"""}			)

       # Assert outputs are {0: batch, 1: sequence} and {0: batch}
       self.assertDictEqual(shapes["""output_0"""]      ,  {0: """batch""", 1: """sequence"""}			)
       self.assertDictEqual(shapes["""output_1"""]      ,  {0: """batch"""}			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Any            =				["""input_ids""", """attention_mask""", """token_type_ids"""]
       UpperCamelCase_					: int            =				{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
       UpperCamelCase_     ,      UpperCamelCase_					: Any            =				ensure_valid_input(FuncContiguousArgs()      ,  SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_			)

       # Should have exactly the same number of args (all are valid)
       self.assertEqual(len(SCREAMING_SNAKE_CASE_			)      ,  3			)

       # Should have exactly the same input names
       self.assertEqual(set(SCREAMING_SNAKE_CASE_			)      ,  set(SCREAMING_SNAKE_CASE_			)			)

       # Parameter should be reordered according to their respective place in the function:
       # (input_ids, token_type_ids, attention_mask)
       self.assertEqual(SCREAMING_SNAKE_CASE_      ,  (tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""])			)

       # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
       UpperCamelCase_     ,      UpperCamelCase_					: Dict            =				ensure_valid_input(FuncNonContiguousArgs()      ,  SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_			)

       # Should have exactly the one arg (all before the one not provided "some_other_args")
       self.assertEqual(len(SCREAMING_SNAKE_CASE_			)      ,  1			)
       self.assertEqual(len(SCREAMING_SNAKE_CASE_			)      ,  1			)

       # Should have only "input_ids"
       self.assertEqual(inputs_args[0]      ,  tokens["""input_ids"""]			)
       self.assertEqual(ordered_input_names[0]      ,  """input_ids"""			)





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""			)      ,  """-test"""			)
       self.assertEqual("""/home/something/my_fake_model-test.onnx"""      ,  generated.as_posix()			)








"""simple docstring"""




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async



def 							_lowerCamelCase			(			__a=None     ):
		if subparsers is not None:
				SCREAMING_SNAKE_CASE_    			=			subparsers.add_parser('''test'''     )
		else:
				SCREAMING_SNAKE_CASE_    			=			argparse.ArgumentParser('''Accelerate test command'''     )

		parser.add_argument(
		    '''--config_file''',				default=__a,				help=(
		        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
		        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
		        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
		        '''with \'huggingface\'.'''
		    ),				)

		if subparsers is not None:
				parser.set_defaults(func=__a     )
		return parser



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			os.path.sep.join(__file__.split(os.path.sep     )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']     )

		if args.config_file is None:
				SCREAMING_SNAKE_CASE_    			=			script_name
		else:
				SCREAMING_SNAKE_CASE_    			=			F'--config_file={args.config_file} {script_name}'

		SCREAMING_SNAKE_CASE_    			=			['''accelerate-launch'''] + test_args.split()
		SCREAMING_SNAKE_CASE_    			=			execute_subprocess_async(__a,				env=os.environ.copy()     )
		if result.returncode == 0:
				print('''Test is a success! You are ready for your distributed training!'''     )



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			test_command_parser()
		SCREAMING_SNAKE_CASE_    			=			parser.parse_args()
		test_command(__a     )


if __name__ == "__main__":
						main()