

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		:

  def 			UpperCAmelCase     (					self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        return None

class 			_A		:

  def 			UpperCAmelCase     (					self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        return None

class 			_A		(	unittest.TestCase):
  SCREAMING_SNAKE_CASE     :   Dict							=	[
      # (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       ):





        """simple docstring"""







        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       ):





        """simple docstring"""







        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       ):





        """simple docstring"""







        from transformers import BertModel

        SCREAMING_SNAKE_CASE_					:	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()
              SCREAMING_SNAKE_CASE_					:	List[str]     							=  BertTokenizerFast(vocab_file.name       )

        with TemporaryDirectory() as bert_save_dir:
              SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  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       ):





        """simple docstring"""







        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              SCREAMING_SNAKE_CASE_					:	Dict     							=  self._test_export(_SCREAMING_SNAKE_CASE			,       'tf'			,       12			,       **_SCREAMING_SNAKE_CASE       )
              SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  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       ):





        """simple docstring"""







        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              SCREAMING_SNAKE_CASE_					:	List[Any]     							=  self._test_export(_SCREAMING_SNAKE_CASE			,       'pt'			,       12			,       **_SCREAMING_SNAKE_CASE       )
              SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  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			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None			,       **_SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        try:
              # Compute path
              with TemporaryDirectory() as tempdir:
                    SCREAMING_SNAKE_CASE_					:	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       ):





        """simple docstring"""







        from transformers import BertModel

        SCREAMING_SNAKE_CASE_					:	List[str]     							=  BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'       )       )
        SCREAMING_SNAKE_CASE_					:	Any     							=  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       ):





        """simple docstring"""







        from transformers import TFBertModel

        SCREAMING_SNAKE_CASE_					:	int     							=  TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'       )       )
        SCREAMING_SNAKE_CASE_					:	Dict     							=  BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'       )
        self._test_infer_dynamic_axis(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       'tf'       )

  def 			UpperCAmelCase     (					self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Tuple     							=  FeatureExtractionPipeline(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE       )

        SCREAMING_SNAKE_CASE_					:	List[str]     							=  ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
        SCREAMING_SNAKE_CASE_					:	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       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  ["""input_ids""", """attention_mask""", """token_type_ids"""]
        SCREAMING_SNAKE_CASE_					:	Any     							=  {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
        SCREAMING_SNAKE_CASE_					:	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)
        SCREAMING_SNAKE_CASE_					:	Any     							=  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       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  generate_identified_filename(Path('/home/something/my_fake_model.onnx'       )			,       '-test'       )
        self.assertEqual('/home/something/my_fake_model-test.onnx'			,       generated.as_posix()       )












import re
from pathlib import Path
from unittest import TestCase

import pytest


@pytest.mark.integration
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						str	)	->       List[Any]:







						"""simple docstring"""







						with open(lowerCAmelCase					,     encoding="""utf-8"""	) as input_file:
											__UpperCamelCase					:	Union[str, Any]             =      re.compile(R"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"""	)
											__UpperCamelCase					:	str             =      input_file.read()
											__UpperCamelCase					:	Optional[int]             =      regexp.search(lowerCAmelCase	)

						return match






	def 					lowerCamelCase__     (					self		:						Optional[Any]					,     lowerCAmelCase		:						str	)	->       Union[str, Any]:







						"""simple docstring"""







						with open(lowerCAmelCase					,     encoding="""utf-8"""	) as input_file:
											__UpperCamelCase					:	Dict             =      re.compile(R"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"""					,     re.DOTALL	)
											__UpperCamelCase					:	str             =      input_file.read()
											# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
											__UpperCamelCase					:	List[Any]             =      regexp.finditer(lowerCAmelCase	)

						__UpperCamelCase					:	int             =      [match for match in matches if match is not None and match.group(1	) is not None]
						return matches[0] if matches else None






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







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      Path("""./datasets"""	)
						__UpperCamelCase					:	Optional[int]             =      list(dataset_paths.absolute().glob("""**/*.py"""	)	)

						for dataset in dataset_files:
											if self._no_encoding_on_file_open(str(lowerCAmelCase	)	):
																raise AssertionError(F'''open(...) must use utf-8 encoding in {dataset}'''	)






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       Any:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      Path("""./datasets"""	)
						__UpperCamelCase					:	Optional[int]             =      list(dataset_paths.absolute().glob("""**/*.py"""	)	)

						for dataset in dataset_files:
											if self._no_print_statements(str(lowerCAmelCase	)	):
																raise AssertionError(F'''print statement found in {dataset}. Use datasets.logger/logging instead.'''	)






