






"""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						:






    def    UpperCamelCase       (       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							str					)     ->		List[Any]:
      return None






class      a						:






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		Union[str, Any]:
      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     :							Union[str, Any]					)     ->		List[str]:
      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     :							Optional[int]					)     ->		int:
      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     :							int					)     ->		List[Any]:
      from transformers import BertModel

      lowerCamelCase_         =						['[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()
        lowerCamelCase_         =						BertTokenizerFast(vocab_file.name					)

      with TemporaryDirectory() as bert_save_dir:
        lowerCamelCase_         =						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     :							Optional[Any]					)     ->		List[str]:
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
        lowerCamelCase_         =						self._test_export(__SCREAMING_SNAKE_CASE       ,						'tf'       ,						12       ,						**__SCREAMING_SNAKE_CASE					)
        lowerCamelCase_         =						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     :							Tuple					)     ->		int:
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
        lowerCamelCase_         =						self._test_export(__SCREAMING_SNAKE_CASE       ,						'pt'       ,						12       ,						**__SCREAMING_SNAKE_CASE					)
        lowerCamelCase_         =						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     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=None       ,						**__SCREAMING_SNAKE_CASE     :							Union[str, Any]					)     ->		Dict:
      try:
        # Compute path
        with TemporaryDirectory() as tempdir:
          lowerCamelCase_         =						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     :							Dict					)     ->		Dict:
      from transformers import BertModel

      lowerCamelCase_         =						BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'					)					)
      lowerCamelCase_         =						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     :							Tuple					)     ->		Union[str, Any]:
      from transformers import TFBertModel

      lowerCamelCase_         =						TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'					)					)
      lowerCamelCase_         =						BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'					)
      self._test_infer_dynamic_axis(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						'tf'					)






    def    UpperCamelCase       (       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		Optional[int]:
      lowerCamelCase_         =						FeatureExtractionPipeline(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
      lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_         =						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     :							List[Any]					)     ->		Optional[Any]:
      lowerCamelCase_         =						['input_ids', 'attention_mask', 'token_type_ids']
      lowerCamelCase_         =						{'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
      lowerCamelCase_     ,	lowerCamelCase_         =						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)
      lowerCamelCase_     ,	lowerCamelCase_         =						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     :							int					)     ->		List[Any]:
      lowerCamelCase_         =						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"""





import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_SCREAMING_SNAKE_CASE					:				Any					    =						importlib.util.find_spec('''s3fs''') is not None

if _has_safs:
  from .safilesystem import SaFileSystem  # noqa: F401

_SCREAMING_SNAKE_CASE					:				List[compression.BaseCompressedFileFileSystem]					    =						[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
  if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
    warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
  fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				str:
  if "://" in dataset_path:
    lowerCamelCase_         =						dataset_path.split('://' )[1]
  return dataset_path







def      lowerCamelCase__       (							_lowerCamelCase       :		fsspec.AbstractFileSystem )      ->				bool:
  if fs is not None and fs.protocol != "file":
    return True
  else:
    return False







def      lowerCamelCase__       (							_lowerCamelCase       :		fsspec.AbstractFileSystem		,				_lowerCamelCase       :		str		,				_lowerCamelCase       :		str )      ->				int:
  lowerCamelCase_         =						not is_remote_filesystem(_lowerCamelCase )
  if is_local:
    # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
    shutil.move(fs._strip_protocol(_lowerCamelCase )		,				fs._strip_protocol(_lowerCamelCase ) )
  else:
    fs.mv(_lowerCamelCase		,				_lowerCamelCase		,				recursive=_lowerCamelCase )







def      lowerCamelCase__       (							)      ->				None:
  if hasattr(fsspec.asyn		,				'reset_lock' ):
    # for future fsspec>2022.05.0
    fsspec.asyn.reset_lock()
  else:
    lowerCamelCase_         =						None
    lowerCamelCase_         =						None
    lowerCamelCase_         =						threading.Lock()




