

'''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 __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_			)			->							Optional[int]:
										return None



class        A  :





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_			)			->							Optional[Any]:
										return None



class        A  (	unittest.TestCase ):
						__UpperCAmelCase				:	Union[str, 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 __lowerCAmelCase   (	self			)			->							Dict:
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
														self._test_export(__A	,    "tf"	,    1_2	,    **__A			)





						@require_torch
						@slow
						def __lowerCAmelCase   (	self			)			->							Union[str, Any]:
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
														self._test_export(__A	,    "pt"	,    1_2	,    **__A			)





						@require_torch
						@slow
						def __lowerCAmelCase   (	self			)			->							Optional[Any]:
										from transformers import BertModel

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

										with TemporaryDirectory() as bert_save_dir:
														_a        =   BertModel(BertConfig(vocab_size=len(__A			)			)			)
														model.save_pretrained(__A			)
														self._test_export(__A	,    "pt"	,    1_2	,    __A			)





						@require_tf
						@slow
						def __lowerCAmelCase   (	self			)			->							List[Any]:
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
														_a        =   self._test_export(__A	,    "tf"	,    1_2	,    **__A			)
														_a        =   quantize(Path(__A			)			)

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





						@require_torch
						@slow
						def __lowerCAmelCase   (	self			)			->							Any:
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
														_a        =   self._test_export(__A	,    "pt"	,    1_2	,    **__A			)
														_a        =   quantize(__A			)

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





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_=None	,    **snake_case_			)			->							Any:
										try:
														# Compute path
														with TemporaryDirectory() as tempdir:
																		_a        =   Path(__A			).joinpath("model.onnx"			)

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

														# Export
														convert(__A	,    __A	,    __A	,    __A	,    __A	,    **__A			)

														return path
										except Exception as e:
														self.fail(__A			)





						@require_torch
						@require_tokenizers
						@slow
						def __lowerCAmelCase   (	self			)			->							Optional[int]:
										from transformers import BertModel

										_a        =   BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
										_a        =   BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
										self._test_infer_dynamic_axis(__A	,    __A	,    "pt"			)





						@require_tf
						@require_tokenizers
						@slow
						def __lowerCAmelCase   (	self			)			->							Dict:
										from transformers import TFBertModel

										_a        =   TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
										_a        =   BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
										self._test_infer_dynamic_axis(__A	,    __A	,    "tf"			)





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_			)			->							str:
										_a        =   FeatureExtractionPipeline(__A	,    __A			)

										_a        =   ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
										_a      ,     _a      ,     _a      ,     _a        =   infer_shapes(__A	,    __A			)

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

										# 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 __lowerCAmelCase   (	self			)			->							str:
										_a        =   ["input_ids", "attention_mask", "token_type_ids"]
										_a        =   {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
										_a      ,     _a        =   ensure_valid_input(FuncContiguousArgs()	,    __A	,    __A			)

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

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

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

										# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
										_a      ,     _a        =   ensure_valid_input(FuncNonContiguousArgs()	,    __A	,    __A			)

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

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





						def __lowerCAmelCase   (	self			)			->							Tuple:
										_a        =   generate_identified_filename(Path("/home/something/my_fake_model.onnx"			)	,    "-test"			)
										self.assertEqual("/home/something/my_fake_model-test.onnx"	,    generated.as_posix()			)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE							    =							{'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'WavLMForAudioFrameClassification',
      'WavLMForCTC',
      'WavLMForSequenceClassification',
      'WavLMForXVector',
      'WavLMModel',
      'WavLMPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE							    =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



