






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    _lowerCAmelCase			:



				"""simple docstring"""



				def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    Dict			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    Union[str, Any]):






											'''simple docstring'''







											return None


class    _lowerCAmelCase			:



				"""simple docstring"""



				def 					__magic_name__							(      self    :    Dict			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    Dict			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    Union[str, Any]):






											'''simple docstring'''







											return None


class    _lowerCAmelCase			(		unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   str      =  [
				    # (model_name, model_kwargs)
				    ('''bert-base-cased''', {}),
				    ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
				]



				@require_tf
				@slow
				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																		self._test_export(UpperCamelCase__			,		"""tf"""			,		1_2			,		**UpperCamelCase__)



				@require_torch
				@slow
				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																		self._test_export(UpperCamelCase__			,		"""pt"""			,		1_2			,		**UpperCamelCase__)



				@require_torch
				@slow
				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											from transformers import BertModel

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

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



				@require_tf
				@slow
				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																		snake_case__	  =       self._test_export(UpperCamelCase__			,		"""tf"""			,		1_2			,		**UpperCamelCase__)
																		snake_case__	  =       quantize(Path(UpperCamelCase__))

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



				@require_torch
				@slow
				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																		snake_case__	  =       self._test_export(UpperCamelCase__			,		"""pt"""			,		1_2			,		**UpperCamelCase__)
																		snake_case__	  =       quantize(UpperCamelCase__)

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



				def 					__magic_name__							(      self    :    Optional[int]			,		UpperCamelCase__    :    Dict			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Tuple			,		UpperCamelCase__    :    str=None			,		**UpperCamelCase__    :    List[str]):






											'''simple docstring'''







											try:
																		# Compute path
																		with TemporaryDirectory() as tempdir:
																									snake_case__	  =       Path(UpperCamelCase__).joinpath("""model.onnx""")

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

																		# Export
																		convert(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		**UpperCamelCase__)

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



				@require_torch
				@require_tokenizers
				@slow
				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											from transformers import BertModel

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



				@require_tf
				@require_tokenizers
				@slow
				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											from transformers import TFBertModel

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



				def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Dict):






											'''simple docstring'''







											snake_case__	  =       FeatureExtractionPipeline(UpperCamelCase__			,		UpperCamelCase__)

											snake_case__	  =       ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
											snake_case__  , snake_case__  , snake_case__  , snake_case__	  =       infer_shapes(UpperCamelCase__			,		UpperCamelCase__)

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

											# 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 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       ["""input_ids""", """attention_mask""", """token_type_ids"""]
											snake_case__	  =       {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
											snake_case__  , snake_case__	  =       ensure_valid_input(FuncContiguousArgs()			,		UpperCamelCase__			,		UpperCamelCase__)

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

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

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

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

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

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



				def 					__magic_name__							(      self    :    List[Any]):






											'''simple docstring'''







											snake_case__	  =       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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__       =     {
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """AutoformerForPrediction""",
	    """AutoformerModel""",
	    """AutoformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_autoformer import (
	    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AutoformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_autoformer import (
		    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AutoformerForPrediction,
		    AutoformerModel,
		    AutoformerPreTrainedModel,
		)

else:
	import sys

	a__       =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
