






"""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  :      List[str]     ,		__snake_case  :      List[str]     ,		__snake_case  :      Any     ,		__snake_case  :      Any					)			->							Tuple:
											return None



class       __A		:

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      int     ,		__snake_case  :      str					)			->							str:
											return None



class       __A		(   unittest.TestCase			):
					UpperCAmelCase__								=				[
					    # (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  :      Union[str, Any]					)			->							Dict:
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	self._test_export(__snake_case     ,		"""tf"""     ,		1_2     ,		**__snake_case					)

					@require_torch
					@slow
					def   lowerCamelCase__   (			self  :      List[str]					)			->							int:
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	self._test_export(__snake_case     ,		"""pt"""     ,		1_2     ,		**__snake_case					)

					@require_torch
					@slow
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							List[str]:
											from transformers import BertModel

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

											with TemporaryDirectory() as bert_save_dir:
																	__magic_name__:	Optional[int]							=       BertModel(BertConfig(vocab_size=len(__snake_case					)					)					)
																	model.save_pretrained(__snake_case					)
																	self._test_export(__snake_case     ,		"""pt"""     ,		1_2     ,		__snake_case					)

					@require_tf
					@slow
					def   lowerCamelCase__   (			self  :      Tuple					)			->							List[str]:
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	__magic_name__:	int							=       self._test_export(__snake_case     ,		"""tf"""     ,		1_2     ,		**__snake_case					)
																	__magic_name__:	List[str]							=       quantize(Path(__snake_case					)					)

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

					@require_torch
					@slow
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							List[Any]:
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	__magic_name__:	Dict							=       self._test_export(__snake_case     ,		"""pt"""     ,		1_2     ,		**__snake_case					)
																	__magic_name__:	Tuple							=       quantize(__snake_case					)

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

					def   lowerCamelCase__   (			self  :      Tuple     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Any     ,		__snake_case  :      Tuple=None     ,		**__snake_case  :      int					)			->							Dict:
											try:
																	# Compute path
																	with TemporaryDirectory() as tempdir:
																							__magic_name__:	Tuple							=       Path(__snake_case					).joinpath("""model.onnx"""					)

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

																	# Export
																	convert(__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		**__snake_case					)

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

					@require_torch
					@require_tokenizers
					@slow
					def   lowerCamelCase__   (			self  :      List[str]					)			->							Dict:
											from transformers import BertModel

											__magic_name__:	str							=       BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""					)					)
											__magic_name__:	int							=       BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""					)
											self._test_infer_dynamic_axis(__snake_case     ,		__snake_case     ,		"""pt"""					)

					@require_tf
					@require_tokenizers
					@slow
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[str]:
											from transformers import TFBertModel

											__magic_name__:	str							=       TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""					)					)
											__magic_name__:	List[Any]							=       BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""					)
											self._test_infer_dynamic_axis(__snake_case     ,		__snake_case     ,		"""tf"""					)

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      int     ,		__snake_case  :      int					)			->							int:
											__magic_name__:	List[Any]							=       FeatureExtractionPipeline(__snake_case     ,		__snake_case					)

											__magic_name__:	Optional[int]							=       ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	List[str]							=       infer_shapes(__snake_case     ,		__snake_case					)

											# Assert all variables are present
											self.assertEqual(len(__snake_case					)     ,		len(__snake_case					)					)
											self.assertTrue(all(var_name in shapes for var_name in variable_names					)					)
											self.assertSequenceEqual(variable_names[:3]     ,		__snake_case					)
											self.assertSequenceEqual(variable_names[3:]     ,		__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   lowerCamelCase__   (			self  :      int					)			->							List[str]:
											__magic_name__:	int							=       ["""input_ids""", """attention_mask""", """token_type_ids"""]
											__magic_name__:	str							=       {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
											__magic_name__,			__magic_name__:	int							=       ensure_valid_input(FuncContiguousArgs()     ,		__snake_case     ,		__snake_case					)

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

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

											# Parameter should be reordered according to their respective place in the function:
											# (input_ids, token_type_ids, attention_mask)
											self.assertEqual(__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)
											__magic_name__,			__magic_name__:	List[Any]							=       ensure_valid_input(FuncNonContiguousArgs()     ,		__snake_case     ,		__snake_case					)

											# Should have exactly the one arg (all before the one not provided "some_other_args")
											self.assertEqual(len(__snake_case					)     ,		1					)
											self.assertEqual(len(__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   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[str]:
											__magic_name__:	Optional[int]							=       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'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "shi-labs/dinat-mini-in1k-224": "https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}







class        UpperCAmelCase_			(			_a						,						_a      ):
    """simple docstring"""







    lowercase   =						"dinat"

    lowercase   =						{
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }




    def __init__(       self   :	Optional[int]					,	snake_case_   :	Optional[Any]=4					,	snake_case_   :	List[str]=3					,	snake_case_   :	Tuple=64					,	snake_case_   :	List[Any]=[3, 4, 6, 5]					,	snake_case_   :	List[Any]=[2, 4, 8, 16]					,	snake_case_   :	List[str]=7					,	snake_case_   :	List[str]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]					,	snake_case_   :	List[Any]=3.0					,	snake_case_   :	Dict=True					,	snake_case_   :	int=0.0					,	snake_case_   :	Optional[int]=0.0					,	snake_case_   :	str=0.1					,	snake_case_   :	Optional[Any]="gelu"					,	snake_case_   :	Union[str, Any]=0.02					,	snake_case_   :	Optional[int]=1E-5					,	snake_case_   :	str=0.0					,	snake_case_   :	Dict=None					,	snake_case_   :	Dict=None					,	**snake_case_   :	Dict					,	):
          super().__init__(**snake_case_		)

          snake_case__    :       Optional[Any]       		= patch_size
          snake_case__    :       Union[str, Any]       		= num_channels
          snake_case__    :       int       		= embed_dim
          snake_case__    :       Dict       		= depths
          snake_case__    :       Dict       		= len(snake_case_		)
          snake_case__    :       Optional[int]       		= num_heads
          snake_case__    :       Any       		= kernel_size
          snake_case__    :       str       		= dilations
          snake_case__    :       Any       		= mlp_ratio
          snake_case__    :       int       		= qkv_bias
          snake_case__    :       int       		= hidden_dropout_prob
          snake_case__    :       int       		= attention_probs_dropout_prob
          snake_case__    :       Optional[Any]       		= drop_path_rate
          snake_case__    :       int       		= hidden_act
          snake_case__    :       Tuple       		= layer_norm_eps
          snake_case__    :       Any       		= initializer_range
          # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          snake_case__    :       Optional[Any]       		= int(embed_dim * 2 ** (len(snake_case_		) - 1)		)
          snake_case__    :       int       		= layer_scale_init_value
          snake_case__    :       Dict       		= ["""stem"""] + [f"stage{idx}" for idx in range(1					,	len(snake_case_		) + 1		)]
          snake_case__		,							snake_case__    :       Tuple       		= get_aligned_output_features_output_indices(
              out_features=snake_case_					,	out_indices=snake_case_					,	stage_names=self.stage_names		)
