





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 			UpperCamelCase_		:




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Dict:
										return None





class 			UpperCamelCase_		:




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Any:
										return None





class 			UpperCamelCase_		(  unittest.TestCase					):
					lowerCAmelCase_							=	[
					    # (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  )		->							int:
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
															self._test_export(lowerCAmelCase_				,						'tf'				,						12				,						**lowerCAmelCase_  )




					@require_torch
					@slow
					def 	lowerCAmelCase			(		self  )		->							int:
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
															self._test_export(lowerCAmelCase_				,						'pt'				,						12				,						**lowerCAmelCase_  )




					@require_torch
					@slow
					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										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(lowerCAmelCase_  )  )
															vocab_file.flush()
															_snake_case									=				BertTokenizerFast(vocab_file.name  )

										with TemporaryDirectory() as bert_save_dir:
															_snake_case									=				BertModel(BertConfig(vocab_size=len(lowerCAmelCase_  )  )  )
															model.save_pretrained(lowerCAmelCase_  )
															self._test_export(lowerCAmelCase_				,						'pt'				,						12				,						lowerCAmelCase_  )




					@require_tf
					@slow
					def 	lowerCAmelCase			(		self  )		->							List[str]:
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
															_snake_case									=				self._test_export(lowerCAmelCase_				,						'tf'				,						12				,						**lowerCAmelCase_  )
															_snake_case									=				quantize(Path(lowerCAmelCase_  )  )

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




					@require_torch
					@slow
					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
															_snake_case									=				self._test_export(lowerCAmelCase_				,						'pt'				,						12				,						**lowerCAmelCase_  )
															_snake_case									=				quantize(lowerCAmelCase_  )

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




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_=None				,						**lowerCAmelCase_  )		->							Tuple:
										try:
															# Compute path
															with TemporaryDirectory() as tempdir:
																				_snake_case									=				Path(lowerCAmelCase_  ).joinpath('model.onnx'  )

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

															# Export
															convert(lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						**lowerCAmelCase_  )

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




					@require_torch
					@require_tokenizers
					@slow
					def 	lowerCAmelCase			(		self  )		->							Optional[Any]:
										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(lowerCAmelCase_				,						lowerCAmelCase_				,						'pt'  )




					@require_tf
					@require_tokenizers
					@slow
					def 	lowerCAmelCase			(		self  )		->							Dict:
										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(lowerCAmelCase_				,						lowerCAmelCase_				,						'tf'  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							int:
										_snake_case									=				FeatureExtractionPipeline(lowerCAmelCase_				,						lowerCAmelCase_  )

										_snake_case									=				['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
										_snake_case							,     _snake_case							,     _snake_case							,     _snake_case									=				infer_shapes(lowerCAmelCase_				,						lowerCAmelCase_  )

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

										# 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  )		->							Optional[Any]:
										_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()				,						lowerCAmelCase_				,						lowerCAmelCase_  )

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

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

										# Parameter should be reordered according to their respective place in the function:
										# (input_ids, token_type_ids, attention_mask)
										self.assertEqual(lowerCAmelCase_				,						(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()				,						lowerCAmelCase_				,						lowerCAmelCase_  )

										# Should have exactly the one arg (all before the one not provided "some_other_args")
										self.assertEqual(len(lowerCAmelCase_  )				,						1  )
										self.assertEqual(len(lowerCAmelCase_  )				,						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  )		->							Optional[int]:
										_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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_      							=     logging.get_logger(__name__)

UpperCAmelCase_      							=     {
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	'''vit_msn'''




					def __init__(		self				,						lowerCAmelCase_=768				,						lowerCAmelCase_=12				,						lowerCAmelCase_=12				,						lowerCAmelCase_=3072				,						lowerCAmelCase_="gelu"				,						lowerCAmelCase_=0.0				,						lowerCAmelCase_=0.0				,						lowerCAmelCase_=0.02				,						lowerCAmelCase_=1E-06				,						lowerCAmelCase_=224				,						lowerCAmelCase_=16				,						lowerCAmelCase_=3				,						lowerCAmelCase_=True				,						**lowerCAmelCase_				,						)		->							str:
										super().__init__(**lowerCAmelCase_  )

										_snake_case									=				hidden_size
										_snake_case									=				num_hidden_layers
										_snake_case									=				num_attention_heads
										_snake_case									=				intermediate_size
										_snake_case									=				hidden_act
										_snake_case									=				hidden_dropout_prob
										_snake_case									=				attention_probs_dropout_prob
										_snake_case									=				initializer_range
										_snake_case									=				layer_norm_eps
										_snake_case									=				image_size
										_snake_case									=				patch_size
										_snake_case									=				num_channels
										_snake_case									=				qkv_bias



