


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     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			int:




									"""simple docstring"""





									return None



class 						__a :
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			str:




									"""simple docstring"""





									return None



class 						__a (							unittest.TestCase     ):
		_a      :    Optional[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       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									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		)					->			List[str]:




									"""simple docstring"""





									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		)					->			List[str]:




									"""simple docstring"""





									from transformers import BertModel

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

									with TemporaryDirectory() as bert_save_dir:
																_UpperCAmelCase											=     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		)					->			List[Any]:




									"""simple docstring"""





									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																_UpperCAmelCase											=     self._test_export(_SCREAMING_SNAKE_CASE     ,       'tf'     ,       12     ,       **_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     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		)					->			Union[str, Any]:




									"""simple docstring"""





									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																_UpperCAmelCase											=     self._test_export(_SCREAMING_SNAKE_CASE     ,       'pt'     ,       12     ,       **_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     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     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=None     ,       **_SCREAMING_SNAKE_CASE		)					->			List[Any]:




									"""simple docstring"""





									try:
																# Compute path
																with TemporaryDirectory() as tempdir:
																							_UpperCAmelCase											=     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		)					->			int:




									"""simple docstring"""





									from transformers import BertModel

									_UpperCAmelCase											=     BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'		)		)
									_UpperCAmelCase											=     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		)					->			Union[str, Any]:




									"""simple docstring"""





									from transformers import TFBertModel

									_UpperCAmelCase											=     TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'		)		)
									_UpperCAmelCase											=     BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'		)
									self._test_infer_dynamic_axis(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       'tf'		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     FeatureExtractionPipeline(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
									_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     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		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     ['input_ids', 'attention_mask', 'token_type_ids']
									_UpperCAmelCase											=     {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
									_UpperCAmelCase						,			_UpperCAmelCase											=     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)
									_UpperCAmelCase						,			_UpperCAmelCase											=     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		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     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 Any

def 			lowerCAmelCase__					(     a__:						list     ,    a__:						list     ,    a__:						dict     ,    a__:						dict     ,    a__:						dict     ,    )    ->						list:


							'''simple docstring'''





							_validation(
							    a__     ,    a__     ,    a__     ,    a__     ,    a__     ,    )
							# Creates data structures and fill initial step
							_UpperCAmelCase											=     {}
							_UpperCAmelCase											=     {}
							for state in states_space:
														_UpperCAmelCase											=     observations_space[0]
														_UpperCAmelCase											=     (
														    initial_probabilities[state] * emission_probabilities[state][observation]
														)
														_UpperCAmelCase											=     None

							# Fills the data structure with the probabilities of
							# different transitions and pointers to previous states
							for o in range(1     ,    len(a__			)			):
														_UpperCAmelCase											=     observations_space[o]
														_UpperCAmelCase											=     observations_space[o - 1]
														for state in states_space:
																					# Calculates the argmax for probability function
																					_UpperCAmelCase											=     ''
																					_UpperCAmelCase											=     -1
																					for k_state in states_space:
																												_UpperCAmelCase											=     (
																												    probabilities[(k_state, prior_observation)]
																												    * transition_probabilities[k_state][state]
																												    * emission_probabilities[state][observation]
																												)
																												if probability > max_probability:
																																			_UpperCAmelCase											=     probability
																																			_UpperCAmelCase											=     k_state

            # Update probabilities and pointers dicts
																					_UpperCAmelCase											=     (
																					    probabilities[(arg_max, prior_observation)]
																					    * transition_probabilities[arg_max][state]
																					    * emission_probabilities[state][observation]
																					)

																					_UpperCAmelCase											=     arg_max

    # The final observation
							_UpperCAmelCase											=     observations_space[len(a__			) - 1]

							# argmax for given final observation
							_UpperCAmelCase											=     ''
							_UpperCAmelCase											=     -1
							for k_state in states_space:
														_UpperCAmelCase											=     probabilities[(k_state, final_observation)]
														if probability > max_probability:
																					_UpperCAmelCase											=     probability
																					_UpperCAmelCase											=     k_state
							_UpperCAmelCase											=     arg_max

							# Process pointers backwards
							_UpperCAmelCase											=     last_state
							_UpperCAmelCase											=     []
							for o in range(len(a__			) - 1     ,    -1     ,    -1			):
														result.append(a__			)
														_UpperCAmelCase											=     pointers[previous, observations_space[o]]
							result.reverse()

							return result

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    )    ->						None:


							'''simple docstring'''





							_validate_not_empty(
							    a__     ,    a__     ,    a__     ,    a__     ,    a__     ,    )
							_validate_lists(a__     ,    a__			)
							_validate_dicts(
							    a__     ,    a__     ,    a__			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    )    ->						None:


							'''simple docstring'''





							if not all(
							    [
							        observations_space,
							        states_space,
							        initial_probabilities,
							        transition_probabilities,
							        emission_probabilities,
							    ]			):
														raise ValueError('There\'s an empty parameter'			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any			)    ->						None:


							'''simple docstring'''





							_validate_list(a__     ,    'observations_space'			)
							_validate_list(a__     ,    'states_space'			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						str			)    ->						None:


							'''simple docstring'''





							if not isinstance(_object     ,    a__			):
														_UpperCAmelCase											=     F'''{var_name} must be a list'''
														raise ValueError(a__			)
							else:
														for x in _object:
																					if not isinstance(a__     ,    a__			):
																												_UpperCAmelCase											=     F'''{var_name} must be a list of strings'''
																												raise ValueError(a__			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    )    ->						None:


							'''simple docstring'''





							_validate_dict(a__     ,    'initial_probabilities'     ,    a__			)
							_validate_nested_dict(a__     ,    'transition_probabilities'			)
							_validate_nested_dict(a__     ,    'emission_probabilities'			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						str			)    ->						None:


							'''simple docstring'''





							_validate_dict(_object     ,    a__     ,    a__			)
							for x in _object.values():
														_validate_dict(a__     ,    a__     ,    a__     ,    a__			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						str     ,    a__:						type     ,    a__:						bool = False			)    ->						None:


							'''simple docstring'''





							if not isinstance(_object     ,    a__			):
														_UpperCAmelCase											=     F'''{var_name} must be a dict'''
														raise ValueError(a__			)
							if not all(isinstance(a__     ,    a__			) for x in _object			):
														_UpperCAmelCase											=     F'''{var_name} all keys must be strings'''
														raise ValueError(a__			)
							if not all(isinstance(a__     ,    a__			) for x in _object.values()			):
														_UpperCAmelCase											=     'nested dictionary ' if nested else ''
														_UpperCAmelCase											=     F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
														raise ValueError(a__			)


if __name__ == "__main__":
		from doctest import testmod

		testmod()
