

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  lowercase				:




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											return None







class  lowercase				:




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											return None







class  lowercase				(       unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: int       			=			[
				    # (model_name, model_kwargs)
				    ('''bert-base-cased''', {}),
				    ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
				]




				@require_tf
				@slow
				def     a							(					self       ):
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																		self._test_export(snake_case      ,   'tf'      ,   12      ,   **snake_case       )




				@require_torch
				@slow
				def     a							(					self       ):
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																		self._test_export(snake_case      ,   'pt'      ,   12      ,   **snake_case       )




				@require_torch
				@slow
				def     a							(					self       ):
											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(snake_case       )       )
																		vocab_file.flush()
																		snake_case_							=    BertTokenizerFast(vocab_file.name       )

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




				@require_tf
				@slow
				def     a							(					self       ):
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																		snake_case_							=    self._test_export(snake_case      ,   'tf'      ,   12      ,   **snake_case       )
																		snake_case_							=    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     a							(					self       ):
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																		snake_case_							=    self._test_export(snake_case      ,   'pt'      ,   12      ,   **snake_case       )
																		snake_case_							=    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     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case=None      ,   **snake_case       ):
											try:
																		# Compute path
																		with TemporaryDirectory() as tempdir:
																									snake_case_							=    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     a							(					self       ):
											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(snake_case      ,   snake_case      ,   'pt'       )




				@require_tf
				@require_tokenizers
				@slow
				def     a							(					self       ):
											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(snake_case      ,   snake_case      ,   'tf'       )




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    FeatureExtractionPipeline(snake_case      ,   snake_case       )

											snake_case_							=    ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
											snake_case_				,    snake_case_				,    snake_case_				,    snake_case_							=    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     a							(					self       ):
											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()      ,   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)
											snake_case_				,    snake_case_							=    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     a							(					self       ):
											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()       )







import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_UpperCAmelCase		:   Optional[int]										=       5_0000
_UpperCAmelCase		:   Dict										=       5000

_UpperCAmelCase				,						_UpperCAmelCase		:   Optional[int]										=       os.path.split(__file__)
_UpperCAmelCase		:   List[str]										=       os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i in range(UpperCamelCase__      ):
														snake_case_							=    dataset[i]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i in range(0   ,							len(UpperCamelCase__      )   ,							UpperCamelCase__      ):
														snake_case_							=    dataset[i : i + batch_size]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with dataset.formatted_as(type=UpperCamelCase__      ):
														for i in range(UpperCamelCase__      ):
																					snake_case_							=    dataset[i]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with dataset.formatted_as(type=UpperCamelCase__      ):
														for i in range(0   ,							UpperCamelCase__   ,							UpperCamelCase__      ):
																					snake_case_							=    dataset[i : i + batch_size]





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    {'num examples': SPEED_TEST_N_EXAMPLES}
							snake_case_							=    [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
							    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
							]

							snake_case_							=    [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
							    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
							]
							with tempfile.TemporaryDirectory() as tmp_dir:
														print('generating dataset'      )
														snake_case_							=    datasets.Features(
														    {'list': datasets.Sequence(datasets.Value('float32'      )      ), 'numbers': datasets.Value('float32'      )}      )
														snake_case_							=    generate_example_dataset(
														    os.path.join(UpperCamelCase__   ,							'dataset.arrow'      )   ,							UpperCamelCase__   ,							num_examples=UpperCamelCase__   ,							seq_shapes={'list': (100,)}   ,							)
														print('first set of iterations'      )
														for func, kwargs in functions:
																					print(func.__name__   ,							str(UpperCamelCase__      )      )
																					snake_case_							=    func(UpperCamelCase__   ,							**UpperCamelCase__      )

														print('shuffling dataset'      )
														snake_case_							=    dataset.shuffle()
														print('Second set of iterations (after shuffling'      )
														for func, kwargs in functions_shuffled:
																					print('shuffled '   ,							func.__name__   ,							str(UpperCamelCase__      )      )
																					snake_case_							=    func(
																					    UpperCamelCase__   ,							**UpperCamelCase__      )

							with open(UpperCamelCase__   ,							'wb'      ) as f:
														f.write(json.dumps(UpperCamelCase__      ).encode('utf-8'      )      )


if __name__ == "__main__":  # useful to run the profiler
					benchmark_iterating()




