


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   :


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							return None






class 							lowerCamelCase   :


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
							return None






class 							lowerCamelCase   (  unittest.TestCase ):
	_lowerCAmelCase     :		List[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  A(    self):
							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(lowercase__				,     '''tf'''				,     1_2				,     **lowercase__)


	@require_torch
	@slow
	def  A(    self):
							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(lowercase__				,     '''pt'''				,     1_2				,     **lowercase__)


	@require_torch
	@slow
	def  A(    self):
							from transformers import BertModel

							__UpperCAmelCase			:							Tuple	    =	['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
							with NamedTemporaryFile(mode='''w+t''') as vocab_file:
													vocab_file.write('''\n'''.join(lowercase__))
													vocab_file.flush()
													__UpperCAmelCase			:							Tuple	    =	BertTokenizerFast(vocab_file.name)

							with TemporaryDirectory() as bert_save_dir:
													__UpperCAmelCase			:							Tuple	    =	BertModel(BertConfig(vocab_size=len(lowercase__)))
													model.save_pretrained(lowercase__)
													self._test_export(lowercase__				,     '''pt'''				,     1_2				,     lowercase__)


	@require_tf
	@slow
	def  A(    self):
							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													__UpperCAmelCase			:							List[Any]	    =	self._test_export(lowercase__				,     '''tf'''				,     1_2				,     **lowercase__)
													__UpperCAmelCase			:							Tuple	    =	quantize(Path(lowercase__))

													# Ensure the actual quantized model is not bigger than the original one
													if quantized_path.stat().st_size >= Path(lowercase__).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:
													__UpperCAmelCase			:							Optional[int]	    =	self._test_export(lowercase__				,     '''pt'''				,     1_2				,     **lowercase__)
													__UpperCAmelCase			:							int	    =	quantize(lowercase__)

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


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__=None				,     **lowercase__):
							try:
													# Compute path
													with TemporaryDirectory() as tempdir:
																			__UpperCAmelCase			:							Tuple	    =	Path(lowercase__).joinpath('''model.onnx''')

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

													# Export
													convert(lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__)

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


	@require_torch
	@require_tokenizers
	@slow
	def  A(    self):
							from transformers import BertModel

							__UpperCAmelCase			:							Union[str, Any]	    =	BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''))
							__UpperCAmelCase			:							Tuple	    =	BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random''')
							self._test_infer_dynamic_axis(lowercase__				,     lowercase__				,     '''pt''')


	@require_tf
	@require_tokenizers
	@slow
	def  A(    self):
							from transformers import TFBertModel

							__UpperCAmelCase			:							Optional[Any]	    =	TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''))
							__UpperCAmelCase			:							Tuple	    =	BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random''')
							self._test_infer_dynamic_axis(lowercase__				,     lowercase__				,     '''tf''')


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							Optional[Any]	    =	FeatureExtractionPipeline(lowercase__				,     lowercase__)

							__UpperCAmelCase			:							str	    =	['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
							__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	infer_shapes(lowercase__				,     lowercase__)

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

							# 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):
							__UpperCAmelCase			:							Any	    =	['''input_ids''', '''attention_mask''', '''token_type_ids''']
							__UpperCAmelCase			:							Tuple	    =	{'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Any	    =	ensure_valid_input(FuncContiguousArgs()				,     lowercase__				,     lowercase__)

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

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

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

							# Should have exactly the one arg (all before the one not provided "some_other_args")
							self.assertEqual(len(lowercase__)				,     1)
							self.assertEqual(len(lowercase__)				,     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):
							__UpperCAmelCase			:							Union[str, Any]	    =	generate_identified_filename(Path('''/home/something/my_fake_model.onnx''')				,     '''-test''')
							self.assertEqual('''/home/something/my_fake_model-test.onnx'''				,     generated.as_posix())





import inspect
import os
import sys
import unittest

import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu




class 							lowerCamelCase   (  unittest.TestCase ):


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	inspect.getfile(accelerate.test_utils)
							__UpperCAmelCase			:							Dict	    =	os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['''scripts''', '''test_script.py'''])
							__UpperCAmelCase			:							Union[str, Any]	    =	os.path.sep.join(inspect.getfile(self.__class__).split(os.path.sep)[:-1])


	@require_tpu
	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	F"\n            {self.test_dir}/xla_spawn.py\n            --num_cores 8\n            {self.test_file_path}\n        ".split()
							__UpperCAmelCase			:							Union[str, Any]	    =	[sys.executable] + distributed_args
							execute_subprocess_async(lowercase__				,     env=os.environ.copy())

