

"""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 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				str   ,lowercase_			:				Any   ,lowercase_			:				str		):
						return None







class 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				int   ,lowercase_			:				Tuple   ,lowercase_			:				Optional[int]   ,lowercase_			:				Dict		):
						return None







class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       [
					    # (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			:				Optional[int]		):
						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
							self._test_export(lowercase_   ,'''tf'''   ,1_2   ,**lowercase_		)







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







					@require_torch
					@slow
					def        __lowerCAmelCase (       self			:				List[Any]		):
						from transformers import BertModel

						lowerCAmelCase__	:						Union[str, Any]  				=	['''[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()
							lowerCAmelCase__	:						List[Any]  				=	BertTokenizerFast(vocab_file.name		)

						with TemporaryDirectory() as bert_save_dir:
							lowerCAmelCase__	:						str  				=	BertModel(BertConfig(vocab_size=len(lowercase_		)		)		)
							model.save_pretrained(lowercase_		)
							self._test_export(lowercase_   ,'''pt'''   ,1_2   ,lowercase_		)







					@require_tf
					@slow
					def        __lowerCAmelCase (       self			:				Any		):
						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
							lowerCAmelCase__	:						Optional[int]  				=	self._test_export(lowercase_   ,'''tf'''   ,1_2   ,**lowercase_		)
							lowerCAmelCase__	:						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        __lowerCAmelCase (       self			:				List[str]		):
						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
							lowerCAmelCase__	:						List[str]  				=	self._test_export(lowercase_   ,'''pt'''   ,1_2   ,**lowercase_		)
							lowerCAmelCase__	:						Union[str, Any]  				=	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        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				List[str]   ,lowercase_			:				Any   ,lowercase_			:				Dict=None   ,**lowercase_			:				str		):
						try:
							# Compute path
							with TemporaryDirectory() as tempdir:
								lowerCAmelCase__	:						Any  				=	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        __lowerCAmelCase (       self			:				Optional[Any]		):
						from transformers import BertModel

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







					@require_tf
					@require_tokenizers
					@slow
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						from transformers import TFBertModel

						lowerCAmelCase__	:						Tuple  				=	TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''		)		)
						lowerCAmelCase__	:						Tuple  				=	BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''		)
						self._test_infer_dynamic_axis(lowercase_   ,lowercase_   ,'''tf'''		)







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				Dict   ,lowercase_			:				Dict   ,lowercase_			:				Optional[Any]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	FeatureExtractionPipeline(lowercase_   ,lowercase_		)

						lowerCAmelCase__	:						List[Any]  				=	['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
						lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						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        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						List[str]  				=	['''input_ids''', '''attention_mask''', '''token_type_ids''']
						lowerCAmelCase__	:						Any  				=	{'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
						lowerCAmelCase__				,lowerCAmelCase__	:						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)
						lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, 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        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						List[Any]  				=	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"""





import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__UpperCamelCase  :      List[str]							      =		'''true'''

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=82						,     A_=16 ):
	set_seed(42 )
	lowerCAmelCase__	:						Union[str, Any]  				=	RegressionModel()
	lowerCAmelCase__	:						Optional[int]  				=	deepcopy(A_ )
	lowerCAmelCase__	:						Any  				=	RegressionDataset(length=A_ )
	lowerCAmelCase__	:						List[str]  				=	DataLoader(A_						,     batch_size=A_ )
	model.to(accelerator.device )
	lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	accelerator.prepare(A_						,     A_ )
	return model, ddp_model, dataloader

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=False ):
	lowerCAmelCase__	:						List[str]  				=	AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
	lowerCAmelCase__	:						List[str]  				=	load_dataset('''glue'''						,     '''mrpc'''						,     split='''validation''' )

	def tokenize_function(A_ ):
		lowerCAmelCase__	:						Optional[Any]  				=	tokenizer(examples['''sentence1''']						,     examples['''sentence2''']						,     truncation=A_						,     max_length=A_ )
		return outputs

	with accelerator.main_process_first():
		lowerCAmelCase__	:						Dict  				=	dataset.map(
		    A_						,     batched=A_						,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']						,     )

	lowerCAmelCase__	:						int  				=	tokenized_datasets.rename_column('''label'''						,     '''labels''' )

	def collate_fn(A_ ):
		if use_longest:
			return tokenizer.pad(A_						,     padding='''longest'''						,     return_tensors='''pt''' )
		return tokenizer.pad(A_						,     padding='''max_length'''						,     max_length=1_28						,     return_tensors='''pt''' )

	return DataLoader(A_						,     shuffle=A_						,     collate_fn=A_						,     batch_size=16 )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	Accelerator(dispatch_batches=A_						,     split_batches=A_ )
	lowerCAmelCase__	:						str  				=	get_dataloader(A_						,     not dispatch_batches )
	lowerCAmelCase__	:						Optional[int]  				=	AutoModelForSequenceClassification.from_pretrained(
	    '''hf-internal-testing/mrpc-bert-base-cased'''						,     return_dict=A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	accelerator.prepare(A_						,     A_ )
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	[]
	for batch in dataloader:
		lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	batch.values()
		with torch.no_grad():
			lowerCAmelCase__	:						List[str]  				=	model(A_ )
			lowerCAmelCase__				,lowerCAmelCase__	:						str  				=	accelerator.gather_for_metrics((logit, target) )
			logits_and_targets.append((logit, target) )
	lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	[], []
	for logit, targ in logits_and_targets:
		logits.append(A_ )
		targs.append(A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	torch.cat(A_ ), torch.cat(A_ )
	return logits, targs

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=82						,     A_=False						,     A_=False						,     A_=16 ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	get_basic_setup(A_						,     A_						,     A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	generate_predictions(A_						,     A_						,     A_ )
	assert (
	    len(A_ ) == num_samples
	), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A_ )}'

def   __SCREAMING_SNAKE_CASE  (      A_ = False						,     A_ = False ):
	lowerCAmelCase__	:						Optional[Any]  				=	evaluate.load('''glue'''						,     '''mrpc''' )
	lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	get_mrpc_setup(A_						,     A_ )
	# First do baseline
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	setup['''no''']
	model.to(A_ )
	model.eval()
	for batch in dataloader:
		batch.to(A_ )
		with torch.inference_mode():
			lowerCAmelCase__	:						Optional[int]  				=	model(**A_ )
		lowerCAmelCase__	:						Dict  				=	outputs.logits.argmax(dim=-1 )
		metric.add_batch(predictions=A_						,     references=batch['''labels'''] )
	lowerCAmelCase__	:						Dict  				=	metric.compute()

	# Then do distributed
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	setup['''ddp''']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			lowerCAmelCase__	:						Union[str, Any]  				=	model(**A_ )
		lowerCAmelCase__	:						int  				=	outputs.logits.argmax(dim=-1 )
		lowerCAmelCase__	:						int  				=	batch['''labels''']
		lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	accelerator.gather_for_metrics((preds, references) )
		metric.add_batch(predictions=A_						,     references=A_ )
	lowerCAmelCase__	:						List[Any]  				=	metric.compute()

	for key in "accuracy f1".split():
		assert math.isclose(
		    baseline[key]						,     distributed[key] ), f'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[str]  				=	Accelerator(split_batches=A_						,     dispatch_batches=A_ )
	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_warning()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	# These are a bit slower so they should only be ran on the GPU or TPU
	if torch.cuda.is_available() or is_tpu_available():
		if accelerator.is_local_main_process:
			print('''**Testing gather_for_metrics**''' )
		for split_batches in [True, False]:
			for dispatch_batches in [True, False]:
				if accelerator.is_local_main_process:
					print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`' )
				test_mrpc(A_						,     A_ )
				accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('''**Test torch metrics**''' )
	for split_batches in [True, False]:
		for dispatch_batches in [True, False]:
			lowerCAmelCase__	:						Optional[Any]  				=	Accelerator(split_batches=A_						,     dispatch_batches=A_ )
			if accelerator.is_local_main_process:
				print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99' )
			test_torch_metrics(A_						,     99 )
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('''**Test last batch is not dropped when perfectly divisible**''' )
	lowerCAmelCase__	:						List[str]  				=	Accelerator()
	test_torch_metrics(A_						,     5_12 )
	accelerator.state._reset_state()

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
							main()




