





"""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


__lowerCAmelCase    :							Tuple													=						'true'







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__=82			,  lowerCamelCase__=16     ):





		"""simple docstring"""




		set_seed(42     )
		lowerCAmelCase__        =   RegressionModel()
		lowerCAmelCase__        =   deepcopy(lowerCamelCase__     )
		lowerCAmelCase__        =   RegressionDataset(length=lowerCamelCase__     )
		lowerCAmelCase__        =   DataLoader(lowerCamelCase__			,  batch_size=lowerCamelCase__     )
		model.to(accelerator.device     )
		lowerCAmelCase__        =   accelerator.prepare(lowerCamelCase__			,  lowerCamelCase__     )
		return model, ddp_model, dataloader







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__=False     ):





		"""simple docstring"""




		lowerCAmelCase__        =   AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""     )
		lowerCAmelCase__        =   load_dataset("""glue"""			,  """mrpc"""			,  split="""validation"""     )

		def tokenize_function(lowerCamelCase__     ):
				lowerCAmelCase__        =   tokenizer(examples["""sentence1"""]			,  examples["""sentence2"""]			,  truncation=lowerCamelCase__			,  max_length=lowerCamelCase__     )
				return outputs

		with accelerator.main_process_first():
				lowerCAmelCase__        =   dataset.map(
				    lowerCamelCase__			,  batched=lowerCamelCase__			,  remove_columns=["""idx""", """sentence1""", """sentence2"""]			,  )

		lowerCAmelCase__        =   tokenized_datasets.rename_column("""label"""			,  """labels"""     )

		def collate_fn(lowerCamelCase__     ):
				if use_longest:
						return tokenizer.pad(lowerCamelCase__			,  padding="""longest"""			,  return_tensors="""pt"""     )
				return tokenizer.pad(lowerCamelCase__			,  padding="""max_length"""			,  max_length=128			,  return_tensors="""pt"""     )

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







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




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







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   []
		for batch in dataloader:
				lowerCAmelCase__        =   batch.values()
				with torch.no_grad():
						lowerCAmelCase__        =   model(lowerCamelCase__     )
						lowerCAmelCase__        =   accelerator.gather_for_metrics((logit, target)     )
						logits_and_targets.append((logit, target)     )
		lowerCAmelCase__        =   [], []
		for logit, targ in logits_and_targets:
				logits.append(lowerCamelCase__     )
				targs.append(lowerCamelCase__     )
		lowerCAmelCase__        =   torch.cat(lowerCamelCase__     ), torch.cat(lowerCamelCase__     )
		return logits, targs







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__=82			,  lowerCamelCase__=False			,  lowerCamelCase__=False			,  lowerCamelCase__=16     ):





		"""simple docstring"""




		lowerCAmelCase__        =   get_basic_setup(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )
		lowerCAmelCase__        =   generate_predictions(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )
		assert (
		    len(lowerCamelCase__     ) == num_samples
		), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase__     )}"""







def 							_UpperCAmelCase		(       lowerCamelCase__ = False			,  lowerCamelCase__ = False     ):





		"""simple docstring"""




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

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





		"""simple docstring"""




		lowerCAmelCase__        =   Accelerator(split_batches=lowerCamelCase__			,  dispatch_batches=lowerCamelCase__     )
		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(lowerCamelCase__			,  lowerCamelCase__     )
								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__        =   Accelerator(split_batches=lowerCamelCase__			,  dispatch_batches=lowerCamelCase__     )
						if accelerator.is_local_main_process:
								print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""     )
						test_torch_metrics(lowerCamelCase__			,  99     )
						accelerator.state._reset_state()
		if accelerator.is_local_main_process:
				print("""**Test last batch is not dropped when perfectly divisible**"""     )
		lowerCAmelCase__        =   Accelerator()
		test_torch_metrics(lowerCamelCase__			,  512     )
		accelerator.state._reset_state()







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		main()


if __name__ == "__main__":
			main()





"""simple docstring"""




import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   0




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""     )
							self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
									json.dump(
									    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
									json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
									self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							# Ensure we can load the image processor from the feature extractor config
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
									json.dump(
									    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
									json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
									self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   CLIPConfig()

									# Create a dummy config file with image_proceesor_type
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
									json.dump(
									    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
									json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

									# remove image_processor_type to make sure config.json alone is enough to load image processor locally
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     ).to_dict()

									config_dict.pop("""image_processor_type"""     )
									lowerCAmelCase__        =   CLIPImageProcessor(**snake_case__     )

									# save in new folder
									model_config.save_pretrained(snake_case__     )
									config.save_pretrained(snake_case__     )

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )

									# make sure private variable is not incorrectly saved
									lowerCAmelCase__        =   json.loads(config.to_json_string()     )
									self.assertTrue("""_processor_class""" not in dict_as_saved     )

							self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									json.dump(
									    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
									self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							with self.assertRaisesRegex(
							    snake_case__  ,						"""clip-base is not a local folder and is not a valid model identifier"""     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""clip-base"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							with self.assertRaisesRegex(
							    snake_case__  ,						R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__  ,						revision="""aaaaaa"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							with self.assertRaisesRegex(
							    snake_case__  ,						"""hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""  ,						):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							# If remote code is not set, we will time out when asking whether to load the model.
							with self.assertRaises(snake_case__     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""     )
							# If remote code is disabled, we can't load this config.
							with self.assertRaises(snake_case__     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )

							lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
							    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )
							self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )

							# Test image processor can be reloaded.
							with tempfile.TemporaryDirectory() as tmp_dir:
									image_processor.save_pretrained(snake_case__     )
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__  ,						trust_remote_code=snake_case__     )
							self.assertEqual(reloaded_image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							try:
									AutoConfig.register("""custom"""  ,						snake_case__     )
									AutoImageProcessor.register(snake_case__  ,						snake_case__     )
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(snake_case__     ):
											AutoImageProcessor.register(snake_case__  ,						snake_case__     )

									with tempfile.TemporaryDirectory() as tmpdirname:
											lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
											lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
											json.dump(
											    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
											json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

											lowerCAmelCase__        =   CustomImageProcessor.from_pretrained(snake_case__     )

									# Now that the config is registered, it can be used as any other config with the auto-API
									with tempfile.TemporaryDirectory() as tmp_dir:
											image_processor.save_pretrained(snake_case__     )
											lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
											self.assertIsInstance(snake_case__  ,						snake_case__     )

							finally:
									if "custom" in CONFIG_MAPPING._extra_content:
											del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
											del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):

							class       a_      (     __UpperCamelCase			):
												UpperCamelCase_ :	Tuple			=    True

							try:
									AutoConfig.register("""custom"""  ,						snake_case__     )
									AutoImageProcessor.register(snake_case__  ,						snake_case__     )
									# If remote code is not set, the default is to use local
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""     )
									self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )
									self.assertTrue(image_processor.is_local     )

									# If remote code is disabled, we load the local one.
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )
									self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )
									self.assertTrue(image_processor.is_local     )

									# If remote is enabled, we load from the Hub
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )
									self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )
									self.assertTrue(not hasattr(snake_case__  ,						"""is_local"""     )     )

							finally:
									if "custom" in CONFIG_MAPPING._extra_content:
											del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
											del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




