






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   SCREAMING_SNAKE_CASE       (				snake_case_      :							str  ,						snake_case_      :							str=82  ,						snake_case_      :							Optional[int]=16     ):
	set_seed(42     )
	snake_case__				:   Tuple     =						RegressionModel()
	snake_case__				:   int     =						deepcopy(snake_case_     )
	snake_case__				:   Dict     =						RegressionDataset(length=snake_case_     )
	snake_case__				:   int     =						DataLoader(snake_case_  ,						batch_size=snake_case_     )
	model.to(accelerator.device     )
	snake_case__,		snake_case__				:   Dict     =						accelerator.prepare(snake_case_  ,						snake_case_     )
	return model, ddp_model, dataloader







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Accelerator  ,						snake_case_      :							Dict=False     ):
	snake_case__				:   str     =						AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"     )
	snake_case__				:   List[str]     =						load_dataset("glue"  ,						"mrpc"  ,						split="validation"     )

	def tokenize_function(snake_case_      :							Any     ):
		snake_case__				:   Optional[int]     =						tokenizer(examples["sentence1"]  ,						examples["sentence2"]  ,						truncation=snake_case_  ,						max_length=snake_case_     )
		return outputs

	with accelerator.main_process_first():
		snake_case__				:   List[Any]     =						dataset.map(
		    snake_case_  ,						batched=snake_case_  ,						remove_columns=["idx", "sentence1", "sentence2"]  ,						)

	snake_case__				:   Optional[Any]     =						tokenized_datasets.rename_column("label"  ,						"labels"     )

	def collate_fn(snake_case_      :							List[Any]     ):
		if use_longest:
			return tokenizer.pad(snake_case_  ,						padding="longest"  ,						return_tensors="pt"     )
		return tokenizer.pad(snake_case_  ,						padding="max_length"  ,						max_length=128  ,						return_tensors="pt"     )

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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[int]  ,						snake_case_      :							List[str]     ):
	snake_case__				:   Tuple     =						Accelerator(dispatch_batches=snake_case_  ,						split_batches=snake_case_     )
	snake_case__				:   int     =						get_dataloader(snake_case_  ,						not dispatch_batches     )
	snake_case__				:   List[str]     =						AutoModelForSequenceClassification.from_pretrained(
	    "hf-internal-testing/mrpc-bert-base-cased"  ,						return_dict=snake_case_     )
	snake_case__,		snake_case__				:   Dict     =						accelerator.prepare(snake_case_  ,						snake_case_     )
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple  ,						snake_case_      :							List[Any]  ,						snake_case_      :							str     ):
	snake_case__				:   List[Any]     =						[]
	for batch in dataloader:
		snake_case__,		snake_case__				:   List[str]     =						batch.values()
		with torch.no_grad():
			snake_case__				:   List[Any]     =						model(snake_case_     )
			snake_case__,		snake_case__				:   Any     =						accelerator.gather_for_metrics((logit, target)     )
			logits_and_targets.append((logit, target)     )
	snake_case__,		snake_case__				:   Dict     =						[], []
	for logit, targ in logits_and_targets:
		logits.append(snake_case_     )
		targs.append(snake_case_     )
	snake_case__,		snake_case__				:   List[Any]     =						torch.cat(snake_case_     ), torch.cat(snake_case_     )
	return logits, targs







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Accelerator  ,						snake_case_      :							Dict=82  ,						snake_case_      :							Union[str, Any]=False  ,						snake_case_      :							Union[str, Any]=False  ,						snake_case_      :							Tuple=16     ):
	snake_case__,		snake_case__,		snake_case__				:   Optional[Any]     =						get_basic_setup(snake_case_  ,						snake_case_  ,						snake_case_     )
	snake_case__,		snake_case__				:   Optional[int]     =						generate_predictions(snake_case_  ,						snake_case_  ,						snake_case_     )
	assert (
	    len(snake_case_     ) == num_samples
	), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case_     )}'''







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							bool = False  ,						snake_case_      :							bool = False     ):
	snake_case__				:   Dict     =						evaluate.load("glue"  ,						"mrpc"     )
	snake_case__,		snake_case__				:   Tuple     =						get_mrpc_setup(snake_case_  ,						snake_case_     )
	# First do baseline
	snake_case__,		snake_case__,		snake_case__				:   int     =						setup["no"]
	model.to(snake_case_     )
	model.eval()
	for batch in dataloader:
		batch.to(snake_case_     )
		with torch.inference_mode():
			snake_case__				:   Tuple     =						model(**snake_case_     )
		snake_case__				:   Tuple     =						outputs.logits.argmax(dim=-1     )
		metric.add_batch(predictions=snake_case_  ,						references=batch["labels"]     )
	snake_case__				:   Union[str, Any]     =						metric.compute()

	# Then do distributed
	snake_case__,		snake_case__,		snake_case__				:   List[Any]     =						setup["ddp"]
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			snake_case__				:   Union[str, Any]     =						model(**snake_case_     )
		snake_case__				:   List[str]     =						outputs.logits.argmax(dim=-1     )
		snake_case__				:   List[Any]     =						batch["labels"]
		snake_case__,		snake_case__				:   List[str]     =						accelerator.gather_for_metrics((preds, references)     )
		metric.add_batch(predictions=snake_case_  ,						references=snake_case_     )
	snake_case__				:   List[str]     =						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       (				):
	snake_case__				:   Optional[Any]     =						Accelerator(split_batches=snake_case_  ,						dispatch_batches=snake_case_     )
	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(snake_case_  ,						snake_case_     )
				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]:
			snake_case__				:   Optional[Any]     =						Accelerator(split_batches=snake_case_  ,						dispatch_batches=snake_case_     )
			if accelerator.is_local_main_process:
				print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''     )
			test_torch_metrics(snake_case_  ,						99     )
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print("**Test last batch is not dropped when perfectly divisible**"     )
	snake_case__				:   str     =						Accelerator()
	test_torch_metrics(snake_case_  ,						512     )
	accelerator.state._reset_state()







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()











import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ConditionalDetrImageProcessor



class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def __init__(      self  :						List[Any]							,	__A  :						Dict							,	__A  :						int=7							,	__A  :						Optional[Any]=3							,	__A  :						List[str]=3_0							,	__A  :						List[Any]=4_0_0							,	__A  :						Union[str, Any]=True							,	__A  :						List[Any]=None							,	__A  :						Optional[Any]=True							,	__A  :						Tuple=[0.5, 0.5, 0.5]							,	__A  :						Union[str, Any]=[0.5, 0.5, 0.5]							,	__A  :						List[str]=True							,	__A  :						Any=1 / 2_5_5							,	__A  :						Optional[int]=True							,	):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						snake_case__				:   List[str]     =						size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
						snake_case__				:   Dict     =						parent
						snake_case__				:   Optional[int]     =						batch_size
						snake_case__				:   Union[str, Any]     =						num_channels
						snake_case__				:   str     =						min_resolution
						snake_case__				:   Tuple     =						max_resolution
						snake_case__				:   List[Any]     =						do_resize
						snake_case__				:   Dict     =						size
						snake_case__				:   List[str]     =						do_normalize
						snake_case__				:   Optional[int]     =						image_mean
						snake_case__				:   Optional[int]     =						image_std
						snake_case__				:   Any     =						do_rescale
						snake_case__				:   Optional[int]     =						rescale_factor
						snake_case__				:   int     =						do_pad



					def  _lowercase  (      self  :						Dict ):
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}






					def  _lowercase  (      self  :						Optional[int]							,	__A  :						Dict							,	__A  :						List[Any]=False ):
						if not batched:
							snake_case__				:   List[str]     =						image_inputs[0]
							if isinstance(__A							,	Image.Image ):
								snake_case__,		snake_case__				:   Tuple     =						image.size
							else:
								snake_case__,		snake_case__				:   List[str]     =						image.shape[1], image.shape[2]
							if w < h:
								snake_case__				:   Dict     =						int(self.size["shortest_edge"] * h / w )
								snake_case__				:   Optional[int]     =						self.size["shortest_edge"]
							elif w > h:
								snake_case__				:   List[Any]     =						self.size["shortest_edge"]
								snake_case__				:   Union[str, Any]     =						int(self.size["shortest_edge"] * w / h )
							else:
								snake_case__				:   Dict     =						self.size["shortest_edge"]
								snake_case__				:   Dict     =						self.size["shortest_edge"]

						else:
							snake_case__				:   str     =						[]
							for image in image_inputs:
								snake_case__,		snake_case__				:   str     =						self.get_expected_values([image] )
								expected_values.append((expected_height, expected_width) )
							snake_case__				:   Dict     =						max(__A							,	key=lambda __A : item[0] )[0]
							snake_case__				:   Tuple     =						max(__A							,	key=lambda __A : item[1] )[1]

						return expected_height, expected_width



@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	ConditionalDetrImageProcessor if is_vision_available() else None



					def  _lowercase  (      self  :						int ):
						snake_case__				:   Tuple     =						ConditionalDetrImageProcessingTester(self )



					@property
					def  _lowercase  (      self  :						Any ):
						return self.image_processor_tester.prepare_image_processor_dict()



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   Dict     =						self.image_processing_class(**self.image_processor_dict )
						self.assertTrue(hasattr(__A							,	"image_mean" ) )
						self.assertTrue(hasattr(__A							,	"image_std" ) )
						self.assertTrue(hasattr(__A							,	"do_normalize" ) )
						self.assertTrue(hasattr(__A							,	"do_resize" ) )
						self.assertTrue(hasattr(__A							,	"size" ) )



					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Optional[Any]     =						self.image_processing_class.from_dict(self.image_processor_dict )
						self.assertEqual(image_processor.size							,	{"shortest_edge": 1_8, "longest_edge": 1_3_3_3} )
						self.assertEqual(image_processor.do_pad							,	__A )

						snake_case__				:   Any     =						self.image_processing_class.from_dict(
						    self.image_processor_dict							,	size=4_2							,	max_size=8_4							,	pad_and_return_pixel_mask=__A )
						self.assertEqual(image_processor.size							,	{"shortest_edge": 4_2, "longest_edge": 8_4} )
						self.assertEqual(image_processor.do_pad							,	__A )



					def  _lowercase  (      self  :						Union[str, Any] ):
						pass



					def  _lowercase  (      self  :						List[str] ):
						# Initialize image_processing
						snake_case__				:   Dict     =						self.image_processing_class(**self.image_processor_dict )
						# create random PIL images
						snake_case__				:   Dict     =						prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A )
						for image in image_inputs:
							self.assertIsInstance(__A							,	Image.Image )

						# Test not batched input
						snake_case__				:   Union[str, Any]     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Union[str, Any]     =						self.image_processor_tester.get_expected_values(__A )

						self.assertEqual(
						    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

						# Test batched
						snake_case__,		snake_case__				:   Tuple     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

						snake_case__				:   int     =						image_processing(__A							,	return_tensors="pt" ).pixel_values
						self.assertEqual(
						    encoded_images.shape							,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,	)



					def  _lowercase  (      self  :						Tuple ):
						# Initialize image_processing
						snake_case__				:   List[str]     =						self.image_processing_class(**self.image_processor_dict )
						# create random numpy tensors
						snake_case__				:   List[str]     =						prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A							,	numpify=__A )
						for image in image_inputs:
							self.assertIsInstance(__A							,	np.ndarray )

						# Test not batched input
						snake_case__				:   int     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Dict     =						self.image_processor_tester.get_expected_values(__A )

						self.assertEqual(
						    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

						# Test batched
						snake_case__				:   Optional[Any]     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   str     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

						self.assertEqual(
						    encoded_images.shape							,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,	)



					def  _lowercase  (      self  :						Tuple ):
						# Initialize image_processing
						snake_case__				:   Optional[Any]     =						self.image_processing_class(**self.image_processor_dict )
						# create random PyTorch tensors
						snake_case__				:   str     =						prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A							,	torchify=__A )
						for image in image_inputs:
							self.assertIsInstance(__A							,	torch.Tensor )

						# Test not batched input
						snake_case__				:   Tuple     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Optional[int]     =						self.image_processor_tester.get_expected_values(__A )

						self.assertEqual(
						    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

						# Test batched
						snake_case__				:   Dict     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   int     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

						self.assertEqual(
						    encoded_images.shape							,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,	)



					@slow
					def  _lowercase  (      self  :						List[Any] ):
						# prepare image and target
						snake_case__				:   Union[str, Any]     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"							,	"r" ) as f:
							snake_case__				:   Union[str, Any]     =						json.loads(f.read() )

						snake_case__				:   Optional[Any]     =						{"image_id": 3_9_7_6_9, "annotations": target}

						# encode them
						snake_case__				:   Tuple     =						ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50" )
						snake_case__				:   int     =						image_processing(images=__A							,	annotations=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   str     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   Tuple     =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   Optional[int]     =						torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,	__A ) )
						# verify boxes
						snake_case__				:   Tuple     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   List[Any]     =						torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   str     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   List[Any]     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   Optional[int]     =						torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify orig_size
						snake_case__				:   Dict     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   List[str]     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )






					@slow
					def  _lowercase  (      self  :						str ):
						# prepare image, target and masks_path
						snake_case__				:   Optional[Any]     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"							,	"r" ) as f:
							snake_case__				:   int     =						json.loads(f.read() )

						snake_case__				:   Optional[int]     =						{"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

						snake_case__				:   Optional[Any]     =						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

						# encode them
						snake_case__				:   Optional[int]     =						ConditionalDetrImageProcessor(format="coco_panoptic" )
						snake_case__				:   Tuple     =						image_processing(images=__A							,	annotations=__A							,	masks_path=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   Optional[Any]     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   List[str]     =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   Tuple     =						torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,	__A ) )
						# verify boxes
						snake_case__				:   Dict     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   int     =						torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   str     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   Tuple     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   Optional[Any]     =						torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify masks
						snake_case__				:   str     =						8_2_2_8_7_3
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,	__A )
						# verify orig_size
						snake_case__				:   int     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   List[Any]     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )



