




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_									=	'true'




def 					lowerCamelCase__       (						A__       :					Any  ,			A__       :					List[str]=82  ,			A__       :					int=16	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					Accelerator  ,			A__       :					List[Any]=False	):





       '''simple docstring'''



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

       def tokenize_function(A__       :					str	):
              __lowerCamelCase   =						tokenizer(examples["""sentence1"""]  ,			examples["""sentence2"""]  ,			truncation=A__  ,			max_length=A__	)
              return outputs

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

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

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

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




def 					lowerCamelCase__       (						A__       :					int  ,			A__       :					List[Any]	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					Any  ,			A__       :					List[Any]  ,			A__       :					List[str]	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					Accelerator  ,			A__       :					List[Any]=82  ,			A__       :					List[Any]=False  ,			A__       :					str=False  ,			A__       :					Union[str, Any]=16	):





       '''simple docstring'''



       __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						get_basic_setup(A__  ,			A__  ,			A__	)
       __lowerCamelCase,   __lowerCamelCase   =						generate_predictions(A__  ,			A__  ,			A__	)
       assert (
           len(A__	) == num_samples
       ), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A__	)}'




def 					lowerCamelCase__       (						A__       :					bool = False  ,			A__       :					bool = False	):





       '''simple docstring'''



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

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





       '''simple docstring'''



       __lowerCamelCase   =						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   =						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   =						Accelerator()
       test_torch_metrics(A__  ,			512	)
       accelerator.state._reset_state()




def 					lowerCamelCase__       (						A__       :					Tuple	):





       '''simple docstring'''



       main()


if __name__ == "__main__":
       main()







'''simple docstring'''

import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
a  :				Dict									=  (720, 1280)  # Height, Width
a  :				Tuple									=  (0.4, 0.6)  # if height or width lower than this scale, drop it.
a  :				Dict									=  1 / 100
a  :				str									=  ''
a  :				Any									=  ''
a  :				Optional[int]									=  ''
a  :				List[str]									=  250


def 		__magic_name__    (	)		->					None:

					'''simple docstring'''

					snake_case_			,snake_case_									=				get_dataset(__UpperCAmelCase,      __UpperCAmelCase	)
					for index in range(__UpperCAmelCase	):
										snake_case_									=				random.sample(range(len(__UpperCAmelCase	)	),      4	)
										snake_case_			,snake_case_			,snake_case_									=				update_image_and_anno(
										    __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      filter_scale=__UpperCAmelCase,      )

										# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
										snake_case_									=				random_chars(32	)
										snake_case_									=				path.split(os.sep	)[-1].rsplit('''.''',      1	)[0]
										snake_case_									=				F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
										cva.imwrite(F"{file_root}.jpg",      __UpperCAmelCase,      [cva.IMWRITE_JPEG_QUALITY, 85]	)
										print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"	)
										snake_case_									=				[]
										for anno in new_annos:
															snake_case_									=				anno[3] - anno[1]
															snake_case_									=				anno[4] - anno[2]
															snake_case_									=				anno[1] + width / 2
															snake_case_									=				anno[2] + height / 2
															snake_case_									=				F"{anno[0]} {x_center} {y_center} {width} {height}"
															annos_list.append(__UpperCAmelCase	)
										with open(F"{file_root}.txt",      '''w'''	) as outfile:
															outfile.write('''\n'''.join(line for line in annos_list	)	)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					tuple[list, list]:

					'''simple docstring'''

					snake_case_									=				[]
					snake_case_									=				[]
					for label_file in glob.glob(os.path.join(__UpperCAmelCase,      '''*.txt'''	)	):
										snake_case_									=				label_file.split(os.sep	)[-1].rsplit('''.''',      1	)[0]
										with open(__UpperCAmelCase	) as in_file:
															snake_case_									=				in_file.readlines()
										snake_case_									=				os.path.join(__UpperCAmelCase,      F"{label_name}.jpg"	)

										snake_case_									=				[]
										for obj_list in obj_lists:
															snake_case_									=				obj_list.rstrip('''\n'''	).split(''' '''	)
															snake_case_									=				float(obj[1]	) - float(obj[3]	) / 2
															snake_case_									=				float(obj[2]	) - float(obj[4]	) / 2
															snake_case_									=				float(obj[1]	) + float(obj[3]	) / 2
															snake_case_									=				float(obj[2]	) + float(obj[4]	) / 2

															boxes.append([int(obj[0]	), xmin, ymin, xmax, ymax]	)
										if not boxes:
															continue
										img_paths.append(__UpperCAmelCase	)
										labels.append(__UpperCAmelCase	)
					return img_paths, labels


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase = 0.0,      )		->					tuple[list, list, str]:

					'''simple docstring'''

					snake_case_									=				np.zeros([output_size[0], output_size[1], 3],      dtype=np.uinta	)
					snake_case_									=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					snake_case_									=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					snake_case_									=				int(scale_x * output_size[1]	)
					snake_case_									=				int(scale_y * output_size[0]	)

					snake_case_									=				[]
					snake_case_									=				[]
					for i, index in enumerate(__UpperCAmelCase	):
										snake_case_									=				all_img_list[index]
										path_list.append(__UpperCAmelCase	)
										snake_case_									=				all_annos[index]
										snake_case_									=				cva.imread(__UpperCAmelCase	)
										if i == 0:  # top-left
															snake_case_									=				cva.resize(__UpperCAmelCase,      (divid_point_x, divid_point_y)	)
															snake_case_									=				img
															for bbox in img_annos:
																				snake_case_									=				bbox[1] * scale_x
																				snake_case_									=				bbox[2] * scale_y
																				snake_case_									=				bbox[3] * scale_x
																				snake_case_									=				bbox[4] * scale_y
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
										elif i == 1:  # top-right
															snake_case_									=				cva.resize(__UpperCAmelCase,      (output_size[1] - divid_point_x, divid_point_y)	)
															snake_case_									=				img
															for bbox in img_annos:
																				snake_case_									=				scale_x + bbox[1] * (1 - scale_x)
																				snake_case_									=				bbox[2] * scale_y
																				snake_case_									=				scale_x + bbox[3] * (1 - scale_x)
																				snake_case_									=				bbox[4] * scale_y
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
										elif i == 2:  # bottom-left
															snake_case_									=				cva.resize(__UpperCAmelCase,      (divid_point_x, output_size[0] - divid_point_y)	)
															snake_case_									=				img
															for bbox in img_annos:
																				snake_case_									=				bbox[1] * scale_x
																				snake_case_									=				scale_y + bbox[2] * (1 - scale_y)
																				snake_case_									=				bbox[3] * scale_x
																				snake_case_									=				scale_y + bbox[4] * (1 - scale_y)
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
										else:  # bottom-right
															snake_case_									=				cva.resize(
															    __UpperCAmelCase,      (output_size[1] - divid_point_x, output_size[0] - divid_point_y)	)
															snake_case_									=				img
															for bbox in img_annos:
																				snake_case_									=				scale_x + bbox[1] * (1 - scale_x)
																				snake_case_									=				scale_y + bbox[2] * (1 - scale_y)
																				snake_case_									=				scale_x + bbox[3] * (1 - scale_x)
																				snake_case_									=				scale_y + bbox[4] * (1 - scale_y)
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)

    # Remove bounding box small than scale of filter
					if filter_scale > 0:
										snake_case_									=				[
										    anno
										    for anno in new_anno
										    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
										]

					return output_img, new_anno, path_list[0]


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					assert number_char > 1, "The number of character should greater than 1"
					snake_case_									=				ascii_lowercase + digits
					return "".join(random.choice(__UpperCAmelCase	) for _ in range(__UpperCAmelCase	)	)


if __name__ == "__main__":
							main()
							print('DONE ✅')

