





'''simple docstring'''







import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
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

					from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
					snake_case_   :				Optional[Any]		=     False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor


class     __a					(unittest.TestCase   ):

							def __init__(							self							:   List[Any]				,   __magic_name__							:   List[str]				,   __magic_name__							:   List[str]=7				,   __magic_name__							:   List[str]=3				,   __magic_name__							:   Dict=18				,   __magic_name__							:   Union[str, Any]=30				,   __magic_name__							:   int=4_00				,   __magic_name__							:   Any=None				,   __magic_name__							:   str=True				,   __magic_name__							:   int=True				,   __magic_name__							:   Tuple=None				,   )    ->				Tuple:







													"""simple docstring"""
													UpperCAmelCase_	:					str     =     size if size is not None else {'''height''': 20, '''width''': 20}
													UpperCAmelCase_	:					List[Any]     =     parent
													UpperCAmelCase_	:					str     =     batch_size
													UpperCAmelCase_	:					Dict     =     num_channels
													UpperCAmelCase_	:					List[str]     =     image_size
													UpperCAmelCase_	:					Optional[int]     =     min_resolution
													UpperCAmelCase_	:					str     =     max_resolution
													UpperCAmelCase_	:					int     =     size
													UpperCAmelCase_	:					Optional[int]     =     do_normalize
													UpperCAmelCase_	:					str     =     do_convert_rgb
													UpperCAmelCase_	:					int     =     [5_12, 10_24, 20_48, 40_96]
													UpperCAmelCase_	:					Any     =     patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}

							def 			UpperCAmelCase__		(							self							:   Tuple      )    ->				int:







													"""simple docstring"""
													return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}



							def 			UpperCAmelCase__		(							self							:   List[str]      )    ->				str:







													"""simple docstring"""
													UpperCAmelCase_	:					int     =     '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
													UpperCAmelCase_	:					List[Any]     =     Image.open(requests.get(_snake_case				,   stream=_snake_case      ).raw      ).convert('''RGB'''      )
													return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11				,						reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."				,						)
@require_torch
@require_vision
class     __a					(lowerCamelCase				,						unittest.TestCase   ):
							__a    :      Optional[int]  	=				PixaStructImageProcessor if is_vision_available() else None

							def 			UpperCAmelCase__		(							self							:   List[str]      )    ->				List[str]:







													"""simple docstring"""
													UpperCAmelCase_	:					Optional[int]     =     PixaStructImageProcessingTester(self      )

							@property
							def 			UpperCAmelCase__		(							self							:   Optional[int]      )    ->				Union[str, Any]:







													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()

							def 			UpperCAmelCase__		(							self							:   str      )    ->				Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_	:					Optional[Any]     =     self.image_processing_class(**self.image_processor_dict      )
													self.assertTrue(hasattr(_snake_case				,   '''do_normalize'''      )      )
													self.assertTrue(hasattr(_snake_case				,   '''do_convert_rgb'''      )      )

							def 			UpperCAmelCase__		(							self							:   Tuple      )    ->				Any:







													"""simple docstring"""
													UpperCAmelCase_	:					Dict     =     self.image_processor_tester.prepare_dummy_image()

													UpperCAmelCase_	:					Tuple     =     self.image_processing_class(**self.image_processor_dict      )
													UpperCAmelCase_	:					Union[str, Any]     =     20_48

													UpperCAmelCase_	:					Union[str, Any]     =     image_processor(_snake_case				,   return_tensors='''pt'''				,   max_patches=_snake_case      )
													self.assertTrue(torch.allclose(inputs.flattened_patches.mean()				,   torch.tensor(0.0_6_0_6      )				,   atol=1E-3				,   rtol=1E-3      )      )

							def 			UpperCAmelCase__		(							self							:   Optional[int]      )    ->				Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_	:					Any     =     self.image_processing_class(**self.image_processor_dict      )
													# create random PIL images
													UpperCAmelCase_	:					Union[str, Any]     =     prepare_image_inputs(self.image_processor_tester				,   equal_resolution=_snake_case      )
													for image in image_inputs:
																			self.assertIsInstance(_snake_case				,   Image.Image      )

													# Test not batched input
													UpperCAmelCase_	:					Tuple     =     (
													    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
													    * self.image_processor_tester.num_channels
													) + 2

													for max_patch in self.image_processor_tester.max_patches:
																			# Test not batched input
																			UpperCAmelCase_	:					List[Any]     =     image_processor(
																			    image_inputs[0]				,   return_tensors='''pt'''				,   max_patches=_snake_case      ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape				,   (1, max_patch, expected_hidden_dim)				,   )

																			# Test batched
																			UpperCAmelCase_	:					Optional[Any]     =     image_processor(
																			    _snake_case				,   return_tensors='''pt'''				,   max_patches=_snake_case      ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape				,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,   )

							def 			UpperCAmelCase__		(							self							:   str      )    ->				Tuple:







													"""simple docstring"""
													UpperCAmelCase_	:					Dict     =     self.image_processing_class(**self.image_processor_dict      )
													# create random PIL images
													UpperCAmelCase_	:					List[Any]     =     prepare_image_inputs(self.image_processor_tester				,   equal_resolution=_snake_case      )
													for image in image_inputs:
																			self.assertIsInstance(_snake_case				,   Image.Image      )

													# Test not batched input
													UpperCAmelCase_	:					Union[str, Any]     =     (
													    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
													    * self.image_processor_tester.num_channels
													) + 2

													UpperCAmelCase_	:					Dict     =     True

													for max_patch in self.image_processor_tester.max_patches:
																			# Test not batched input
																			with self.assertRaises(_snake_case      ):
																									UpperCAmelCase_	:					List[str]     =     image_processor(
																									    image_inputs[0]				,   return_tensors='''pt'''				,   max_patches=_snake_case      ).flattened_patches

																			UpperCAmelCase_	:					Optional[int]     =     '''Hello'''

																			UpperCAmelCase_	:					str     =     image_processor(
																			    image_inputs[0]				,   return_tensors='''pt'''				,   max_patches=_snake_case				,   header_text=_snake_case      ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape				,   (1, max_patch, expected_hidden_dim)				,   )

																			# Test batched
																			UpperCAmelCase_	:					Tuple     =     image_processor(
																			    _snake_case				,   return_tensors='''pt'''				,   max_patches=_snake_case				,   header_text=_snake_case      ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape				,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,   )

							def 			UpperCAmelCase__		(							self							:   List[str]      )    ->				Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_	:					List[str]     =     self.image_processing_class(**self.image_processor_dict      )
													# create random numpy tensors
													UpperCAmelCase_	:					int     =     prepare_image_inputs(self.image_processor_tester				,   equal_resolution=_snake_case				,   numpify=_snake_case      )
													for image in image_inputs:
																			self.assertIsInstance(_snake_case				,   np.ndarray      )

													UpperCAmelCase_	:					Tuple     =     (
													    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
													    * self.image_processor_tester.num_channels
													) + 2

													for max_patch in self.image_processor_tester.max_patches:
																			# Test not batched input
																			UpperCAmelCase_	:					Tuple     =     image_processor(
																			    image_inputs[0]				,   return_tensors='''pt'''				,   max_patches=_snake_case      ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape				,   (1, max_patch, expected_hidden_dim)				,   )

																			# Test batched
																			UpperCAmelCase_	:					Dict     =     image_processor(
																			    _snake_case				,   return_tensors='''pt'''				,   max_patches=_snake_case      ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape				,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,   )



							def 			UpperCAmelCase__		(							self							:   List[str]      )    ->				Any:







													"""simple docstring"""
													UpperCAmelCase_	:					Union[str, Any]     =     self.image_processing_class(**self.image_processor_dict      )
													# create random PyTorch tensors
													UpperCAmelCase_	:					Optional[Any]     =     prepare_image_inputs(self.image_processor_tester				,   equal_resolution=_snake_case				,   torchify=_snake_case      )
													for image in image_inputs:
																			self.assertIsInstance(_snake_case				,   torch.Tensor      )

													# Test not batched input
													UpperCAmelCase_	:					Dict     =     (
													    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
													    * self.image_processor_tester.num_channels
													) + 2

													for max_patch in self.image_processor_tester.max_patches:
																			# Test not batched input
																			UpperCAmelCase_	:					Tuple     =     image_processor(
																			    image_inputs[0]				,   return_tensors='''pt'''				,   max_patches=_snake_case      ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape				,   (1, max_patch, expected_hidden_dim)				,   )

																			# Test batched
																			UpperCAmelCase_	:					Tuple     =     image_processor(
																			    _snake_case				,   return_tensors='''pt'''				,   max_patches=_snake_case      ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape				,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,   )





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11				,						reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."				,						)
@require_torch
@require_vision
class     __a					(lowerCamelCase				,						unittest.TestCase   ):
							__a    :      int  	=				PixaStructImageProcessor if is_vision_available() else None

							def 			UpperCAmelCase__		(							self							:   Optional[Any]      )    ->				Dict:







													"""simple docstring"""
													UpperCAmelCase_	:					List[str]     =     PixaStructImageProcessingTester(self				,   num_channels=4      )
													UpperCAmelCase_	:					List[Any]     =     3

							@property
							def 			UpperCAmelCase__		(							self							:   Dict      )    ->				Union[str, Any]:







													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()

							def 			UpperCAmelCase__		(							self							:   Dict      )    ->				str:







													"""simple docstring"""
													UpperCAmelCase_	:					Any     =     self.image_processing_class(**self.image_processor_dict      )
													self.assertTrue(hasattr(_snake_case				,   '''do_normalize'''      )      )
													self.assertTrue(hasattr(_snake_case				,   '''do_convert_rgb'''      )      )



							def 			UpperCAmelCase__		(							self							:   Any      )    ->				List[str]:







													"""simple docstring"""
													UpperCAmelCase_	:					Tuple     =     self.image_processing_class(**self.image_processor_dict      )
													# create random PIL images
													UpperCAmelCase_	:					int     =     prepare_image_inputs(self.image_processor_tester				,   equal_resolution=_snake_case      )
													for image in image_inputs:
																			self.assertIsInstance(_snake_case				,   Image.Image      )

													# Test not batched input
													UpperCAmelCase_	:					Tuple     =     (
													    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
													    * (self.image_processor_tester.num_channels - 1)
													) + 2

													for max_patch in self.image_processor_tester.max_patches:
																			# Test not batched input
																			UpperCAmelCase_	:					Tuple     =     image_processor(
																			    image_inputs[0]				,   return_tensors='''pt'''				,   max_patches=_snake_case      ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape				,   (1, max_patch, expected_hidden_dim)				,   )

																			# Test batched
																			UpperCAmelCase_	:					Tuple     =     image_processor(
																			    _snake_case				,   return_tensors='''pt'''				,   max_patches=_snake_case      ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape				,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,   )







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def     __lowerCAmelCase			(			a__						,    a__						,    a__=1024						,    a__=1024						,    a__=False						,    **a__  )       ->  Optional[Any]:
				__a										=    AutoTokenizer.from_pretrained(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''train'''						,    **a__  )
				__a										=    tok.pad_token_id

				def get_lens(a__  ):
								__a										=    tqdm(
								    DataLoader(a__						,    batch_size=512						,    num_workers=8						,    shuffle=a__						,    collate_fn=ds.collate_fn  )						,    desc=str(ds.len_file  )						,    )
								__a										=    []
								for batch in dl:
												__a										=    batch['''input_ids'''].ne(a__  ).sum(1  ).tolist()
												__a										=    batch['''labels'''].ne(a__  ).sum(1  ).tolist()
												if consider_target:
																for src, tgt in zip(a__						,    a__  ):
																				max_lens.append(max(a__						,    a__  )  )
												else:
																max_lens.extend(a__  )
								return max_lens

				__a										=    get_lens(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''val'''						,    **a__  )
				__a										=    get_lens(a__  )
				pickle_save(a__						,    train_ds.len_file  )
				pickle_save(a__						,    val_ds.len_file  )


if __name__ == "__main__":
					fire.Fire(save_len_file)