
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:
					A     :	Optional[Any]    = False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor







class 			__A( unittest.TestCase			):




				def __init__(     self		,     _snake_case		,     _snake_case=7		,     _snake_case=3		,     _snake_case=18		,     _snake_case=30		,     _snake_case=400		,     _snake_case=None		,     _snake_case=True		,     _snake_case=True		,     _snake_case=None		,     )    ->      Tuple:


								'''simple docstring'''




								__a										=    size if size is not None else {'''height''': 20, '''width''': 20}
								__a										=    parent
								__a										=    batch_size
								__a										=    num_channels
								__a										=    image_size
								__a										=    min_resolution
								__a										=    max_resolution
								__a										=    size
								__a										=    do_normalize
								__a										=    do_convert_rgb
								__a										=    [512, 1_024, 2_048, 4_096]
								__a										=    patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
								__a										=    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( a  ,   unittest.TestCase			):
				snake_case_						   =    PixaStructImageProcessor if is_vision_available() else None




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


								'''simple docstring'''




								__a										=    PixaStructImageProcessingTester(self				)




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


								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(_snake_case		,     '''do_normalize'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_convert_rgb'''				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.image_processor_tester.prepare_dummy_image()

								__a										=    self.image_processing_class(**self.image_processor_dict				)
								__a										=    2_048

								__a										=    image_processor(_snake_case		,     return_tensors='''pt'''		,     max_patches=_snake_case				)
								self.assertTrue(torch.allclose(inputs.flattened_patches.mean()		,     torch.tensor(0.0606				)		,     atol=1E-3		,     rtol=1E-3				)				)




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


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								# create random PIL images
								__a										=    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
								__a										=    (
								    (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
												__a										=    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
												__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								# create random PIL images
								__a										=    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
								__a										=    (
								    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
								    * self.image_processor_tester.num_channels
								) + 2

								__a										=    True

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

												__a										=    '''Hello'''

												__a										=    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
												__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								# create random numpy tensors
								__a										=    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				)

								__a										=    (
								    (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
												__a										=    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
												__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								# create random PyTorch tensors
								__a										=    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
								__a										=    (
								    (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
												__a										=    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
												__a										=    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( a  ,   unittest.TestCase			):
				snake_case_						   =    PixaStructImageProcessor if is_vision_available() else None




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    PixaStructImageProcessingTester(self		,     num_channels=4				)
								__a										=    3




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


								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(_snake_case		,     '''do_normalize'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_convert_rgb'''				)				)




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


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								# create random PIL images
								__a										=    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
								__a										=    (
								    (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
												__a										=    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
												__a										=    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)