import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


SCREAMING_SNAKE_CASE__		:					int						=					"bert-base-cased"
SCREAMING_SNAKE_CASE__		:					List[Any]						=					"google/pegasus-xsum"
SCREAMING_SNAKE_CASE__		:					Union[str, Any]						=					[" Sam ate lunch today.", "Sams lunch ingredients."]
SCREAMING_SNAKE_CASE__		:					Any						=					["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
SCREAMING_SNAKE_CASE__		:					str						=					"patrickvonplaten/t5-tiny-random"
SCREAMING_SNAKE_CASE__		:					Tuple						=					"sshleifer/bart-tiny-random"
SCREAMING_SNAKE_CASE__		:					Any						=					"sshleifer/tiny-mbart"
SCREAMING_SNAKE_CASE__		:					Optional[Any]						=					"sshleifer/tiny-marian-en-de"







def     _a				(      lowercase__  :			Path     ,  lowercase__  :			list )     ->   Dict:







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'\n'.join(lowercase__ )
     Path(lowercase__ ).open('w' ).writelines(lowercase__ )







def     _a				(      lowercase__  :			List[Any] )     ->   Optional[int]:







     '''simple docstring'''


     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(lowercase__     ,  f'''{split}.source''' )     ,  lowercase__ )
          _dump_articles(os.path.join(lowercase__     ,  f'''{split}.target''' )     ,  lowercase__ )
     return tmp_dir







class       snake_case					(      UpperCamelCase_						):




  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]     , )
  @slow
  def 	__lowercase(      self     :	Optional[Any]     , a_     :	Optional[int]				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		AutoTokenizer.from_pretrained(a_				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
       SCREAMING_SNAKE_CASE__				:				int			=		max(len(tokenizer.encode(a_				)				) for a in ARTICLES				)
       SCREAMING_SNAKE_CASE__				:				str			=		max(len(tokenizer.encode(a_				)				) for a in SUMMARIES				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		4
       SCREAMING_SNAKE_CASE__				:				Dict			=		8
       assert max_len_target > max_src_len  # Will be truncated
       assert max_len_source > max_src_len  # Will be truncated
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		SeqaSeqDataset(
           a_     , data_dir=a_     , type_path='train'     , max_source_length=a_     , max_target_length=a_     , src_lang=a_     , tgt_lang=a_     , )
       SCREAMING_SNAKE_CASE__				:				Any			=		DataLoader(a_     , batch_size=2     , collate_fn=train_dataset.collate_fn				)
       for batch in dataloader:
            assert isinstance(a_     , a_				)
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_src_len
            # show that targets are the same len
            assert batch["labels"].shape[1] == max_tgt_len
            if tok_name != MBART_TINY:
                 continue
            # check language codes in correct place
            SCREAMING_SNAKE_CASE__				:				Tuple			=		shift_tokens_right(batch['labels']     , tokenizer.pad_token_id				)
            assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
            assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

            break  # No need to test every batch




  @parameterized.expand([BART_TINY, BERT_BASE_CASED]				)
  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	List[str]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		AutoTokenizer.from_pretrained(a_				)
       SCREAMING_SNAKE_CASE__				:				int			=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
       SCREAMING_SNAKE_CASE__				:				Any			=		max(len(tokenizer.encode(a_				)				) for a in ARTICLES				)
       SCREAMING_SNAKE_CASE__				:				Any			=		max(len(tokenizer.encode(a_				)				) for a in SUMMARIES				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		4
       SCREAMING_SNAKE_CASE__				:				str			=		LegacySeqaSeqDataset(
           a_     , data_dir=a_     , type_path='train'     , max_source_length=20     , max_target_length=a_     , )
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		DataLoader(a_     , batch_size=2     , collate_fn=train_dataset.collate_fn				)
       for batch in dataloader:
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_len_source
            assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
            # show that targets were truncated
            assert batch["labels"].shape[1] == trunc_target  # Truncated
            assert max_len_target > trunc_target  # Truncated
            break  # No need to test every batch




  def 	__lowercase(      self     :	Optional[int]				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'				)

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
       SCREAMING_SNAKE_CASE__				:				Any			=		tmp_dir.joinpath('train.source'				).open().readlines()
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
       pack_data_dir(a_     , a_     , 128     , a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{x.name for x in tmp_dir.iterdir()}
       SCREAMING_SNAKE_CASE__				:				Dict			=		{x.name for x in save_dir.iterdir()}
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		save_dir.joinpath('train.source'				).open().readlines()
       # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
       # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
       assert len(a_				) < len(a_				)
       assert len(a_				) == 1
       assert len(packed_examples[0]				) == sum(len(a_				) for x in orig_examples				)
       assert orig_paths == new_paths




  @pytest.mark.skipif(not FAIRSEQ_AVAILABLE     , reason='This test requires fairseq'				)
  def 	__lowercase(      self     :	int				)->       Union[str, Any]:




       """simple docstring"""
       if not FAIRSEQ_AVAILABLE:
            return
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self._get_dataset(max_len=64				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		64
       SCREAMING_SNAKE_CASE__				:				int			=		ds.make_dynamic_sampler(a_     , required_batch_size_multiple=a_				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		[len(a_				) for x in batch_sampler]
       assert len(set(a_				)				) > 1  # it's not dynamic batch size if every batch is the same length
       assert sum(a_				) == len(a_				)  # no dropped or added examples
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		DataLoader(a_     , batch_sampler=a_     , collate_fn=ds.collate_fn     , num_workers=2				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]
       for batch in data_loader:
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		batch['input_ids'].shape
            SCREAMING_SNAKE_CASE__				:				int			=		src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            SCREAMING_SNAKE_CASE__				:				Any			=		np.product(batch['input_ids'].shape				)
            num_src_per_batch.append(a_				)
            if num_src_tokens > (max_tokens * 1.1):
                 failures.append(a_				)
       assert num_src_per_batch[0] == max(a_				)
       if failures:
            raise AssertionError(F'''too many tokens in {len(a_				)} batches'''				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		self._get_dataset(max_len=512				)
       SCREAMING_SNAKE_CASE__				:				int			=		2
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		ds.make_sortish_sampler(a_     , shuffle=a_				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		DataLoader(a_     , batch_size=a_     , collate_fn=ds.collate_fn     , num_workers=2				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		DataLoader(a_     , batch_size=a_     , collate_fn=ds.collate_fn     , num_workers=2     , sampler=a_				)

       SCREAMING_SNAKE_CASE__				:				str			=		tokenizer.pad_token_id

       def count_pad_tokens(a_     :	int     , a_     :	Dict="input_ids"				):
            return [batch[k].eq(a_				).sum().item() for batch in data_loader]

       assert sum(count_pad_tokens(a_     , k='labels'				)				) < sum(count_pad_tokens(a_     , k='labels'				)				)
       assert sum(count_pad_tokens(a_				)				) < sum(count_pad_tokens(a_				)				)
       assert len(a_				) == len(a_				)




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	Union[str, Any]=1000     , a_     :	Tuple=128				)->       Optional[int]:




       """simple docstring"""
       if os.getenv('USE_REAL_DATA'     , a_				):
            SCREAMING_SNAKE_CASE__				:				Tuple			=		'examples/seq2seq/wmt_en_ro'
            SCREAMING_SNAKE_CASE__				:				Tuple			=		max_len * 2 * 64
            if not Path(a_				).joinpath('train.len'				).exists():
                 save_len_file(a_     , a_				)
       else:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		'examples/seq2seq/test_data/wmt_en_ro'
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		max_len * 4
            save_len_file(a_     , a_				)

       SCREAMING_SNAKE_CASE__				:				str			=		AutoTokenizer.from_pretrained(a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		SeqaSeqDataset(
           a_     , data_dir=a_     , type_path='train'     , max_source_length=a_     , max_target_length=a_     , n_obs=a_     , )
       return ds, max_tokens, tokenizer




  def 	__lowercase(      self     :	List[Any]				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		self._get_dataset()
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		set(DistributedSortishSampler(a_     , 256     , num_replicas=2     , rank=0     , add_extra_examples=a_				)				)
       SCREAMING_SNAKE_CASE__				:				Any			=		set(DistributedSortishSampler(a_     , 256     , num_replicas=2     , rank=1     , add_extra_examples=a_				)				)
       assert idsa.intersection(a_				) == set()




  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]     , )
  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	Union[str, Any]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		AutoTokenizer.from_pretrained(a_     , use_fast=a_				)
       if tok_name == MBART_TINY:
            SCREAMING_SNAKE_CASE__				:				Any			=		SeqaSeqDataset(
                a_     , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)     , type_path='train'     , max_source_length=4     , max_target_length=8     , src_lang='EN'     , tgt_lang='FR'     , )
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		SeqaSeqDataset(
                a_     , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)     , type_path='train'     , max_source_length=4     , max_target_length=8     , )
            SCREAMING_SNAKE_CASE__				:				Tuple			=		train_dataset.dataset_kwargs
            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
            assert len(a_				) == 1 if tok_name == BART_TINY else len(a_				) == 0




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:
      SCREAMING_SNAKE_CASE__		:					Any						=					False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor

class       snake_case					(      unittest.TestCase						):




  def __init__(      self     :	List[Any]     , a_     :	Optional[int]     , a_     :	Dict=7     , a_     :	Any=3     , a_     :	Any=18     , a_     :	int=30     , a_     :	int=400     , a_     :	List[Any]=None     , a_     :	int=True     , a_     :	int=True     , a_     :	Dict=None     , )->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		size if size is not None else {'height': 20, 'width': 20}
       SCREAMING_SNAKE_CASE__				:				str			=		parent
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		batch_size
       SCREAMING_SNAKE_CASE__				:				Any			=		num_channels
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image_size
       SCREAMING_SNAKE_CASE__				:				List[str]			=		min_resolution
       SCREAMING_SNAKE_CASE__				:				Dict			=		max_resolution
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		do_normalize
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		do_convert_rgb
       SCREAMING_SNAKE_CASE__				:				List[str]			=		[512, 1024, 2048, 4096]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		patch_size if patch_size is not None else {'height': 16, 'width': 16}




  def 	__lowercase(      self     :	Optional[Any]				)->       str:




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




  def 	__lowercase(      self     :	Dict				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
       SCREAMING_SNAKE_CASE__				:				str			=		Image.open(requests.get(a_     , stream=a_				).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       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PixaStructImageProcessor if is_vision_available() else None




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		PixaStructImageProcessingTester(self				)




  @property
  def 	__lowercase(      self     :	Dict				)->       Tuple:




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




  def 	__lowercase(      self     :	Any				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(a_     , 'do_normalize'				)				)
       self.assertTrue(hasattr(a_     , 'do_convert_rgb'				)				)




  def 	__lowercase(      self     :	List[Any]				)->       Tuple:




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

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.image_processing_class(**self.image_processor_dict				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		2048

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_processor(a_     , return_tensors='pt'     , max_patches=a_				)
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()     , torch.tensor(0.0606				)     , atol=1e-3     , rtol=1e-3				)				)




  def 	__lowercase(      self     :	Any				)->       Tuple:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_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
       SCREAMING_SNAKE_CASE__				:				str			=		(
           (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
            SCREAMING_SNAKE_CASE__				:				List[str]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

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




  def 	__lowercase(      self     :	Any				)->       Any:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_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
       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       SCREAMING_SNAKE_CASE__				:				int			=		True

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

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		'Hello'

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_     , header_text=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

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




  def 	__lowercase(      self     :	List[Any]				)->       Dict:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_     , numpify=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , np.ndarray				)

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

            # Test batched
            SCREAMING_SNAKE_CASE__				:				int			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




  def 	__lowercase(      self     :	str				)->       Optional[Any]:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Any			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		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
       SCREAMING_SNAKE_CASE__				:				Any			=		(
           (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
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).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       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PixaStructImageProcessor if is_vision_available() else None




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		PixaStructImageProcessingTester(self     , num_channels=4				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		3




  @property
  def 	__lowercase(      self     :	Any				)->       Any:




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




  def 	__lowercase(      self     :	Dict				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(a_     , 'do_normalize'				)				)
       self.assertTrue(hasattr(a_     , 'do_convert_rgb'				)				)




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




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				str			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				Tuple			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				Dict			=		(
           (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
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

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



