import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
      from test_module.custom_tokenization_fast import CustomTokenizerFast

class       snake_case					(      unittest.TestCase						):




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		mock.Mock()
       SCREAMING_SNAKE_CASE__				:				int			=		500
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		{}
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		HTTPError
       SCREAMING_SNAKE_CASE__				:				List[str]			=		{}

       # Download this model to make sure it's in the cache.
       SCREAMING_SNAKE_CASE__				:				List[str]			=		BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'				)

       # Under the mock environment we get a 500 error when trying to reach the tokenizer.
       with mock.patch('requests.Session.request'     , return_value=a_				) as mock_head:
            SCREAMING_SNAKE_CASE__				:				Tuple			=		BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'				)
            # This check we did call the fake head request
            mock_head.assert_called()




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		mock.Mock()
       SCREAMING_SNAKE_CASE__				:				List[str]			=		500
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		{}
       SCREAMING_SNAKE_CASE__				:				List[str]			=		HTTPError
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		{}

       # Download this model to make sure it's in the cache.
       SCREAMING_SNAKE_CASE__				:				List[str]			=		GPTaTokenizerFast.from_pretrained('gpt2'				)

       # Under the mock environment we get a 500 error when trying to reach the tokenizer.
       with mock.patch('requests.Session.request'     , return_value=a_				) as mock_head:
            SCREAMING_SNAKE_CASE__				:				Any			=		GPTaTokenizerFast.from_pretrained('gpt2'				)
            # This check we did call the fake head request
            mock_head.assert_called()




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




       """simple docstring"""
       # This test is for deprecated behavior and can be removed in v5
       try:
            SCREAMING_SNAKE_CASE__				:				str			=		tempfile.mktemp()
            with open(a_     , 'wb'				) as f:
                 http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'     , a_				)

            SCREAMING_SNAKE_CASE__				:				List[str]			=		AlbertTokenizer.from_pretrained(a_				)
       finally:
            os.remove(a_				)

       # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
       # the current folder and have the right name.
       if os.path.isfile('tokenizer.json'				):
            # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
            return
       try:
            with open('tokenizer.json'     , 'wb'				) as f:
                 http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json'     , a_				)
            SCREAMING_SNAKE_CASE__				:				int			=		AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
            # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
            self.assertEqual(tokenizer.vocab_size     , 1000				)
            # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

       finally:
            os.remove('tokenizer.json'				)




  def 	__lowercase(      self     :	Any				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'				)




@is_staging_test
class       snake_case					(      unittest.TestCase						):
  lowercase_			=     ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']




  @classmethod
  def 	__lowercase(      cls     :	Tuple				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		TOKEN
       HfFolder.save_token(a_				)




  @classmethod
  def 	__lowercase(      cls     :	Any				)->       Dict:




       """simple docstring"""
       try:
            delete_repo(token=cls._token     , repo_id='test-tokenizer'				)
       except HTTPError:
            pass

       try:
            delete_repo(token=cls._token     , repo_id='valid_org/test-tokenizer-org'				)
       except HTTPError:
            pass

       try:
            delete_repo(token=cls._token     , repo_id='test-dynamic-tokenizer'				)
       except HTTPError:
            pass




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




       """simple docstring"""
       with tempfile.TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		os.path.join(a_     , 'vocab.txt'				)
            with open(a_     , 'w'     , encoding='utf-8'				) as vocab_writer:
                 vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]				)				)
            SCREAMING_SNAKE_CASE__				:				Tuple			=		BertTokenizer(a_				)

       tokenizer.push_to_hub('test-tokenizer'     , use_auth_token=self._token				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		BertTokenizer.from_pretrained(F'''{USER}/test-tokenizer'''				)
       self.assertDictEqual(new_tokenizer.vocab     , tokenizer.vocab				)

       # Reset repo
       delete_repo(token=self._token     , repo_id='test-tokenizer'				)

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
            tokenizer.save_pretrained(a_     , repo_id='test-tokenizer'     , push_to_hub=a_     , use_auth_token=self._token				)

       SCREAMING_SNAKE_CASE__				:				Dict			=		BertTokenizer.from_pretrained(F'''{USER}/test-tokenizer'''				)
       self.assertDictEqual(new_tokenizer.vocab     , tokenizer.vocab				)




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




       """simple docstring"""
       with tempfile.TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE__				:				Tuple			=		os.path.join(a_     , 'vocab.txt'				)
            with open(a_     , 'w'     , encoding='utf-8'				) as vocab_writer:
                 vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]				)				)
            SCREAMING_SNAKE_CASE__				:				Dict			=		BertTokenizer(a_				)

       tokenizer.push_to_hub('valid_org/test-tokenizer-org'     , use_auth_token=self._token				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'				)
       self.assertDictEqual(new_tokenizer.vocab     , tokenizer.vocab				)

       # Reset repo
       delete_repo(token=self._token     , repo_id='valid_org/test-tokenizer-org'				)

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
            tokenizer.save_pretrained(
                a_     , repo_id='valid_org/test-tokenizer-org'     , push_to_hub=a_     , use_auth_token=self._token				)

       SCREAMING_SNAKE_CASE__				:				int			=		BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'				)
       self.assertDictEqual(new_tokenizer.vocab     , tokenizer.vocab				)




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




       """simple docstring"""
       CustomTokenizer.register_for_auto_class()
       with tempfile.TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		os.path.join(a_     , 'vocab.txt'				)
            with open(a_     , 'w'     , encoding='utf-8'				) as vocab_writer:
                 vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]				)				)
            SCREAMING_SNAKE_CASE__				:				int			=		CustomTokenizer(a_				)

       # No fast custom tokenizer
       tokenizer.push_to_hub('test-dynamic-tokenizer'     , use_auth_token=self._token				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		AutoTokenizer.from_pretrained(F'''{USER}/test-dynamic-tokenizer'''     , trust_remote_code=a_				)
       # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
       self.assertEqual(tokenizer.__class__.__name__     , 'CustomTokenizer'				)

       # Fast and slow custom tokenizer
       CustomTokenizerFast.register_for_auto_class()
       with tempfile.TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE__				:				List[str]			=		os.path.join(a_     , 'vocab.txt'				)
            with open(a_     , 'w'     , encoding='utf-8'				) as vocab_writer:
                 vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]				)				)

            SCREAMING_SNAKE_CASE__				:				int			=		BertTokenizerFast.from_pretrained(a_				)
            bert_tokenizer.save_pretrained(a_				)
            SCREAMING_SNAKE_CASE__				:				Any			=		CustomTokenizerFast.from_pretrained(a_				)

       tokenizer.push_to_hub('test-dynamic-tokenizer'     , use_auth_token=self._token				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		AutoTokenizer.from_pretrained(F'''{USER}/test-dynamic-tokenizer'''     , trust_remote_code=a_				)
       # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
       self.assertEqual(tokenizer.__class__.__name__     , 'CustomTokenizerFast'				)
       SCREAMING_SNAKE_CASE__				:				Any			=		AutoTokenizer.from_pretrained(
           F'''{USER}/test-dynamic-tokenizer'''     , use_fast=a_     , trust_remote_code=a_				)
       # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
       self.assertEqual(tokenizer.__class__.__name__     , 'CustomTokenizer'				)




class       snake_case					(      unittest.TestCase						):




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		Trie()
       trie.add('Hello 友達'				)
       self.assertEqual(trie.data     , {'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}}				)
       trie.add('Hello'				)
       trie.data
       self.assertEqual(trie.data     , {'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}}				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		Trie()
       self.assertEqual(trie.split('[CLS] This is a extra_id_100'				)     , ['[CLS] This is a extra_id_100']				)
       trie.add('[CLS]'				)
       trie.add('extra_id_1'				)
       trie.add('extra_id_100'				)
       self.assertEqual(trie.split('[CLS] This is a extra_id_100'				)     , ['[CLS]', ' This is a ', 'extra_id_100']				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		Trie()
       trie.add('A'				)
       self.assertEqual(trie.split('ABC'				)     , ['A', 'BC']				)
       self.assertEqual(trie.split('BCA'				)     , ['BC', 'A']				)




  def 	__lowercase(      self     :	Tuple				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		Trie()
       trie.add('TOKEN]'				)
       trie.add('[SPECIAL_TOKEN]'				)
       self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'				)     , ['This is something ', '[SPECIAL_TOKEN]']				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		Trie()
       trie.add('A'				)
       trie.add('P'				)
       trie.add('[SPECIAL_TOKEN]'				)
       self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'				)     , ['This is something ', '[SPECIAL_TOKEN]']				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		Trie()
       trie.add('AB'				)
       trie.add('B'				)
       trie.add('C'				)
       self.assertEqual(trie.split('ABC'				)     , ['AB', 'C']				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		Trie()
       trie.add('ABC'				)
       trie.add('B'				)
       trie.add('CD'				)
       self.assertEqual(trie.split('ABCD'				)     , ['ABC', 'D']				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		Trie()
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		trie.cut_text('ABC'     , [0, 0, 2, 1, 2, 3]				)
       self.assertEqual(a_     , ['AB', 'C']				)




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)     , )



