


'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
    import jax.numpy as jnp






class        lowerCAmelCase_  (     UpperCAmelCase	):






       def __get__( self			,     _UpperCamelCase			,     _UpperCamelCase=None   )->      str:
            # See docs.python.org/3/howto/descriptor.html#properties
            if obj is None:
                 return self
            if self.fget is None:
                 raise AttributeError('unreadable attribute'   )
            _A				=							'__cached_' + self.fget.__name__
            _A				=							getattr(_UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )
            if cached is None:
                 _A				=							self.fget(_UpperCamelCase   )
                 setattr(_UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )
            return cached






def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[Any]		)					->	Tuple:







     """simple docstring"""
     _A				=							val.lower()
     if val in {"y", "yes", "t", "true", "on", "1"}:
          return 1
     if val in {"n", "no", "f", "false", "off", "0"}:
          return 0
     raise ValueError(F'invalid truth value {val!r}'		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[Any]		)					->	str:







     """simple docstring"""
     if is_torch_fx_proxy(__UpperCamelCase		):
          return True
     if is_torch_available():
          import torch

          if isinstance(__UpperCamelCase							,    torch.Tensor		):
               return True
     if is_tf_available():
          import tensorflow as tf

          if isinstance(__UpperCamelCase							,    tf.Tensor		):
               return True

     if is_flax_available():
          import jax.numpy as jnp
          from jax.core import Tracer

          if isinstance(__UpperCamelCase							,    (jnp.ndarray, Tracer)		):
               return True

     return isinstance(__UpperCamelCase							,    np.ndarray		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       str		)					->	Dict:







     """simple docstring"""
     return isinstance(__UpperCamelCase							,    np.ndarray		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Union[str, Any]		)					->	List[Any]:







     """simple docstring"""
     return _is_numpy(__UpperCamelCase		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Any		)					->	Any:







     """simple docstring"""
     import torch

     return isinstance(__UpperCamelCase							,    torch.Tensor		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       int		)					->	Any:







     """simple docstring"""
     return False if not is_torch_available() else _is_torch(__UpperCamelCase		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[str]		)					->	Optional[Any]:







     """simple docstring"""
     import torch

     return isinstance(__UpperCamelCase							,    torch.device		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[Any]		)					->	Tuple:







     """simple docstring"""
     return False if not is_torch_available() else _is_torch_device(__UpperCamelCase		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Dict		)					->	Dict:







     """simple docstring"""
     import torch

     if isinstance(__UpperCamelCase							,    __UpperCamelCase		):
          if hasattr(__UpperCamelCase							,    __UpperCamelCase		):
               _A				=							getattr(__UpperCamelCase							,    __UpperCamelCase		)
          else:
               return False
     return isinstance(__UpperCamelCase							,    torch.dtype		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[int]		)					->	List[str]:







     """simple docstring"""
     return False if not is_torch_available() else _is_torch_dtype(__UpperCamelCase		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[str]		)					->	Dict:







     """simple docstring"""
     import tensorflow as tf

     return isinstance(__UpperCamelCase							,    tf.Tensor		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[int]		)					->	str:







     """simple docstring"""
     return False if not is_tf_available() else _is_tensorflow(__UpperCamelCase		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Union[str, Any]		)					->	str:







     """simple docstring"""
     import tensorflow as tf

     # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
     if hasattr(__UpperCamelCase							,    'is_symbolic_tensor'		):
          return tf.is_symbolic_tensor(__UpperCamelCase		)
     return type(__UpperCamelCase		) == tf.Tensor

def 					lowerCamelCase_ ( __UpperCamelCase					:       int		)					->	List[Any]:







     """simple docstring"""
     return False if not is_tf_available() else _is_tf_symbolic_tensor(__UpperCamelCase		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Tuple		)					->	Union[str, Any]:







     """simple docstring"""
     import jax.numpy as jnp  # noqa: F811

     return isinstance(__UpperCamelCase							,    jnp.ndarray		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[Any]		)					->	List[str]:







     """simple docstring"""
     return False if not is_flax_available() else _is_jax(__UpperCamelCase		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[Any]		)					->	Any:







     """simple docstring"""
     if isinstance(__UpperCamelCase							,    (dict, UserDict)		):
          return {k: to_py_obj(__UpperCamelCase		) for k, v in obj.items()}
     elif isinstance(__UpperCamelCase							,    (list, tuple)		):
          return [to_py_obj(__UpperCamelCase		) for o in obj]
     elif is_tf_tensor(__UpperCamelCase		):
          return obj.numpy().tolist()
     elif is_torch_tensor(__UpperCamelCase		):
          return obj.detach().cpu().tolist()
     elif is_jax_tensor(__UpperCamelCase		):
          return np.asarray(__UpperCamelCase		).tolist()
     elif isinstance(__UpperCamelCase							,    (np.ndarray, np.number)		):  # tolist also works on 0d np arrays
          return obj.tolist()
     else:
          return obj

def 					lowerCamelCase_ ( __UpperCamelCase					:       Dict		)					->	Union[str, Any]:







     """simple docstring"""
     if isinstance(__UpperCamelCase							,    (dict, UserDict)		):
          return {k: to_numpy(__UpperCamelCase		) for k, v in obj.items()}
     elif isinstance(__UpperCamelCase							,    (list, tuple)		):
          return np.array(__UpperCamelCase		)
     elif is_tf_tensor(__UpperCamelCase		):
          return obj.numpy()
     elif is_torch_tensor(__UpperCamelCase		):
          return obj.detach().cpu().numpy()
     elif is_jax_tensor(__UpperCamelCase		):
          return np.asarray(__UpperCamelCase		)
     else:
          return obj






class        lowerCAmelCase_  (     UpperCAmelCase	):






       def   UpperCamelCase						( self   )->      int:
            _A				=							fields(self   )

            # Safety and consistency checks
            if not len(_UpperCamelCase   ):
                 raise ValueError(F'{self.__class__.__name__} has no fields.'   )
            if not all(field.default is None for field in class_fields[1:]   ):
                 raise ValueError(F'{self.__class__.__name__} should not have more than one required field.'   )

            _A				=							getattr(self			,     class_fields[0].name   )
            _A				=							all(getattr(self			,     field.name   ) is None for field in class_fields[1:]   )

            if other_fields_are_none and not is_tensor(_UpperCamelCase   ):
                 if isinstance(_UpperCamelCase			,     _UpperCamelCase   ):
                      _A				=							first_field.items()
                      _A				=							True
                 else:
                      try:
                           _A				=							iter(_UpperCamelCase   )
                           _A				=							True
                      except TypeError:
                           _A				=							False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
                 if first_field_iterator:
                      for idx, element in enumerate(_UpperCamelCase   ):
                           if (
                               not isinstance(_UpperCamelCase			,     (list, tuple)   )
                               or not len(_UpperCamelCase   ) == 2
                               or not isinstance(element[0]			,     _UpperCamelCase   )
                           ):
                                if idx == 0:
                                     # If we do not have an iterator of key/values, set it as attribute
                                     _A				=							first_field
                                else:
                                     # If we have a mixed iterator, raise an error
                                     raise ValueError(
                                         F'Cannot set key/value for {element}. It needs to be a tuple (key, value).'   )
                                break
                           setattr(self			,     element[0]			,     element[1]   )
                           if element[1] is not None:
                                _A				=							element[1]
                 elif first_field is not None:
                      _A				=							first_field
            else:
                 for field in class_fields:
                      _A				=							getattr(self			,     field.name   )
                      if v is not None:
                           _A				=							v






       def __delitem__( self			,     *_UpperCamelCase			,     **_UpperCamelCase   )->      Optional[int]:
            raise Exception(F'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'   )






       def   UpperCamelCase						( self			,     *_UpperCamelCase			,     **_UpperCamelCase   )->      Union[str, Any]:
            raise Exception(F'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'   )






       def   UpperCamelCase						( self			,     *_UpperCamelCase			,     **_UpperCamelCase   )->      str:
            raise Exception(F'You cannot use ``pop`` on a {self.__class__.__name__} instance.'   )






       def   UpperCamelCase						( self			,     *_UpperCamelCase			,     **_UpperCamelCase   )->      Dict:
            raise Exception(F'You cannot use ``update`` on a {self.__class__.__name__} instance.'   )






       def __getitem__( self			,     _UpperCamelCase   )->      Dict:
            if isinstance(_UpperCamelCase			,     _UpperCamelCase   ):
                 _A				=							dict(self.items()   )
                 return inner_dict[k]
            else:
                 return self.to_tuple()[k]






       def __setattr__( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      List[str]:
            if name in self.keys() and value is not None:
                 # Don't call self.__setitem__ to avoid recursion errors
                 super().__setitem__(_UpperCamelCase			,     _UpperCamelCase   )
            super().__setattr__(_UpperCamelCase			,     _UpperCamelCase   )






       def __setitem__( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      Tuple:
            # Will raise a KeyException if needed
            super().__setitem__(_UpperCamelCase			,     _UpperCamelCase   )
            # Don't call self.__setattr__ to avoid recursion errors
            super().__setattr__(_UpperCamelCase			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Tuple[Any]:
            return tuple(self[k] for k in self.keys()   )






class        lowerCAmelCase_  (     UpperCAmelCase      ,							UpperCAmelCase	):






       @classmethod
       def   UpperCamelCase						( cls			,     _UpperCamelCase   )->      Any:
            raise ValueError(
                F'{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()   )}'   )






class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					='longest'
       __UpperCAmelCase					='max_length'
       __UpperCAmelCase					='do_not_pad'






class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					='pt'
       __UpperCAmelCase					='tf'
       __UpperCAmelCase					='np'
       __UpperCAmelCase					='jax'






class        lowerCAmelCase_  :






       def __init__( self			,     _UpperCamelCase   )->      List[Any]:
            _A				=							context_managers
            _A				=							ExitStack()






       def __enter__( self   )->      List[Any]:
            for context_manager in self.context_managers:
                 self.stack.enter_context(_UpperCamelCase   )






       def __exit__( self			,     *_UpperCamelCase			,     **_UpperCamelCase   )->      List[str]:
            self.stack.__exit__(*_UpperCamelCase			,     **_UpperCamelCase   )






def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[int]		)					->	List[Any]:







     """simple docstring"""
     _A				=							infer_framework(__UpperCamelCase		)
     if framework == "tf":
          _A				=							inspect.signature(model_class.call		)  # TensorFlow models
     elif framework == "pt":
          _A				=							inspect.signature(model_class.forward		)  # PyTorch models
     else:
          _A				=							inspect.signature(model_class.__call__		)  # Flax models

     for p in signature.parameters:
          if p == "return_loss" and signature.parameters[p].default is True:
               return True

     return False

def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[int]		)					->	Union[str, Any]:







     """simple docstring"""
     _A				=							model_class.__name__
     _A				=							infer_framework(__UpperCamelCase		)
     if framework == "tf":
          _A				=							inspect.signature(model_class.call		)  # TensorFlow models
     elif framework == "pt":
          _A				=							inspect.signature(model_class.forward		)  # PyTorch models
     else:
          _A				=							inspect.signature(model_class.__call__		)  # Flax models

     if "QuestionAnswering" in model_name:
          return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
     else:
          return [p for p in signature.parameters if "label" in p]

def 					lowerCamelCase_ ( __UpperCamelCase					:       MutableMapping							,    __UpperCamelCase					:       str = ""							,    __UpperCamelCase					:       str = "."		)					->	int:







     """simple docstring"""

     def _flatten_dict(__UpperCamelCase					:       Optional[int]							,    __UpperCamelCase					:       int=""							,    __UpperCamelCase					:       List[Any]="."		):
          for k, v in d.items():
               _A				=							str(__UpperCamelCase		) + delimiter + str(__UpperCamelCase		) if parent_key else k
               if v and isinstance(__UpperCamelCase							,    __UpperCamelCase		):
                    yield from flatten_dict(__UpperCamelCase							,    __UpperCamelCase							,    delimiter=__UpperCamelCase		).items()
               else:
                    yield key, v

     return dict(_flatten_dict(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase		)		)

@contextmanager
def 					lowerCamelCase_ ( __UpperCamelCase					:       Tuple							,    __UpperCamelCase					:       bool = False		)					->	List[Any]:







     """simple docstring"""
     if use_temp_dir:
          with tempfile.TemporaryDirectory() as tmp_dir:
               yield tmp_dir
     else:
          yield working_dir

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[str]							,    __UpperCamelCase					:       int=None		)					->	int:







     """simple docstring"""
     if is_numpy_array(__UpperCamelCase		):
          return np.transpose(__UpperCamelCase							,    axes=__UpperCamelCase		)
     elif is_torch_tensor(__UpperCamelCase		):
          return array.T if axes is None else array.permute(*__UpperCamelCase		)
     elif is_tf_tensor(__UpperCamelCase		):
          import tensorflow as tf

          return tf.transpose(__UpperCamelCase							,    perm=__UpperCamelCase		)
     elif is_jax_tensor(__UpperCamelCase		):
          return jnp.transpose(__UpperCamelCase							,    axes=__UpperCamelCase		)
     else:
          raise ValueError(F'Type not supported for transpose: {type(__UpperCamelCase		)}.'		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       List[str]		)					->	Any:







     """simple docstring"""
     if is_numpy_array(__UpperCamelCase		):
          return np.reshape(__UpperCamelCase							,    __UpperCamelCase		)
     elif is_torch_tensor(__UpperCamelCase		):
          return array.reshape(*__UpperCamelCase		)
     elif is_tf_tensor(__UpperCamelCase		):
          import tensorflow as tf

          return tf.reshape(__UpperCamelCase							,    __UpperCamelCase		)
     elif is_jax_tensor(__UpperCamelCase		):
          return jnp.reshape(__UpperCamelCase							,    __UpperCamelCase		)
     else:
          raise ValueError(F'Type not supported for reshape: {type(__UpperCamelCase		)}.'		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Any							,    __UpperCamelCase					:       List[Any]=None		)					->	Any:







     """simple docstring"""
     if is_numpy_array(__UpperCamelCase		):
          return np.squeeze(__UpperCamelCase							,    axis=__UpperCamelCase		)
     elif is_torch_tensor(__UpperCamelCase		):
          return array.squeeze() if axis is None else array.squeeze(dim=__UpperCamelCase		)
     elif is_tf_tensor(__UpperCamelCase		):
          import tensorflow as tf

          return tf.squeeze(__UpperCamelCase							,    axis=__UpperCamelCase		)
     elif is_jax_tensor(__UpperCamelCase		):
          return jnp.squeeze(__UpperCamelCase							,    axis=__UpperCamelCase		)
     else:
          raise ValueError(F'Type not supported for squeeze: {type(__UpperCamelCase		)}.'		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       str							,    __UpperCamelCase					:       Tuple		)					->	Optional[Any]:







     """simple docstring"""
     if is_numpy_array(__UpperCamelCase		):
          return np.expand_dims(__UpperCamelCase							,    __UpperCamelCase		)
     elif is_torch_tensor(__UpperCamelCase		):
          return array.unsqueeze(dim=__UpperCamelCase		)
     elif is_tf_tensor(__UpperCamelCase		):
          import tensorflow as tf

          return tf.expand_dims(__UpperCamelCase							,    axis=__UpperCamelCase		)
     elif is_jax_tensor(__UpperCamelCase		):
          return jnp.expand_dims(__UpperCamelCase							,    axis=__UpperCamelCase		)
     else:
          raise ValueError(F'Type not supported for expand_dims: {type(__UpperCamelCase		)}.'		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       int		)					->	Optional[Any]:







     """simple docstring"""
     if is_numpy_array(__UpperCamelCase		):
          return np.size(__UpperCamelCase		)
     elif is_torch_tensor(__UpperCamelCase		):
          return array.numel()
     elif is_tf_tensor(__UpperCamelCase		):
          import tensorflow as tf

          return tf.size(__UpperCamelCase		)
     elif is_jax_tensor(__UpperCamelCase		):
          return array.size
     else:
          raise ValueError(F'Type not supported for expand_dims: {type(__UpperCamelCase		)}.'		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       Optional[int]		)					->	Dict:







     """simple docstring"""
     for key, value in auto_map.items():
          if isinstance(__UpperCamelCase							,    (tuple, list)		):
               _A				=							[F'{repo_id}--{v}' if (v is not None and '--' not in v) else v for v in value]
          elif value is not None and "--" not in value:
               _A				=							F'{repo_id}--{value}'

     return auto_map

def 					lowerCamelCase_ ( __UpperCamelCase					:       Tuple		)					->	Any:







     """simple docstring"""
     for base_class in inspect.getmro(__UpperCamelCase		):
          _A				=							base_class.__module__
          _A				=							base_class.__name__
          if module.startswith('tensorflow'		) or module.startswith('keras'		) or name == "TFPreTrainedModel":
               return "tf"
          elif module.startswith('torch'		) or name == "PreTrainedModel":
               return "pt"
          elif module.startswith('flax'		) or module.startswith('jax'		) or name == "FlaxPreTrainedModel":
               return "flax"
     else:
          raise TypeError(F'Could not infer framework from class {model_class}.'		)









'''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:
    lowerCAmelCase						=					False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor






class        lowerCAmelCase_  (     unittest.TestCase	):






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase=7			,     _UpperCamelCase=3			,     _UpperCamelCase=18			,     _UpperCamelCase=30			,     _UpperCamelCase=400			,     _UpperCamelCase=None			,     _UpperCamelCase=True			,     _UpperCamelCase=True			,     _UpperCamelCase=None			,     )->      Dict:
            _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, 1024, 2048, 4096]
            _A				=							patch_size if patch_size is not None else {'height': 16, 'width': 16}






       def   UpperCamelCase						( self   )->      Dict:
            return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






       def   UpperCamelCase						( self   )->      Tuple:
            _A				=							'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
            _A				=							Image.open(requests.get(_UpperCamelCase			,     stream=_UpperCamelCase   ).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        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=PixaStructImageProcessor if is_vision_available() else None






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							PixaStructImageProcessingTester(self   )






       @property
       def   UpperCamelCase						( self   )->      Tuple:
            return self.image_processor_tester.prepare_image_processor_dict()






       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_normalize'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_convert_rgb'   )   )






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.image_processor_tester.prepare_dummy_image()

            _A				=							self.image_processing_class(**self.image_processor_dict   )
            _A				=							2048

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






       def   UpperCamelCase						( self   )->      int:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     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=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






       def   UpperCamelCase						( self   )->      List[str]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     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(_UpperCamelCase   ):
                      _A				=							image_processor(
                          image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches

                 _A				=							'Hello'

                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase			,     header_text=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase			,     header_text=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






       def   UpperCamelCase						( self   )->      Optional[int]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random numpy tensors
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase			,     numpify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     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=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PyTorch tensors
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase			,     torchify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     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=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).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        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=PixaStructImageProcessor if is_vision_available() else None






       def   UpperCamelCase						( self   )->      str:
            _A				=							PixaStructImageProcessingTester(self			,     num_channels=4   )
            _A				=							3






       @property
       def   UpperCamelCase						( self   )->      str:
            return self.image_processor_tester.prepare_image_processor_dict()






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_normalize'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_convert_rgb'   )   )






       def   UpperCamelCase						( self   )->      Optional[Any]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     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=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )





