






"""simple docstring"""


import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
       import PIL.Image

       from .features import FeatureType


lowercase_				     =     None
lowercase_				     =     """<""" if sys.byteorder == """little""" else """>"""
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowercase_				     =     [
    np.dtype('|b1'),
    np.dtype('|u1'),
    np.dtype('<u2'),
    np.dtype('>u2'),
    np.dtype('<i2'),
    np.dtype('>i2'),
    np.dtype('<u4'),
    np.dtype('>u4'),
    np.dtype('<i4'),
    np.dtype('>i4'),
    np.dtype('<f4'),
    np.dtype('>f4'),
    np.dtype('<f8'),
    np.dtype('>f8'),
]


@dataclass
class 					snake_case		:


   '''simple docstring'''

   A_						:	Tuple						 = True
   A_						:	Union[str, Any]						 = None
   # Automatically constructed
   A_						:	List[Any]						 = "PIL.Image.Image"
   A_						:	Any						 = pa.struct({"bytes": pa.binary(), "path": pa.string()}						)
   A_						:	Optional[int]						 = field(default="Image"   ,	init=UpperCAmelCase__   ,	repr=UpperCAmelCase__						)


   def __call__(	self :     str  ):

     '''simple docstring'''
     return self.pa_type


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     Any  ):

     '''simple docstring'''
     if config.PIL_AVAILABLE:
       import PIL.Image
     else:
       raise ImportError('''To support encoding images, please install \'Pillow\'.'''  )

     if isinstance(_a,       _a  ):
       __A									= np.array(_a  )

     if isinstance(_a,       _a  ):
       return {"path": value, "bytes": None}
     elif isinstance(_a,       _a  ):
       return {"path": None, "bytes": value}
     elif isinstance(_a,       np.ndarray  ):
       # convert the image array to PNG/TIFF bytes
       return encode_np_array(_a  )
     elif isinstance(_a,       PIL.Image.Image  ):
       # convert the PIL image to bytes (default format is PNG/TIFF)
       return encode_pil_image(_a  )
     elif value.get('''path'''  ) is not None and os.path.isfile(value['''path''']  ):
       # we set "bytes": None to not duplicate the data if they're already available locally
       return {"bytes": None, "path": value.get('''path'''  )}
     elif value.get('''bytes'''  ) is not None or value.get('''path'''  ) is not None:
       # store the image bytes, and path is used to infer the image format using the file extension
       return {"bytes": value.get('''bytes'''  ), "path": value.get('''path'''  )}
     else:
       raise ValueError(
           f'An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     str,       _lowerCamelCase :     List[Any]=None  ):

     '''simple docstring'''
     if not self.decode:
       raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.'''  )

     if config.PIL_AVAILABLE:
       import PIL.Image
     else:
       raise ImportError('''To support decoding images, please install \'Pillow\'.'''  )

     if token_per_repo_id is None:
       __A									= {}

     __A			,    __A									= value['''path'''], value['''bytes''']
     if bytes_ is None:
       if path is None:
         raise ValueError(f'An image should have one of \'path\' or \'bytes\' but both are None in {value}.'  )
       else:
         if is_local_path(_a  ):
           __A									= PIL.Image.open(_a  )
         else:
           __A									= path.split('''::'''  )[-1]
           try:
             __A									= string_to_dict(_a,       config.HUB_DATASETS_URL  )['''repo_id''']
             __A									= token_per_repo_id.get(_a  )
           except ValueError:
             __A									= None
           with xopen(_a,       '''rb''',       use_auth_token=_a  ) as f:
             __A									= BytesIO(f.read()  )
           __A									= PIL.Image.open(bytes_  )
     else:
       __A									= PIL.Image.open(BytesIO(bytes_  )  )
     image.load()  # to avoid "Too many open files" errors
     return image


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     from .features import Value

     return (
         self
         if self.decode
         else {
             "bytes": Value('''binary'''  ),
             "path": Value('''string'''  ),
         }
     )


   def 				_SCREAMING_SNAKE_CASE				(	self :     str,       _lowerCamelCase :     str  ):

     '''simple docstring'''
     if pa.types.is_string(storage.type  ):
       __A									= pa.array([None] * len(_a  ),       type=pa.binary()  )
       __A									= pa.StructArray.from_arrays([bytes_array, storage],       ['''bytes''', '''path'''],       mask=storage.is_null()  )
     elif pa.types.is_binary(storage.type  ):
       __A									= pa.array([None] * len(_a  ),       type=pa.string()  )
       __A									= pa.StructArray.from_arrays([storage, path_array],       ['''bytes''', '''path'''],       mask=storage.is_null()  )
     elif pa.types.is_struct(storage.type  ):
       if storage.type.get_field_index('''bytes'''  ) >= 0:
         __A									= storage.field('''bytes'''  )
       else:
         __A									= pa.array([None] * len(_a  ),       type=pa.binary()  )
       if storage.type.get_field_index('''path'''  ) >= 0:
         __A									= storage.field('''path'''  )
       else:
         __A									= pa.array([None] * len(_a  ),       type=pa.string()  )
       __A									= pa.StructArray.from_arrays([bytes_array, path_array],       ['''bytes''', '''path'''],       mask=storage.is_null()  )
     elif pa.types.is_list(storage.type  ):
       __A									= pa.array(
           [encode_np_array(np.array(_a  )  )['''bytes'''] if arr is not None else None for arr in storage.to_pylist()],       type=pa.binary(),       )
       __A									= pa.array([None] * len(_a  ),       type=pa.string()  )
       __A									= pa.StructArray.from_arrays(
           [bytes_array, path_array],       ['''bytes''', '''path'''],       mask=bytes_array.is_null()  )
     return array_cast(_a,       self.pa_type  )






   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     Optional[int]  ):

     '''simple docstring'''

     @no_op_if_value_is_null
     def path_to_bytes(_lowerCamelCase :     Union[str, Any]  ):
       with xopen(_a,       '''rb'''  ) as f:
         __A									= f.read()
       return bytes_

     __A									= pa.array(
         [
             (path_to_bytes(x['''path''']  ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
             for x in storage.to_pylist()
         ],       type=pa.binary(),       )
     __A									= pa.array(
         [os.path.basename(_a  ) if path is not None else None for path in storage.field('''path'''  ).to_pylist()],       type=pa.string(),       )
     __A									= pa.StructArray.from_arrays([bytes_array, path_array],       ['''bytes''', '''path'''],       mask=bytes_array.is_null()  )
     return array_cast(_a,       self.pa_type  )





def 					lowerCAmelCase				(    ):




  """simple docstring"""







  if config.PIL_AVAILABLE:
    import PIL.Image
  else:
    raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

  global _IMAGE_COMPRESSION_FORMATS
  if _IMAGE_COMPRESSION_FORMATS is None:
    PIL.Image.init()
    __A									= list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
  return _IMAGE_COMPRESSION_FORMATS



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= BytesIO()
  if image.format in list_image_compression_formats():
    __A									= image.format
  else:
    __A									= '''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
  image.save(snake_case__		,       format=snake_case__ )
  return buffer.getvalue()



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if hasattr(snake_case__		,       '''filename''' ) and image.filename != "":
    return {"path": image.filename, "bytes": None}
  else:
    return {"path": None, "bytes": image_to_bytes(snake_case__ )}



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if config.PIL_AVAILABLE:
    import PIL.Image
  else:
    raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

  __A									= array.dtype
  __A									= dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
  __A									= dtype.kind
  __A									= dtype.itemsize

  __A									= None

  # Multi-channel array case (only np.dtype("|u1") is allowed)
  if array.shape[2:]:
    __A									= np.dtype('''|u1''' )
    if dtype_kind not in ["u", "i"]:
      raise TypeError(
          f'Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.' )
    if dtype is not dest_dtype:
      warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'' )
    # Exact match
  elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
    __A									= dtype
  else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
    while dtype_itemsize >= 1:
      __A									= dtype_byteorder + dtype_kind + str(snake_case__ )
      __A									= np.dtype(snake_case__ )
      if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
        warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'' )
        break
      else:
        dtype_itemsize //= 2

  if dest_dtype is None:
    raise TypeError(
        f'Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}' )
  __A									= PIL.Image.fromarray(array.astype(snake_case__ ) )
  return {"path": None, "bytes": image_to_bytes(snake_case__ )}





def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if config.PIL_AVAILABLE:
    import PIL.Image
  else:
    raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

  if objs:
    __A			,    __A									= first_non_null_value(snake_case__ )
    if isinstance(snake_case__		,       snake_case__ ):
      return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
    if isinstance(snake_case__		,       np.ndarray ):
      __A									= no_op_if_value_is_null(snake_case__ )
      return [obj_to_image_dict_func(snake_case__ ) for obj in objs]
    elif isinstance(snake_case__		,       PIL.Image.Image ):
      __A									= no_op_if_value_is_null(snake_case__ )
      return [obj_to_image_dict_func(snake_case__ ) for obj in objs]
    else:
      return objs
  else:
    return objs










"""simple docstring"""




import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import ViTMSNForImageClassification, ViTMSNModel
					from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor






class     __magic_name__							:






				'''simple docstring'''





				def __init__(       self		, _a		, _a=13		, _a=30		, _a=2		, _a=3		, _a=True		, _a=True		, _a=32		, _a=5		, _a=4		, _a=37		, _a="gelu"		, _a=0.1		, _a=0.1		, _a=10		, _a=0.02		, _a=None		, ):





							"""simple docstring"""
							lowerCamelCase			      =					parent
							lowerCamelCase			      =					batch_size
							lowerCamelCase			      =					image_size
							lowerCamelCase			      =					patch_size
							lowerCamelCase			      =					num_channels
							lowerCamelCase			      =					is_training
							lowerCamelCase			      =					use_labels
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					num_attention_heads
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					hidden_dropout_prob
							lowerCamelCase			      =					attention_probs_dropout_prob
							lowerCamelCase			      =					type_sequence_label_size
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					scope

							# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							lowerCamelCase			      =					(image_size // patch_size) ** 2
							lowerCamelCase			      =					num_patches + 1





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

							lowerCamelCase			      =					None
							if self.use_labels:
										lowerCamelCase			      =					ids_tensor([self.batch_size]		, self.type_sequence_label_size )

							lowerCamelCase			      =					self.get_config()

							return config, pixel_values, labels





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return ViTMSNConfig(
							    image_size=self.image_size		, patch_size=self.patch_size		, num_channels=self.num_channels		, hidden_size=self.hidden_size		, num_hidden_layers=self.num_hidden_layers		, num_attention_heads=self.num_attention_heads		, intermediate_size=self.intermediate_size		, hidden_act=self.hidden_act		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, initializer_range=self.initializer_range		, )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					ViTMSNModel(config=_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(_a )
							self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size) )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.type_sequence_label_size
							lowerCamelCase			      =					ViTMSNForImageClassification(_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(_a		, labels=_a )
							print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}""" )
							print("""Labels: {labels}""" )
							self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.type_sequence_label_size) )

							# test greyscale images
							lowerCamelCase			      =					1
							lowerCamelCase			      =					ViTMSNForImageClassification(_a )
							model.to(_a )
							model.eval()

							lowerCamelCase			      =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
							lowerCamelCase			      =					model(_a )
							self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.type_sequence_label_size) )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.prepare_config_and_inputs()
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					config_and_inputs
							lowerCamelCase			      =					{"""pixel_values""": pixel_values}
							return config, inputs_dict






@require_torch
class     __magic_name__							(       UpperCAmelCase__						,					UpperCAmelCase__						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				__UpperCamelCase			  =						(
				    {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					ViTMSNModelTester(self )
							lowerCamelCase			      =					ConfigTester(self		, config_class=_a		, has_text_modality=_a		, hidden_size=37 )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							self.config_tester.run_common_tests()





				@unittest.skip(reason="""ViTMSN does not use inputs_embeds""" )
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							pass





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase      ,   lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowerCamelCase			      =					model_class(_a )
										self.assertIsInstance(model.get_input_embeddings()		, (nn.Module) )
										lowerCamelCase			      =					model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(_a		, nn.Linear ) )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase      ,   lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowerCamelCase			      =					model_class(_a )
										lowerCamelCase			      =					inspect.signature(model.forward )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowerCamelCase			      =					[*signature.parameters.keys()]

										lowerCamelCase			      =					["""pixel_values"""]
										self.assertListEqual(arg_names[:1]		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_a )





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCamelCase			      =					ViTMSNModel.from_pretrained(_a )
										self.assertIsNotNone(_a )




def     a__ (				)						->					Any:
			lowerCamelCase			      =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
			return image






@require_torch
@require_vision
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				@cached_property
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small""" ) if is_vision_available() else None





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							torch.manual_seed(2 )
							lowerCamelCase			      =					ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small""" ).to(_a )

							lowerCamelCase			      =					self.default_image_processor
							lowerCamelCase			      =					prepare_img()
							lowerCamelCase			      =					image_processor(images=_a		, return_tensors="""pt""" ).to(_a )

							# forward pass
							with torch.no_grad():
										lowerCamelCase			      =					model(**_a )

							# verify the logits
							lowerCamelCase			      =					torch.Size((1, 1_000) )
							self.assertEqual(outputs.logits.shape		, _a )

							lowerCamelCase			      =					torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(_a )

							self.assertTrue(torch.allclose(outputs.logits[0, :3]		, _a		, atol=1e-4 ) )



