


'''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


a__  :					Optional[List[str]]        =       None
a__  :					Dict        =       '<' 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
a__  :					Any        =       [
    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 					lowercase_     :
    __UpperCAmelCase   			=	True
    __UpperCAmelCase   			=	None
    # Automatically constructed
    __UpperCAmelCase   			=	"PIL.Image.Image"
    __UpperCAmelCase   			=	pa.struct({'bytes': pa.binary(), 'path': pa.string()}   )
    __UpperCAmelCase   			=	field(default='Image'  ,						init=a__  ,						repr=a__   )


    def __call__(     self    ):
          return self.pa_type


    def    __a							(     self  ,					a    ):
          if config.PIL_AVAILABLE:
                import PIL.Image
          else:
                raise ImportError("To support encoding images, please install 'Pillow'."    )

          if isinstance(a  ,					a    ):
                UpperCamelCase__										=				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    __a							(     self  ,					a  ,					a=None    ):
          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:
                UpperCamelCase__										=				{}

          UpperCamelCase__					,	UpperCamelCase__										=				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    ):
                            UpperCamelCase__										=				PIL.Image.open(a    )
                      else:
                            UpperCamelCase__										=				path.split("::"    )[-1]
                            try:
                                  UpperCamelCase__										=				string_to_dict(a  ,					config.HUB_DATASETS_URL    )["repo_id"]
                                  UpperCamelCase__										=				token_per_repo_id.get(a    )
                            except ValueError:
                                  UpperCamelCase__										=				None
                            with xopen(a  ,					"rb"  ,					use_auth_token=a    ) as f:
                                  UpperCamelCase__										=				BytesIO(f.read()    )
                            UpperCamelCase__										=				PIL.Image.open(bytes_    )
          else:
                UpperCamelCase__										=				PIL.Image.open(BytesIO(bytes_    )    )
          image.load()  # to avoid "Too many open files" errors
          return image


    def    __a							(     self    ):
          from .features import Value

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


    def    __a							(     self  ,					a    ):
          if pa.types.is_string(storage.type    ):
                UpperCamelCase__										=				pa.array([None] * len(a    )  ,					type=pa.binary()    )
                UpperCamelCase__										=				pa.StructArray.from_arrays([bytes_array, storage]  ,					["bytes", "path"]  ,					mask=storage.is_null()    )
          elif pa.types.is_binary(storage.type    ):
                UpperCamelCase__										=				pa.array([None] * len(a    )  ,					type=pa.string()    )
                UpperCamelCase__										=				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:
                      UpperCamelCase__										=				storage.field("bytes"    )
                else:
                      UpperCamelCase__										=				pa.array([None] * len(a    )  ,					type=pa.binary()    )
                if storage.type.get_field_index("path"    ) >= 0:
                      UpperCamelCase__										=				storage.field("path"    )
                else:
                      UpperCamelCase__										=				pa.array([None] * len(a    )  ,					type=pa.string()    )
                UpperCamelCase__										=				pa.StructArray.from_arrays([bytes_array, path_array]  ,					["bytes", "path"]  ,					mask=storage.is_null()    )
          elif pa.types.is_list(storage.type    ):
                UpperCamelCase__										=				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()  ,					)
                UpperCamelCase__										=				pa.array([None] * len(a    )  ,					type=pa.string()    )
                UpperCamelCase__										=				pa.StructArray.from_arrays(
                    [bytes_array, path_array]  ,					["bytes", "path"]  ,					mask=bytes_array.is_null()    )
          return array_cast(a  ,					self.pa_type    )


    def    __a							(     self  ,					a    ):

          @no_op_if_value_is_null
          def path_to_bytes(a    ):
                with xopen(a  ,					"rb"    ) as f:
                      UpperCamelCase__										=				f.read()
                return bytes_

          UpperCamelCase__										=				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()  ,					)
          UpperCamelCase__										=				pa.array(
              [os.path.basename(a    ) if path is not None else None for path in storage.field("path"    ).to_pylist()]  ,					type=pa.string()  ,					)
          UpperCamelCase__										=				pa.StructArray.from_arrays([bytes_array, path_array]  ,					["bytes", "path"]  ,					mask=bytes_array.is_null()    )
          return array_cast(a  ,					self.pa_type    )







def _UpperCamelCase       (     )							->		List[str]:


      '''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()
            UpperCamelCase__										=				list(set(PIL.Image.OPEN.keys()    ) & set(PIL.Image.SAVE.keys()    )    )
      return _IMAGE_COMPRESSION_FORMATS







def _UpperCamelCase       (     __A    )							->		bytes:


      '''simple docstring'''



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







def _UpperCamelCase       (     __A    )							->		dict:


      '''simple docstring'''



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







def _UpperCamelCase       (     __A    )							->		dict:


      '''simple docstring'''



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

      UpperCamelCase__										=				array.dtype
      UpperCamelCase__										=				dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
      UpperCamelCase__										=				dtype.kind
      UpperCamelCase__										=				dtype.itemsize

      UpperCamelCase__										=				None

      # Multi-channel array case (only np.dtype("|u1") is allowed)
      if array.shape[2:]:
            UpperCamelCase__										=				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:
            UpperCamelCase__										=				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:
                  UpperCamelCase__										=				dtype_byteorder + dtype_kind + str(__A    )
                  UpperCamelCase__										=				np.dtype(__A    )
                  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}'''    )
      UpperCamelCase__										=				PIL.Image.fromarray(array.astype(__A    )    )
      return {"path": None, "bytes": image_to_bytes(__A    )}







def _UpperCamelCase       (     __A    )							->		List[dict]:


      '''simple docstring'''



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

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



