






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ImageGPTImageProcessor
class       _UpperCAmelCase					(       unittest.TestCase	):

				"""simple docstring"""




				def __init__(     self						:    List[str],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Union[str, Any]=7,  lowerCamelCase						:    Union[str, Any]=3,  lowerCamelCase						:    Optional[Any]=18,  lowerCamelCase						:    Optional[int]=30,  lowerCamelCase						:    List[str]=400,  lowerCamelCase						:    Dict=True,  lowerCamelCase						:    Optional[Any]=None,  lowerCamelCase						:    List[Any]=True,  ):

								'''simple docstring'''
								lowercase__    =			size if size is not None else {'''height''': 18, '''width''': 18}
								lowercase__    =			parent
								lowercase__    =			batch_size
								lowercase__    =			num_channels
								lowercase__    =			image_size
								lowercase__    =			min_resolution
								lowercase__    =			max_resolution
								lowercase__    =			do_resize
								lowercase__    =			size
								lowercase__    =			do_normalize




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								return {
								    # here we create 2 clusters for the sake of simplicity
								    "clusters": np.asarray(
								        [
								            [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
								            [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
								        ]  ),
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								}



@require_torch
@require_vision
class       _UpperCAmelCase					(       A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       ImageGPTImageProcessor if is_vision_available() else None




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			ImageGPTImageProcessingTester(self  )




				@property
				def 				lowercase__   (     self						:    List[Any]  ):

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




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(lowerCamelCase,  '''clusters'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''do_resize'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''size'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''do_normalize'''  )  )




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.image_processing_class.from_dict(self.image_processor_dict  )
								self.assertEqual(image_processor.size,  {'''height''': 18, '''width''': 18}  )

								lowercase__    =			self.image_processing_class.from_dict(self.image_processor_dict,  size=42  )
								self.assertEqual(image_processor.size,  {'''height''': 42, '''width''': 42}  )




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			self.image_processing_class(**self.image_processor_dict  )
								lowercase__    =			json.loads(image_processor.to_json_string()  )
								for key, value in self.image_processor_dict.items():
												if key == "clusters":
																self.assertTrue(np.array_equal(lowerCamelCase,  obj[key]  )  )
												else:
																self.assertEqual(obj[key],  lowerCamelCase  )




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			self.image_processing_class(**self.image_processor_dict  )

								with tempfile.TemporaryDirectory() as tmpdirname:
												lowercase__    =			os.path.join(lowerCamelCase,  '''image_processor.json'''  )
												image_processor_first.to_json_file(lowerCamelCase  )
												lowercase__    =			self.image_processing_class.from_json_file(lowerCamelCase  ).to_dict()

								lowercase__    =			image_processor_first.to_dict()
								for key, value in image_processor_first.items():
												if key == "clusters":
																self.assertTrue(np.array_equal(lowerCamelCase,  image_processor_second[key]  )  )
												else:
																self.assertEqual(image_processor_first[key],  lowerCamelCase  )




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			self.image_processing_class(**self.image_processor_dict  )

								with tempfile.TemporaryDirectory() as tmpdirname:
												image_processor_first.save_pretrained(lowerCamelCase  )
												lowercase__    =			self.image_processing_class.from_pretrained(lowerCamelCase  ).to_dict()

								lowercase__    =			image_processor_first.to_dict()
								for key, value in image_processor_first.items():
												if key == "clusters":
																self.assertTrue(np.array_equal(lowerCamelCase,  image_processor_second[key]  )  )
												else:
																self.assertEqual(image_processor_first[key],  lowerCamelCase  )




				@unittest.skip('''ImageGPT requires clusters at initialization'''  )
				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								pass






def   a			(						):






				'''simple docstring'''
				lowercase__    =			load_dataset('''hf-internal-testing/fixtures_image_utils'''		,   split='''test'''				)

				lowercase__    =			Image.open(dataset[4]['''file''']				)
				lowercase__    =			Image.open(dataset[5]['''file''']				)

				lowercase__    =			[imagea, imagea]

				return images




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

				"""simple docstring"""




				@slow
				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''  )

								lowercase__    =			prepare_images()

								# test non-batched
								lowercase__    =			image_processing(images[0],  return_tensors='''pt'''  )

								self.assertIsInstance(encoding.input_ids,  torch.LongTensor  )
								self.assertEqual(encoding.input_ids.shape,  (1, 1_024)  )

								lowercase__    =			[306, 191, 191]
								self.assertEqual(encoding.input_ids[0, :3].tolist(),  lowerCamelCase  )

								# test batched
								lowercase__    =			image_processing(lowerCamelCase,  return_tensors='''pt'''  )

								self.assertIsInstance(encoding.input_ids,  torch.LongTensor  )
								self.assertEqual(encoding.input_ids.shape,  (2, 1_024)  )

								lowercase__    =			[303, 13, 13]
								self.assertEqual(encoding.input_ids[1, -3:].tolist(),  lowerCamelCase  )












from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
							import timm


if is_torch_available():
							from torch import Tensor
class       _UpperCAmelCase					(       A__		,A__	):

				"""simple docstring"""




				lowercase__											=       """pixel_values"""
				lowercase__											=       False
				lowercase__											=       TimmBackboneConfig




				def __init__(     self						:    Tuple,  lowerCamelCase						:    List[str],  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								requires_backends(self,  '''timm'''  )
								super().__init__(lowerCamelCase  )
								lowercase__    =			config

								if config.backbone is None:
												raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''  )

								if config.backbone not in timm.list_models():
												raise ValueError(F"""backbone {config.backbone} is not supported by timm."""  )

								if hasattr(lowerCamelCase,  '''out_features'''  ) and config.out_features is not None:
												raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''  )

								lowercase__    =			getattr(lowerCamelCase,  '''use_pretrained_backbone''',  lowerCamelCase  )
								if pretrained is None:
												raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''  )

								# We just take the final layer by default. This matches the default for the transformers models.
								lowercase__    =			config.out_indices if getattr(lowerCamelCase,  '''out_indices''',  lowerCamelCase  ) is not None else (-1,)

								lowercase__    =			timm.create_model(
								    config.backbone,  pretrained=lowerCamelCase,  features_only=config.features_only,  in_chans=config.num_channels,  out_indices=lowerCamelCase,  **lowerCamelCase,  )
								# These are used to control the output of the model when called. If output_hidden_states is True, then
								# return_layers is modified to include all layers.
								lowercase__    =			self._backbone.return_layers
								lowercase__    =			{layer['''module''']: str(lowerCamelCase  ) for i, layer in enumerate(self._backbone.feature_info.info  )}
								super()._init_backbone(lowerCamelCase  )




				@classmethod
				def 				lowercase__   (     cls						:    List[str],  lowerCamelCase						:    List[str],  *lowerCamelCase						:    Optional[int],  **lowerCamelCase						:    List[Any]  ):

								'''simple docstring'''
								requires_backends(cls,  ['''vision''', '''timm''']  )
								from ...models.timm_backbone import TimmBackboneConfig

								lowercase__    =			kwargs.pop('''config''',  TimmBackboneConfig()  )

								lowercase__    =			kwargs.pop('''use_timm_backbone''',  lowerCamelCase  )
								if not use_timm:
												raise ValueError('''use_timm_backbone must be True for timm backbones'''  )

								lowercase__    =			kwargs.pop('''num_channels''',  config.num_channels  )
								lowercase__    =			kwargs.pop('''features_only''',  config.features_only  )
								lowercase__    =			kwargs.pop('''use_pretrained_backbone''',  config.use_pretrained_backbone  )
								lowercase__    =			kwargs.pop('''out_indices''',  config.out_indices  )
								lowercase__    =			TimmBackboneConfig(
								    backbone=lowerCamelCase,  num_channels=lowerCamelCase,  features_only=lowerCamelCase,  use_pretrained_backbone=lowerCamelCase,  out_indices=lowerCamelCase,  )
								return super()._from_config(lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    Optional[int]=None,  lowerCamelCase						:    List[Any]=None,  lowerCamelCase						:    int=None,  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			return_dict if return_dict is not None else self.config.use_return_dict
								lowercase__    =			(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								lowercase__    =			output_attentions if output_attentions is not None else self.config.output_attentions

								if output_attentions:
												raise ValueError('''Cannot output attentions for timm backbones at the moment'''  )

								if output_hidden_states:
												# We modify the return layers to include all the stages of the backbone
												lowercase__    =			self._all_layers
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			self._return_layers
												lowercase__    =			tuple(hidden_states[i] for i in self.out_indices  )
								else:
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			None

								lowercase__    =			tuple(lowerCamelCase  )
								lowercase__    =			tuple(lowerCamelCase  ) if hidden_states is not None else None

								if not return_dict:
												lowercase__    =			(feature_maps,)
												if output_hidden_states:
																lowercase__    =			output + (hidden_states,)
												return output

								return BackboneOutput(feature_maps=lowerCamelCase,  hidden_states=lowerCamelCase,  attentions=lowerCamelCase  )




