



"""simple docstring"""






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        __SCREAMING_SNAKE_CASE	(      unittest.TestCase   ):


					'''simple docstring'''





					def __init__(       self							:  Any,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  int=7,  lowerCamelCase							:  int=3,  lowerCamelCase							:  Optional[Any]=18,  lowerCamelCase							:  Optional[int]=30,  lowerCamelCase							:  Any=400,  lowerCamelCase							:  Tuple=True,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Union[str, Any]=True,  )->					List[Any]:
								lowerCamelCase__		:		Any			=size if size is not None else {'''height''': 18, '''width''': 18}
								lowerCamelCase__		:		Tuple			=parent
								lowerCamelCase__		:		Optional[int]			=batch_size
								lowerCamelCase__		:		Tuple			=num_channels
								lowerCamelCase__		:		Union[str, Any]			=image_size
								lowerCamelCase__		:		Optional[int]			=min_resolution
								lowerCamelCase__		:		Dict			=max_resolution
								lowerCamelCase__		:		List[str]			=do_resize
								lowerCamelCase__		:		Any			=size
								lowerCamelCase__		:		str			=do_normalize




					def 		snake_case       (       self							:  Any     )->					List[Any]:
								return {
								    # here we create 2 clusters for the sake of simplicity
								    "clusters": np.asarray(
								        [
								            [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
								            [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
								        ]     ),
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								}





@require_torch
@require_vision
class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_		,   unittest.TestCase   ):


					'''simple docstring'''





					_a   			=							ImageGPTImageProcessor if is_vision_available() else None



					def 		snake_case       (       self							:  List[Any]     )->					List[Any]:
								lowerCamelCase__		:		Union[str, Any]			=ImageGPTImageProcessingTester(self     )



					@property
					def 		snake_case       (       self							:  List[str]     )->					int:
								return self.image_processor_tester.prepare_image_processor_dict()



					def 		snake_case       (       self							:  Optional[Any]     )->					List[Any]:
								lowerCamelCase__		:		Optional[Any]			=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 		snake_case       (       self							:  List[str]     )->					Any:
								lowerCamelCase__		:		List[Any]			=self.image_processing_class.from_dict(self.image_processor_dict     )
								self.assertEqual(image_processor.size,  {'''height''': 18, '''width''': 18}     )

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



					def 		snake_case       (       self							:  List[str]     )->					Optional[Any]:
								lowerCamelCase__		:		int			=self.image_processing_class(**self.image_processor_dict     )
								lowerCamelCase__		:		Any			=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 		snake_case       (       self							:  Any     )->					Union[str, Any]:
								lowerCamelCase__		:		Tuple			=self.image_processing_class(**self.image_processor_dict     )

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

								lowerCamelCase__		:		Optional[int]			=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 		snake_case       (       self							:  str     )->					List[str]:
								lowerCamelCase__		:		Tuple			=self.image_processing_class(**self.image_processor_dict     )

								with tempfile.TemporaryDirectory() as tmpdirname:
											image_processor_first.save_pretrained(lowerCamelCase     )
											lowerCamelCase__		:		Union[str, Any]			=self.image_processing_class.from_pretrained(lowerCamelCase     ).to_dict()

								lowerCamelCase__		:		str			=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 		snake_case       (       self							:  Optional[Any]     )->					str:
								pass






def      snake_case__   (		):
			"""simple docstring"""
			lowerCamelCase__		:		int			=load_dataset('''hf-internal-testing/fixtures_image_utils'''							,		split='''test'''	)

			lowerCamelCase__		:		Any			=Image.open(dataset[4]['''file''']	)
			lowerCamelCase__		:		str			=Image.open(dataset[5]['''file''']	)

			lowerCamelCase__		:		Optional[int]			=[imagea, imagea]

			return images

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


					'''simple docstring'''





					@slow
					def 		snake_case       (       self							:  Tuple     )->					List[Any]:
								lowerCamelCase__		:		Tuple			=ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''     )

								lowerCamelCase__		:		Dict			=prepare_images()

								# test non-batched
								lowerCamelCase__		:		List[str]			=image_processing(images[0],  return_tensors='''pt'''     )

								self.assertIsInstance(encoding.input_ids,  torch.LongTensor     )
								self.assertEqual(encoding.input_ids.shape,  (1, 1024)     )

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

								# test batched
								lowerCamelCase__		:		List[str]			=image_processing(lowerCamelCase,  return_tensors='''pt'''     )

								self.assertIsInstance(encoding.input_ids,  torch.LongTensor     )
								self.assertEqual(encoding.input_ids.shape,  (2, 1024)     )

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









"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
		from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_lowercase : List[str]      						=						logging.get_logger(__name__)

_lowercase : int      						=						{
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							'deberta-v2'



					def __init__(       self							:  Optional[Any],  lowerCamelCase							:  Optional[int]=12_8100,  lowerCamelCase							:  List[Any]=1536,  lowerCamelCase							:  Dict=24,  lowerCamelCase							:  Any=24,  lowerCamelCase							:  Union[str, Any]=6144,  lowerCamelCase							:  List[Any]="gelu",  lowerCamelCase							:  int=0.1,  lowerCamelCase							:  Union[str, Any]=0.1,  lowerCamelCase							:  Union[str, Any]=512,  lowerCamelCase							:  Optional[Any]=0,  lowerCamelCase							:  Any=0.02,  lowerCamelCase							:  int=1E-7,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Union[str, Any]=-1,  lowerCamelCase							:  Tuple=0,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  int=None,  lowerCamelCase							:  Dict=0,  lowerCamelCase							:  Tuple="gelu",  **lowerCamelCase							:  Optional[int],  )->					Union[str, Any]:
								super().__init__(**lowerCamelCase     )

								lowerCamelCase__		:		str			=hidden_size
								lowerCamelCase__		:		Optional[int]			=num_hidden_layers
								lowerCamelCase__		:		Optional[Any]			=num_attention_heads
								lowerCamelCase__		:		List[Any]			=intermediate_size
								lowerCamelCase__		:		int			=hidden_act
								lowerCamelCase__		:		Tuple			=hidden_dropout_prob
								lowerCamelCase__		:		Union[str, Any]			=attention_probs_dropout_prob
								lowerCamelCase__		:		Optional[Any]			=max_position_embeddings
								lowerCamelCase__		:		int			=type_vocab_size
								lowerCamelCase__		:		Tuple			=initializer_range
								lowerCamelCase__		:		Tuple			=relative_attention
								lowerCamelCase__		:		Optional[Any]			=max_relative_positions
								lowerCamelCase__		:		List[Any]			=pad_token_id
								lowerCamelCase__		:		int			=position_biased_input

								# Backwards compatibility
								if type(lowerCamelCase     ) == str:
											lowerCamelCase__		:		Union[str, Any]			=[x.strip() for x in pos_att_type.lower().split('''|'''     )]

								lowerCamelCase__		:		Tuple			=pos_att_type
								lowerCamelCase__		:		Union[str, Any]			=vocab_size
								lowerCamelCase__		:		Optional[int]			=layer_norm_eps

								lowerCamelCase__		:		Dict			=kwargs.get('''pooler_hidden_size''',  lowerCamelCase     )
								lowerCamelCase__		:		Tuple			=pooler_dropout
								lowerCamelCase__		:		List[Any]			=pooler_hidden_act





class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					@property
					def 		snake_case       (       self							:  List[str]     )->					Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
											lowerCamelCase__		:		Union[str, Any]			={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
											lowerCamelCase__		:		Any			={0: '''batch''', 1: '''sequence'''}
								if self._config.type_vocab_size > 0:
											return OrderedDict(
											    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]     )
								else:
											return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]     )



					@property
					def 		snake_case       (       self							:  List[str]     )->					int:
								return 12




					def 		snake_case       (       self							:  str,  lowerCamelCase							:  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  Optional["TensorType"] = None,  lowerCamelCase							:  int = 3,  lowerCamelCase							:  int = 40,  lowerCamelCase							:  int = 40,  lowerCamelCase							:  "PreTrainedTokenizerBase" = None,  )->					Mapping[str, Any]:
								lowerCamelCase__		:		List[Any]			=super().generate_dummy_inputs(preprocessor=lowerCamelCase,  framework=lowerCamelCase     )
								if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
											del dummy_inputs["token_type_ids"]
								return dummy_inputs




