


"""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 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		def __init__(  self							, _a							, _a=7							, _a=3							, _a=18							, _a=30							, _a=400							, _a=True							, _a=None							, _a=True							, ):
				__a           =			size if size is not None else {'''height''': 18, '''width''': 18}
				__a           =			parent
				__a           =			batch_size
				__a           =			num_channels
				__a           =			image_size
				__a           =			min_resolution
				__a           =			max_resolution
				__a           =			do_resize
				__a           =			size
				__a           =			do_normalize





		def 			__UpperCAmelCase       (  self      ):
				return {
				    # here we create 2 clusters for the sake of simplicity
				    "clusters": np.asarray(
				        [
				            [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
				            [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
				        ]      ),
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				}







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


		'''simple docstring'''



		__UpperCAmelCase		:			List[Any]										=				ImageGPTImageProcessor if is_vision_available() else None

		def 			__UpperCAmelCase       (  self      ):
				__a           =			ImageGPTImageProcessingTester(self      )

		@property
		def 			__UpperCAmelCase       (  self      ):
				return self.image_processor_tester.prepare_image_processor_dict()

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.image_processing_class(**self.image_processor_dict      )
				self.assertTrue(hasattr(_a							, '''clusters'''      )      )
				self.assertTrue(hasattr(_a							, '''do_resize'''      )      )
				self.assertTrue(hasattr(_a							, '''size'''      )      )
				self.assertTrue(hasattr(_a							, '''do_normalize'''      )      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.image_processing_class.from_dict(self.image_processor_dict      )
				self.assertEqual(image_processor.size							, {'''height''': 18, '''width''': 18}      )

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

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.image_processing_class(**self.image_processor_dict      )
				__a           =			json.loads(image_processor.to_json_string()      )
				for key, value in self.image_processor_dict.items():
						if key == "clusters":
								self.assertTrue(np.array_equal(_a							, obj[key]      )      )
						else:
								self.assertEqual(obj[key]							, _a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.image_processing_class(**self.image_processor_dict      )

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

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

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.image_processing_class(**self.image_processor_dict      )

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

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





		@unittest.skip('''ImageGPT requires clusters at initialization'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass







def        lowercase							(		) ->      Any:
		__a           =			load_dataset('''hf-internal-testing/fixtures_image_utils'''    ,					split='''test'''			)

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

		__a           =			[imagea, imagea]

		return images




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


		'''simple docstring'''



		@slow
		def 			__UpperCAmelCase       (  self      ):
				__a           =			ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''      )

				__a           =			prepare_images()

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

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

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

				# test batched
				__a           =			image_processing(_a							, return_tensors='''pt'''      )

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

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










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_			     =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["RoFormerTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RoFormerForCausalLM",
				    "RoFormerForMaskedLM",
				    "RoFormerForMultipleChoice",
				    "RoFormerForQuestionAnswering",
				    "RoFormerForSequenceClassification",
				    "RoFormerForTokenClassification",
				    "RoFormerLayer",
				    "RoFormerModel",
				    "RoFormerPreTrainedModel",
				    "load_tf_weights_in_roformer",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRoFormerForCausalLM",
				    "TFRoFormerForMaskedLM",
				    "TFRoFormerForMultipleChoice",
				    "TFRoFormerForQuestionAnswering",
				    "TFRoFormerForSequenceClassification",
				    "TFRoFormerForTokenClassification",
				    "TFRoFormerLayer",
				    "TFRoFormerModel",
				    "TFRoFormerPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "FlaxRoFormerForMaskedLM",
				    "FlaxRoFormerForMultipleChoice",
				    "FlaxRoFormerForQuestionAnswering",
				    "FlaxRoFormerForSequenceClassification",
				    "FlaxRoFormerForTokenClassification",
				    "FlaxRoFormerModel",
				    "FlaxRoFormerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
				from .tokenization_roformer import RoFormerTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_roformer_fast import RoFormerTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roformer import (
								    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RoFormerForCausalLM,
								    RoFormerForMaskedLM,
								    RoFormerForMultipleChoice,
								    RoFormerForQuestionAnswering,
								    RoFormerForSequenceClassification,
								    RoFormerForTokenClassification,
								    RoFormerLayer,
								    RoFormerModel,
								    RoFormerPreTrainedModel,
								    load_tf_weights_in_roformer,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roformer import (
								    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRoFormerForCausalLM,
								    TFRoFormerForMaskedLM,
								    TFRoFormerForMultipleChoice,
								    TFRoFormerForQuestionAnswering,
								    TFRoFormerForSequenceClassification,
								    TFRoFormerForTokenClassification,
								    TFRoFormerLayer,
								    TFRoFormerModel,
								    TFRoFormerPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roformer import (
								    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaxRoFormerForMaskedLM,
								    FlaxRoFormerForMultipleChoice,
								    FlaxRoFormerForQuestionAnswering,
								    FlaxRoFormerForSequenceClassification,
								    FlaxRoFormerForTokenClassification,
								    FlaxRoFormerModel,
								    FlaxRoFormerPreTrainedModel,
								)


else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






