'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_:	str    ={
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:	Union[str, Any]    =[
        'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
        'GPTBigCodeForSequenceClassification',
        'GPTBigCodeForTokenClassification',
        'GPTBigCodeForCausalLM',
        'GPTBigCodeModel',
        'GPTBigCodePreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_bigcode import (
            GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTBigCodeForCausalLM,
            GPTBigCodeForSequenceClassification,
            GPTBigCodeForTokenClassification,
            GPTBigCodeModel,
            GPTBigCodePreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_:	Tuple    =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









'''simple docstring'''
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor






class 				lowercase__      (				unittest.TestCase  ):





	def __init__(					self   :							int					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Optional[Any]=13					,lowerCamelCase__   :							List[Any]=3					,lowerCamelCase__   :							Optional[Any]=224					,lowerCamelCase__   :							int=30					,lowerCamelCase__   :							str=400					,lowerCamelCase__   :							str=True					,lowerCamelCase__   :							Any=None					,lowerCamelCase__   :							Tuple=True					,lowerCamelCase__   :							Tuple=[0.5, 0.5, 0.5]					,lowerCamelCase__   :							Optional[int]=[0.5, 0.5, 0.5]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  size if size is not None else {'height': 18, 'width': 18}
							_UpperCamelCase   :						List[Any]												=  parent
							_UpperCamelCase   :						Union[str, Any]												=  batch_size
							_UpperCamelCase   :						Dict												=  num_channels
							_UpperCamelCase   :						List[str]												=  image_size
							_UpperCamelCase   :						Optional[Any]												=  min_resolution
							_UpperCamelCase   :						Dict												=  max_resolution
							_UpperCamelCase   :						Tuple												=  do_resize
							_UpperCamelCase   :						Optional[int]												=  size
							_UpperCamelCase   :						Tuple												=  do_normalize
							_UpperCamelCase   :						str												=  image_mean
							_UpperCamelCase   :						int												=  image_std





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							}






@require_torch
@require_vision
class 				lowercase__      (				lowercase		,   unittest.TestCase  ):
	lowercase__    							=     ViTImageProcessor if is_vision_available() else None





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  EfficientFormerImageProcessorTester(self					)





	@property
	def       UpperCamelCase_    (					self   :							Optional[int]					):







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





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(lowerCamelCase__					,'image_mean'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'image_std'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'do_normalize'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'do_resize'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'size'					)					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							pass





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							# Initialize image_processor
							_UpperCamelCase   :						str												=  self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							_UpperCamelCase   :						Optional[Any]												=  prepare_image_inputs(self.image_proc_tester					,equal_resolution=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,Image.Image					)

							# Test not batched input
							_UpperCamelCase   :						int												=  image_processor(image_inputs[0]					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)

							# Test batched
							_UpperCamelCase   :						int												=  image_processor(lowerCamelCase__					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							# Initialize image_processor
							_UpperCamelCase   :						Any												=  self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							_UpperCamelCase   :						Optional[int]												=  prepare_image_inputs(self.image_proc_tester					,equal_resolution=lowerCamelCase__					,numpify=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,np.ndarray					)

							# Test not batched input
							_UpperCamelCase   :						Union[str, Any]												=  image_processor(image_inputs[0]					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)

							# Test batched
							_UpperCamelCase   :						Optional[Any]												=  image_processor(lowerCamelCase__					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							# Initialize image_processor
							_UpperCamelCase   :						Any												=  self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							_UpperCamelCase   :						Any												=  prepare_image_inputs(self.image_proc_tester					,equal_resolution=lowerCamelCase__					,torchify=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,torch.Tensor					)

							# Test not batched input
							_UpperCamelCase   :						Dict												=  image_processor(image_inputs[0]					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)

							# Test batched
							_UpperCamelCase   :						Any												=  image_processor(lowerCamelCase__					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)


