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 PoolFormerImageProcessor



class 			SCREAMING_SNAKE_CASE_			(       unittest.TestCase      ):
  '''simple docstring'''


  def __init__(					self     :       str   ,						SCREAMING_SNAKE_CASE__     :       Union[str, Any]   ,						SCREAMING_SNAKE_CASE__     :       Optional[int]=7   ,						SCREAMING_SNAKE_CASE__     :       Tuple=3   ,						SCREAMING_SNAKE_CASE__     :       Optional[int]=30   ,						SCREAMING_SNAKE_CASE__     :       int=4_00   ,						SCREAMING_SNAKE_CASE__     :       Dict=True   ,						SCREAMING_SNAKE_CASE__     :       Optional[int]=None   ,						SCREAMING_SNAKE_CASE__     :       List[Any]=0.9   ,						SCREAMING_SNAKE_CASE__     :       str=None   ,						SCREAMING_SNAKE_CASE__     :       Optional[Any]=True   ,						SCREAMING_SNAKE_CASE__     :       List[str]=[0.5, 0.5, 0.5]   ,						SCREAMING_SNAKE_CASE__     :       Optional[int]=[0.5, 0.5, 0.5]   ,						)     ->					Tuple:
         A    :  List[Any]								=size if size is not None else {'shortest_edge': 30}
         A    :  Any								=crop_size if crop_size is not None else {'height': 30, 'width': 30}
         A    :  str								=parent
         A    :  Union[str, Any]								=batch_size
         A    :  int								=num_channels
         A    :  Optional[Any]								=min_resolution
         A    :  str								=max_resolution
         A    :  Any								=do_resize_and_center_crop
         A    :  List[str]								=size
         A    :  Optional[Any]								=crop_pct
         A    :  Any								=crop_size
         A    :  Any								=do_normalize
         A    :  Dict								=image_mean
         A    :  Tuple								=image_std







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Union[str, Any]  )     ->					Dict:
         return {
             "size": self.size,
             "do_resize_and_center_crop": self.do_resize_and_center_crop,
             "crop_pct": self.crop_pct,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }







@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE_			(       _a ,    unittest.TestCase      ):
  '''simple docstring'''


  lowercase		:  int							  =					PoolFormerImageProcessor if is_vision_available() else None







  def 		SCREAMING_SNAKE_CASE_		(					self     :       int  )     ->					int:
         A    :  List[Any]								=PoolFormerImageProcessingTester(self  )







  @property
  def 		SCREAMING_SNAKE_CASE_		(					self     :       Union[str, Any]  )     ->					str:
         return self.image_processor_tester.prepare_image_processor_dict()







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Any  )     ->					int:
         A    :  Any								=self.image_processing_class(**self.image_processor_dict  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,						'do_resize_and_center_crop'  )  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,						'size'  )  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,						'crop_pct'  )  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,						'do_normalize'  )  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,						'image_mean'  )  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,						'image_std'  )  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       List[Any]  )     ->					Any:
         A    :  int								=self.image_processing_class.from_dict(self.image_processor_dict  )
         self.assertEqual(image_processor.size   ,						{'shortest_edge': 30}  )
         self.assertEqual(image_processor.crop_size   ,						{'height': 30, 'width': 30}  )

         A    :  Any								=self.image_processing_class.from_dict(self.image_processor_dict   ,						size=42   ,						crop_size=84  )
         self.assertEqual(image_processor.size   ,						{'shortest_edge': 42}  )
         self.assertEqual(image_processor.crop_size   ,						{'height': 84, 'width': 84}  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Dict  )     ->					Optional[Any]:
         pass







  def 		SCREAMING_SNAKE_CASE_		(					self     :       str  )     ->					str:
         A    :  Tuple								=self.image_processing_class(**self.image_processor_dict  )
         # create random PIL images
         A    :  str								=prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=SCREAMING_SNAKE_CASE__  )
         for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,						Image.Image  )

         # Test not batched input
         A    :  Union[str, Any]								=image_processing(image_inputs[0]   ,						return_tensors='pt'  ).pixel_values

         self.assertEqual(
             encoded_images.shape   ,						(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )   ,						)

         # Test batched
         A    :  int								=image_processing(SCREAMING_SNAKE_CASE__   ,						return_tensors='pt'  ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )   ,						)







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Any  )     ->					Union[str, Any]:
         A    :  Optional[int]								=self.image_processing_class(**self.image_processor_dict  )
         # create random numpy tensors
         A    :  Union[str, Any]								=prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=SCREAMING_SNAKE_CASE__   ,						numpify=SCREAMING_SNAKE_CASE__  )
         for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,						np.ndarray  )

         # Test not batched input
         A    :  Dict								=image_processing(image_inputs[0]   ,						return_tensors='pt'  ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,						(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )   ,						)

         # Test batched
         A    :  Dict								=image_processing(SCREAMING_SNAKE_CASE__   ,						return_tensors='pt'  ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )   ,						)







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Optional[Any]  )     ->					Optional[Any]:
         A    :  Tuple								=self.image_processing_class(**self.image_processor_dict  )
         # create random PyTorch tensors
         A    :  List[str]								=prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=SCREAMING_SNAKE_CASE__   ,						torchify=SCREAMING_SNAKE_CASE__  )
         for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,						torch.Tensor  )

         # Test not batched input
         A    :  List[str]								=image_processing(image_inputs[0]   ,						return_tensors='pt'  ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,						(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )   ,						)

         # Test batched
         A    :  Optional[Any]								=image_processing(SCREAMING_SNAKE_CASE__   ,						return_tensors='pt'  ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )   ,						)










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			: int				   =					[
     "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			: List[Any]				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


