




import json
import os
import tempfile

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


if is_torch_available():
   import numpy as np
   import torch

if is_vision_available():
   from PIL import Image

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase=False	,      __UpperCamelCase=False	,      __UpperCamelCase=False)    ->  List[str]:
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     a											=   []
     for i in range(image_processor_tester.batch_size):
          if equal_resolution:
               a											=   a											=   image_processor_tester.max_resolution
          else:
               # To avoid getting image width/height 0
               a											=   image_processor_tester.min_resolution
               if getattr(__UpperCamelCase	,      "size_divisor"	,      __UpperCamelCase):
                    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                    a											=   max(image_processor_tester.size_divisor	,      __UpperCamelCase)
               a						,   a											=   np.random.choice(np.arange(__UpperCamelCase	,      image_processor_tester.max_resolution)	,      2)
          image_inputs.append(
              np.random.randint(2_55	,      size=(image_processor_tester.num_channels, width, height)	,      dtype=np.uinta))

     if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          a											=   [Image.fromarray(np.moveaxis(__UpperCamelCase	,      0	,      -1)) for image in image_inputs]

     if torchify:
          a											=   [torch.from_numpy(__UpperCamelCase) for image in image_inputs]

     return image_inputs

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase=10	,      __UpperCamelCase=10	,      __UpperCamelCase=False	,      __UpperCamelCase=False)    ->  Union[str, Any]:
     a											=   []
     for i in range(image_processor_tester.num_frames):
          video.append(np.random.randint(2_55	,      size=(image_processor_tester.num_channels, width, height)	,      dtype=np.uinta))

     if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          a											=   [Image.fromarray(np.moveaxis(__UpperCamelCase	,      0	,      -1)) for frame in video]

     if torchify:
          a											=   [torch.from_numpy(__UpperCamelCase) for frame in video]

     return video

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase=False	,      __UpperCamelCase=False	,      __UpperCamelCase=False)    ->  List[Any]:
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     a											=   []
     for i in range(image_processor_tester.batch_size):
          if equal_resolution:
               a											=   a											=   image_processor_tester.max_resolution
          else:
               a						,   a											=   np.random.choice(
                   np.arange(image_processor_tester.min_resolution	,      image_processor_tester.max_resolution)	,      2)
               a											=   prepare_video(
                   image_processor_tester=__UpperCamelCase	,      width=__UpperCamelCase	,      height=__UpperCamelCase	,      numpify=__UpperCamelCase	,      torchify=__UpperCamelCase	,      )
          video_inputs.append(__UpperCamelCase)

     return video_inputs





class     a__ :
     a	:    Tuple	      =			None







     def     lowerCAmelCase_       (			self				)						->					Dict:



          '''simple docstring'''



          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():
               self.assertEqual(obj[key]						,	A				)







     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          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				)

          self.assertEqual(image_processor_second.to_dict()						,	image_processor_first.to_dict()				)







     def     lowerCAmelCase_       (			self				)						->					Optional[Any]:



          '''simple docstring'''



          a											=   self.image_processing_class(**self.image_processor_dict				)

          with tempfile.TemporaryDirectory() as tmpdirname:
               a											=   image_processor_first.save_pretrained(A				)[0]
               check_json_file_has_correct_format(A				)
               a											=   self.image_processing_class.from_pretrained(A				)

          self.assertEqual(image_processor_second.to_dict()						,	image_processor_first.to_dict()				)







     def     lowerCAmelCase_       (			self				)						->					Tuple:



          '''simple docstring'''



          a											=   self.image_processing_class()
          self.assertIsNotNone(A				)







     @require_torch
     @require_vision
     def     lowerCAmelCase_       (			self				)						->					Any:



          '''simple docstring'''



          if self.test_cast_dtype is not None:
               # Initialize image_processor
               a											=   self.image_processing_class(**self.image_processor_dict				)

               # create random PyTorch tensors
               a											=   prepare_image_inputs(self.image_processor_tester						,	equal_resolution=A						,	torchify=A				)

               a											=   image_processor(A						,	return_tensors="pt"				)
               # for layoutLM compatiblity
               self.assertEqual(encoding.pixel_values.device						,	torch.device("cpu"				)				)
               self.assertEqual(encoding.pixel_values.dtype						,	torch.floataa				)

               a											=   image_processor(A						,	return_tensors="pt"				).to(torch.floataa				)
               self.assertEqual(encoding.pixel_values.device						,	torch.device("cpu"				)				)
               self.assertEqual(encoding.pixel_values.dtype						,	torch.floataa				)

               a											=   image_processor(A						,	return_tensors="pt"				).to("cpu"						,	torch.bfloataa				)
               self.assertEqual(encoding.pixel_values.device						,	torch.device("cpu"				)				)
               self.assertEqual(encoding.pixel_values.dtype						,	torch.bfloataa				)

               with self.assertRaises(A				):
                    a											=   image_processor(A						,	return_tensors="pt"				).to(torch.bfloataa						,	"cpu"				)

               # Try with text + image feature
               a											=   image_processor(A						,	return_tensors="pt"				)
               encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]				)}				)
               a											=   encoding.to(torch.floataa				)

               self.assertEqual(encoding.pixel_values.device						,	torch.device("cpu"				)				)
               self.assertEqual(encoding.pixel_values.dtype						,	torch.floataa				)
               self.assertEqual(encoding.input_ids.dtype						,	torch.long				)







from typing import TYPE_CHECKING

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


lowercase__   :    str 					=      {
    "configuration_rwkv": ["RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP", "RwkvConfig", "RwkvOnnxConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__   :    int 					=      [
       "RWKV_PRETRAINED_MODEL_ARCHIVE_LIST",
       "RwkvForCausalLM",
       "RwkvModel",
       "RwkvPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rwkv import (
          RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
          RwkvForCausalLM,
          RwkvModel,
          RwkvPreTrainedModel,
      )
else:
   import sys

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

