

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 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			Optional[int]=False  ,						SCREAMING_SNAKE_CASE__						:			int=False  ,						SCREAMING_SNAKE_CASE__						:			int=False ):
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    UpperCamelCase :int										=     []
    for i in range(image_processor_tester.batch_size ):
        if equal_resolution:
            UpperCamelCase :Any										=     image_processor_tester.max_resolution
        else:
            # To avoid getting image width/height 0
            UpperCamelCase :List[str]										=     image_processor_tester.min_resolution
            if getattr(SCREAMING_SNAKE_CASE__  ,						'''size_divisor'''  ,						SCREAMING_SNAKE_CASE__ ):
                # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                UpperCamelCase :str										=     max(image_processor_tester.size_divisor  ,						SCREAMING_SNAKE_CASE__ )
            UpperCamelCase :Optional[int]										=     np.random.choice(np.arange(SCREAMING_SNAKE_CASE__  ,						image_processor_tester.max_resolution )  ,						2 )
        image_inputs.append(
            np.random.randint(255  ,						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
        UpperCamelCase :Tuple										=     [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__  ,						0  ,						-1 ) ) for image in image_inputs]

    if torchify:
        UpperCamelCase :Dict										=     [torch.from_numpy(SCREAMING_SNAKE_CASE__ ) for image in image_inputs]

    return image_inputs


def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			List[str]=10  ,						SCREAMING_SNAKE_CASE__						:			str=10  ,						SCREAMING_SNAKE_CASE__						:			List[Any]=False  ,						SCREAMING_SNAKE_CASE__						:			List[Any]=False ):
    UpperCamelCase :Dict										=     []
    for i in range(image_processor_tester.num_frames ):
        video.append(np.random.randint(255  ,						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
        UpperCamelCase :Union[str, Any]										=     [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__  ,						0  ,						-1 ) ) for frame in video]

    if torchify:
        UpperCamelCase :Dict										=     [torch.from_numpy(SCREAMING_SNAKE_CASE__ ) for frame in video]

    return video


def 						_A     (       SCREAMING_SNAKE_CASE__						:			List[str]  ,						SCREAMING_SNAKE_CASE__						:			Tuple=False  ,						SCREAMING_SNAKE_CASE__						:			Union[str, Any]=False  ,						SCREAMING_SNAKE_CASE__						:			int=False ):
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    UpperCamelCase :str										=     []
    for i in range(image_processor_tester.batch_size ):
        if equal_resolution:
            UpperCamelCase :Union[str, Any]										=     image_processor_tester.max_resolution
        else:
            UpperCamelCase :Union[str, Any]										=     np.random.choice(
                np.arange(image_processor_tester.min_resolution  ,						image_processor_tester.max_resolution )  ,						2 )
            UpperCamelCase :List[str]										=     prepare_video(
                image_processor_tester=SCREAMING_SNAKE_CASE__  ,						width=SCREAMING_SNAKE_CASE__  ,						height=SCREAMING_SNAKE_CASE__  ,						numpify=SCREAMING_SNAKE_CASE__  ,						torchify=SCREAMING_SNAKE_CASE__  ,						)
        video_inputs.append(SCREAMING_SNAKE_CASE__ )

    return video_inputs







class     UpperCAmelCase_		:



      """simple docstring"""
      UpperCamelCase_       :      int     =None
      def 					UpperCAmelCase		(       self				)       ->   int:
          UpperCamelCase :Optional[Any]										=     self.image_processing_class(**self.image_processor_dict				)
          UpperCamelCase :Union[str, Any]										=     json.loads(image_processor.to_json_string()				)
          for key, value in self.image_processor_dict.items():
              self.assertEqual(obj[key]					,       SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   List[Any]:
          UpperCamelCase :List[str]										=     self.image_processing_class(**self.image_processor_dict				)

          with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCamelCase :Any										=     os.path.join(SCREAMING_SNAKE_CASE_					,       '''image_processor.json'''				)
              image_processor_first.to_json_file(SCREAMING_SNAKE_CASE_				)
              UpperCamelCase :Dict										=     self.image_processing_class.from_json_file(SCREAMING_SNAKE_CASE_				)

          self.assertEqual(image_processor_second.to_dict()					,       image_processor_first.to_dict()				)
      def 					UpperCAmelCase		(       self				)       ->   List[str]:
          UpperCamelCase :Any										=     self.image_processing_class(**self.image_processor_dict				)

          with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCamelCase :List[Any]										=     image_processor_first.save_pretrained(SCREAMING_SNAKE_CASE_				)[0]
              check_json_file_has_correct_format(SCREAMING_SNAKE_CASE_				)
              UpperCamelCase :str										=     self.image_processing_class.from_pretrained(SCREAMING_SNAKE_CASE_				)

          self.assertEqual(image_processor_second.to_dict()					,       image_processor_first.to_dict()				)
      def 					UpperCAmelCase		(       self				)       ->   Optional[int]:
          UpperCamelCase :Union[str, Any]										=     self.image_processing_class()
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)




      @require_torch
      @require_vision
      def 					UpperCAmelCase		(       self				)       ->   Union[str, Any]:
          if self.test_cast_dtype is not None:
              # Initialize image_processor
              UpperCamelCase :str										=     self.image_processing_class(**self.image_processor_dict				)

              # create random PyTorch tensors
              UpperCamelCase :Any										=     prepare_image_inputs(self.image_processor_tester					,       equal_resolution=SCREAMING_SNAKE_CASE_					,       torchify=SCREAMING_SNAKE_CASE_				)

              UpperCamelCase :List[Any]										=     image_processor(SCREAMING_SNAKE_CASE_					,       return_tensors='''pt'''				)
              # for layoutLM compatiblity
              self.assertEqual(encoding.pixel_values.device					,       torch.device('''cpu'''				)				)
              self.assertEqual(encoding.pixel_values.dtype					,       torch.floataa				)

              UpperCamelCase :List[str]										=     image_processor(SCREAMING_SNAKE_CASE_					,       return_tensors='''pt'''				).to(torch.floataa				)
              self.assertEqual(encoding.pixel_values.device					,       torch.device('''cpu'''				)				)
              self.assertEqual(encoding.pixel_values.dtype					,       torch.floataa				)

              UpperCamelCase :int										=     image_processor(SCREAMING_SNAKE_CASE_					,       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(SCREAMING_SNAKE_CASE_				):
                  UpperCamelCase :List[str]										=     image_processor(SCREAMING_SNAKE_CASE_					,       return_tensors='''pt'''				).to(torch.bfloataa					,       '''cpu'''				)

              # Try with text + image feature
              UpperCamelCase :Tuple										=     image_processor(SCREAMING_SNAKE_CASE_					,       return_tensors='''pt'''				)
              encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]				)}				)
              UpperCamelCase :Optional[Any]										=     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 __future__ import annotations


def 						_A     (       SCREAMING_SNAKE_CASE__						:			int ):
    UpperCamelCase :int										=     {1}
    UpperCamelCase :Optional[int]										=     [1]
    for _ in range(2  ,						depth + 1 ):
        UpperCamelCase :Union[str, Any]										=     [*previous_coefficients, 0]
        UpperCamelCase :List[str]										=     [0, *previous_coefficients]
        UpperCamelCase :List[str]										=     []
        for x, y in zip(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ):
            coefficients.add(x + y )
            previous_coefficients.append(x + y )
    return coefficients


def 						_A     (       SCREAMING_SNAKE_CASE__						:			set[int] ):
    UpperCamelCase :Union[str, Any]										=     set()
    for number in unique_coefficients:
        UpperCamelCase :Optional[int]										=     2
        UpperCamelCase :List[Any]										=     number
        while divisor**2 <= copy_number:
            UpperCamelCase :List[Any]										=     0
            while copy_number % divisor == 0:
                copy_number //= divisor
                multiplicity += 1
            if multiplicity >= 2:
                non_squarefrees.add(SCREAMING_SNAKE_CASE__ )
                break
            divisor += 1

    return unique_coefficients.difference(SCREAMING_SNAKE_CASE__ )


def 						_A     (       SCREAMING_SNAKE_CASE__						:			int = 51 ):
    UpperCamelCase :Any										=     get_pascal_triangle_unique_coefficients(SCREAMING_SNAKE_CASE__ )
    UpperCamelCase :str										=     get_squarefrees(SCREAMING_SNAKE_CASE__ )
    return sum(SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
 print(f'''{solution() = }''')





