



import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
    import torch

if is_soundfile_availble():
    import soundfile as sf

if is_vision_available():
    from PIL import Image




def      UpperCAmelCase__			(				__snake_case=""				) ->			int:
  _A							     =     tempfile.mkdtemp()
  return os.path.join(__snake_case    ,					str(uuid.uuida()				) + suffix				)



@require_soundfile
@require_torch
class 		_snake_case   (      unittest.TestCase					):





 """simple docstring"""



 def lowercase_		(    self       )						->				str:




   """simple docstring"""







   _A							     =     torch.rand(1_2						,     dtype=torch.floataa       ) - 0.5
   _A							     =     AgentAudio(A_       )
   _A							     =     str(agent_type.to_string()       )

   # Ensure that the tensor and the agent_type's tensor are the same
   self.assertTrue(torch.allclose(A_						,     agent_type.to_raw()						,     atol=1e-4       )       )

   del agent_type

   # Ensure the path remains even after the object deletion
   self.assertTrue(os.path.exists(A_       )       )

   # Ensure that the file contains the same value as the original tensor
   _A     , _A							     =     sf.read(A_       )
   self.assertTrue(torch.allclose(A_						,     torch.tensor(A_       )						,     atol=1e-4       )       )



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




   """simple docstring"""







   _A							     =     torch.rand(1_2						,     dtype=torch.floataa       ) - 0.5
   _A							     =     get_new_path(suffix='''.wav'''       )
   sf.write(A_						,     A_						,     1_6_0_0_0       )

   _A							     =     AgentAudio(A_       )

   self.assertTrue(torch.allclose(A_						,     agent_type.to_raw()						,     atol=1e-4       )       )
   self.assertEqual(agent_type.to_string()						,     A_       )







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





 """simple docstring"""



 def lowercase_		(    self       )						->				int:




   """simple docstring"""







   _A							     =     torch.randint(0						,     2_5_6						,     (6_4, 6_4, 3)       )
   _A							     =     AgentImage(A_       )
   _A							     =     str(agent_type.to_string()       )

   # Ensure that the tensor and the agent_type's tensor are the same
   self.assertTrue(torch.allclose(A_						,     agent_type._tensor						,     atol=1e-4       )       )

   self.assertIsInstance(agent_type.to_raw()						,     Image.Image       )

   # Ensure the path remains even after the object deletion
   del agent_type
   self.assertTrue(os.path.exists(A_       )       )
 def lowercase_		(    self       )						->				List[str]:




   """simple docstring"""







   _A							     =     Path(get_tests_dir('''fixtures/tests_samples/COCO'''       )       ) / '''000000039769.png'''
   _A							     =     Image.open(A_       )
   _A							     =     AgentImage(A_       )

   self.assertTrue(path.samefile(agent_type.to_string()       )       )
   self.assertTrue(image == agent_type.to_raw()       )

   # Ensure the path remains even after the object deletion
   del agent_type
   self.assertTrue(os.path.exists(A_       )       )



 def lowercase_		(    self       )						->				Tuple:




   """simple docstring"""







   _A							     =     Path(get_tests_dir('''fixtures/tests_samples/COCO'''       )       ) / '''000000039769.png'''
   _A							     =     Image.open(A_       )
   _A							     =     AgentImage(A_       )

   self.assertFalse(path.samefile(agent_type.to_string()       )       )
   self.assertTrue(image == agent_type.to_raw()       )

   # Ensure the path remains even after the object deletion
   del agent_type
   self.assertTrue(os.path.exists(A_       )       )







class 		_snake_case   (      unittest.TestCase					):





 """simple docstring"""



 def lowercase_		(    self       )						->				List[Any]:




   """simple docstring"""







   _A							     =     '''Hey!'''
   _A							     =     AgentText(A_       )

   self.assertEqual(A_						,     agent_type.to_string()       )
   self.assertEqual(A_						,     agent_type.to_raw()       )
   self.assertEqual(A_						,     A_       )






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'donut-swin'

 __UpperCamelCase       =      {
     'num_attention_heads': 'num_heads',
     'num_hidden_layers': 'num_layers',
 }

 def __init__(  self,		A_=224,		A_=4,		A_=3,		A_=96,		A_=[2, 2, 6, 2],		A_=[3, 6, 12, 24],		A_=7,		A_=4.0,		A_=True,		A_=0.0,		A_=0.0,		A_=0.1,		A_="gelu",		A_=False,		A_=0.02,		A_=1E-5,		**A_,		)	->       List[str]:
    super().__init__(**A_		)

    UpperCAmelCase__						=image_size
    UpperCAmelCase__						=patch_size
    UpperCAmelCase__						=num_channels
    UpperCAmelCase__						=embed_dim
    UpperCAmelCase__						=depths
    UpperCAmelCase__						=len(A_		)
    UpperCAmelCase__						=num_heads
    UpperCAmelCase__						=window_size
    UpperCAmelCase__						=mlp_ratio
    UpperCAmelCase__						=qkv_bias
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=drop_path_rate
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=use_absolute_embeddings
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=initializer_range
    # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
    # this indicates the channel dimension after the last stage of the model
    UpperCAmelCase__						=int(embed_dim * 2 ** (len(A_		) - 1)		)



