





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
      import torch

      from transformers import AutoModelForImageClassification

if is_vision_available():
      from transformers import AutoImageProcessor





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







       """simple docstring"""





       @slow
       def  __UpperCAmelCase			(     self						:						Optional[Any]	)    ->       str:
         _lowercase						    =			AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
         _lowercase						    =			AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
         model.to(__A	)

         from datasets import load_dataset

         _lowercase						    =			load_dataset('nielsr/rvlcdip-demo'	)

         _lowercase						    =			dataset['train'][0]['image'].convert('RGB'	)

         _lowercase						    =			image_processor(__A    ,return_tensors='pt'	).to(__A	)

         # forward pass
         with torch.no_grad():
           _lowercase						    =			model(**__A	)
           _lowercase						    =			outputs.logits

         _lowercase						    =			torch.Size((1, 16)	)
         self.assertEqual(logits.shape    ,__A	)

         _lowercase						    =			torch.tensor(
             [-0.4158, -0.4092, -0.4347]    ,device=__A    ,dtype=torch.float    ,)
         self.assertTrue(torch.allclose(logits[0, :3]    ,__A    ,atol=1e-4	)	)





"""simple docstring"""






from typing import Any







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validation(
				    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       )
				# Creates data structures and fill initial step
				_lowerCamelCase	:     dict       =       {}
				_lowerCamelCase	:     dict       =       {}
				for state in states_space:
								_lowerCamelCase	:     Optional[Any]       =       observations_space[0]
								_lowerCamelCase	:     int       =       (
								    initial_probabilities[state] * emission_probabilities[state][observation]
								)
								_lowerCamelCase	:     List[Any]       =       None

				# Fills the data structure with the probabilities of
				# different transitions and pointers to previous states
				for o in range(1				,       len(lowercase__						)						):
								_lowerCamelCase	:     List[str]       =       observations_space[o]
								_lowerCamelCase	:     str       =       observations_space[o - 1]
								for state in states_space:
												# Calculates the argmax for probability function
												_lowerCamelCase	:     Dict       =       ''
												_lowerCamelCase	:     int       =       -1
												for k_state in states_space:
																_lowerCamelCase	:     Any       =       (
																    probabilities[(k_state, prior_observation)]
																    * transition_probabilities[k_state][state]
																    * emission_probabilities[state][observation]
																)
																if probability > max_probability:
																				_lowerCamelCase	:     Dict       =       probability
																				_lowerCamelCase	:     int       =       k_state

            # Update probabilities and pointers dicts
												_lowerCamelCase	:     List[str]       =       (
												    probabilities[(arg_max, prior_observation)]
												    * transition_probabilities[arg_max][state]
												    * emission_probabilities[state][observation]
												)

												_lowerCamelCase	:     Union[str, Any]       =       arg_max

    # The final observation
				_lowerCamelCase	:     Tuple       =       observations_space[len(lowercase__						) - 1]

				# argmax for given final observation
				_lowerCamelCase	:     Optional[int]       =       ''
				_lowerCamelCase	:     str       =       -1
				for k_state in states_space:
								_lowerCamelCase	:     Optional[int]       =       probabilities[(k_state, final_observation)]
								if probability > max_probability:
												_lowerCamelCase	:     int       =       probability
												_lowerCamelCase	:     Optional[int]       =       k_state
				_lowerCamelCase	:     Any       =       arg_max

				# Process pointers backwards
				_lowerCamelCase	:     Optional[Any]       =       last_state
				_lowerCamelCase	:     Union[str, Any]       =       []
				for o in range(len(lowercase__						) - 1				,       -1				,       -1						):
								result.append(lowercase__						)
								_lowerCamelCase	:     Dict       =       pointers[previous, observations_space[o]]
				result.reverse()

				return result







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validate_not_empty(
				    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       )
				_validate_lists(lowercase__				,       lowercase__						)
				_validate_dicts(
				    lowercase__				,       lowercase__				,       lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				if not all(
				    [
				        observations_space,
				        states_space,
				        initial_probabilities,
				        transition_probabilities,
				        emission_probabilities,
				    ]						):
								raise ValueError('There\'s an empty parameter'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_validate_list(lowercase__				,       'observations_space'						)
				_validate_list(lowercase__				,       'states_space'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				if not isinstance(_object				,       lowercase__						):
								_lowerCamelCase	:     Tuple       =       f'''{var_name} must be a list'''
								raise ValueError(lowercase__						)
				else:
								for x in _object:
												if not isinstance(lowercase__				,       lowercase__						):
																_lowerCamelCase	:     int       =       f'''{var_name} must be a list of strings'''
																raise ValueError(lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validate_dict(lowercase__				,       'initial_probabilities'				,       lowercase__						)
				_validate_nested_dict(lowercase__				,       'transition_probabilities'						)
				_validate_nested_dict(lowercase__				,       'emission_probabilities'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_validate_dict(_object				,       lowercase__				,       lowercase__						)
				for x in _object.values():
								_validate_dict(lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__ = False						):
				if not isinstance(_object				,       lowercase__						):
								_lowerCamelCase	:     Tuple       =       f'''{var_name} must be a dict'''
								raise ValueError(lowercase__						)
				if not all(isinstance(lowercase__				,       lowercase__						) for x in _object						):
								_lowerCamelCase	:     Optional[int]       =       f'''{var_name} all keys must be strings'''
								raise ValueError(lowercase__						)
				if not all(isinstance(lowercase__				,       lowercase__						) for x in _object.values()						):
								_lowerCamelCase	:     Optional[int]       =       'nested dictionary ' if nested else ''
								_lowerCamelCase	:     Optional[Any]       =       f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
								raise ValueError(lowercase__						)


if __name__ == "__main__":
			from doctest import testmod

			testmod()