
'''simple docstring'''


import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
					import torch

class 		__lowercase (     _lowerCAmelCase			):
			lowerCamelCase	:						int							=	"char"
			lowerCamelCase	:						str							=	"bpe"
			lowerCamelCase	:						List[str]							=	"wp"
__lowercase							:      Tuple		 =   (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)

class 		__lowercase (     _lowerCAmelCase			):
			lowerCamelCase	:						Any							=	["image_processor", "char_tokenizer"]
			lowerCamelCase	:						Union[str, Any]							=	"ViTImageProcessor"
			lowerCamelCase	:						int							=	"MgpstrTokenizer"

			def __init__(self	,      A=None	,      A=None	,      **A					):
								lowerCamelCase_    :					Any   =						None
								if "feature_extractor" in kwargs:
													warnings.warn(
													    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
													    ''' instead.'''	,      _lowerCamelCase	,      )
													lowerCamelCase_    :					int   =						kwargs.pop('''feature_extractor'''					)

								lowerCamelCase_    :					str   =						image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
													raise ValueError('''You need to specify an `image_processor`.'''					)
								if tokenizer is None:
													raise ValueError('''You need to specify a `tokenizer`.'''					)

								lowerCamelCase_    :					Union[str, Any]   =						tokenizer
								lowerCamelCase_    :					int   =						AutoTokenizer.from_pretrained('''gpt2'''					)
								lowerCamelCase_    :					Any   =						AutoTokenizer.from_pretrained('''bert-base-uncased'''					)

								super().__init__(_lowerCamelCase	,      _lowerCamelCase					)

			def __call__(self	,      A=None	,      A=None	,      A=None	,      **A					):
								if images is None and text is None:
													raise ValueError('''You need to specify either an `images` or `text` input to process.'''					)

								if images is not None:
													lowerCamelCase_    :					str   =						self.image_processor(_lowerCamelCase	,      return_tensors=_lowerCamelCase	,      **_lowerCamelCase					)
								if text is not None:
													lowerCamelCase_    :					List[Any]   =						self.char_tokenizer(_lowerCamelCase	,      return_tensors=_lowerCamelCase	,      **_lowerCamelCase					)

								if text is None:
													return inputs
								elif images is None:
													return encodings
								else:
													lowerCamelCase_    :					List[Any]   =						encodings['''input_ids''']
													return inputs

			def 				UpperCAmelCase__       (self	,      A					):
								lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_    :					Tuple   =						sequences
								lowerCamelCase_    :					List[Any]   =						char_preds.size(0					)

								lowerCamelCase_,					lowerCamelCase_    :					List[str]   =						self._decode_helper(_lowerCamelCase	,      '''char'''					)
								lowerCamelCase_,					lowerCamelCase_    :					Tuple   =						self._decode_helper(_lowerCamelCase	,      '''bpe'''					)
								lowerCamelCase_,					lowerCamelCase_    :					List[Any]   =						self._decode_helper(_lowerCamelCase	,      '''wp'''					)

								lowerCamelCase_    :					Tuple   =						[]
								lowerCamelCase_    :					str   =						[]
								for i in range(_lowerCamelCase					):
													lowerCamelCase_    :					str   =						[char_scores[i], bpe_scores[i], wp_scores[i]]
													lowerCamelCase_    :					Any   =						[char_strs[i], bpe_strs[i], wp_strs[i]]
													lowerCamelCase_    :					Dict   =						scores.index(max(_lowerCamelCase					)					)
													final_strs.append(strs[max_score_index]					)
													final_scores.append(scores[max_score_index]					)

								lowerCamelCase_    :					str   =						{}
								lowerCamelCase_    :					Any   =						final_strs
								lowerCamelCase_    :					Tuple   =						final_scores
								lowerCamelCase_    :					int   =						char_strs
								lowerCamelCase_    :					Union[str, Any]   =						bpe_strs
								lowerCamelCase_    :					Union[str, Any]   =						wp_strs
								return out

			def 				UpperCAmelCase__       (self	,      A	,      A					):
								if format == DecodeType.CHARACTER:
													lowerCamelCase_    :					Optional[int]   =						self.char_decode
													lowerCamelCase_    :					List[str]   =						1
													lowerCamelCase_    :					Any   =						'''[s]'''
								elif format == DecodeType.BPE:
													lowerCamelCase_    :					Any   =						self.bpe_decode
													lowerCamelCase_    :					Tuple   =						2
													lowerCamelCase_    :					List[Any]   =						'''#'''
								elif format == DecodeType.WORDPIECE:
													lowerCamelCase_    :					List[Any]   =						self.wp_decode
													lowerCamelCase_    :					int   =						1_0_2
													lowerCamelCase_    :					Union[str, Any]   =						'''[SEP]'''
								else:
													raise ValueError(F"""Format {format} is not supported."""					)

								lowerCamelCase_,					lowerCamelCase_    :					Union[str, Any]   =						[], []
								lowerCamelCase_    :					str   =						pred_logits.size(0					)
								lowerCamelCase_    :					Dict   =						pred_logits.size(1					)
								lowerCamelCase_,					lowerCamelCase_    :					Optional[int]   =						pred_logits.topk(1	,      dim=-1	,      largest=_lowerCamelCase	,      sorted=_lowerCamelCase					)
								lowerCamelCase_    :					int   =						preds_index.view(-1	,      _lowerCamelCase					)[:, 1:]
								lowerCamelCase_    :					Tuple   =						decoder(_lowerCamelCase					)
								lowerCamelCase_,					lowerCamelCase_    :					Dict   =						torch.nn.functional.softmax(_lowerCamelCase	,      dim=2					).max(dim=2					)
								lowerCamelCase_    :					Optional[Any]   =						preds_max_prob[:, 1:]

								for index in range(_lowerCamelCase					):
													lowerCamelCase_    :					str   =						preds_str[index].find(_lowerCamelCase					)
													lowerCamelCase_    :					Tuple   =						preds_str[index][:pred_eos]
													lowerCamelCase_    :					Optional[int]   =						preds_index[index].cpu().tolist()
													lowerCamelCase_    :					List[str]   =						pred_index.index(_lowerCamelCase					) if eos_token in pred_index else -1
													lowerCamelCase_    :					Tuple   =						preds_max_prob[index][: pred_eos_index + 1]
													lowerCamelCase_    :					List[str]   =						pred_max_prob.cumprod(dim=0					)[-1] if pred_max_prob.nelement() != 0 else 0.0
													dec_strs.append(_lowerCamelCase					)
													conf_scores.append(_lowerCamelCase					)

								return dec_strs, conf_scores

			def 				UpperCAmelCase__       (self	,      A					):
								lowerCamelCase_    :					Any   =						[seq.replace(''' '''	,      ''''''					) for seq in self.char_tokenizer.batch_decode(_lowerCamelCase					)]
								return decode_strs

			def 				UpperCAmelCase__       (self	,      A					):
								return self.bpe_tokenizer.batch_decode(_lowerCamelCase					)






			def 				UpperCAmelCase__       (self	,      A					):
								lowerCamelCase_    :					Optional[Any]   =						[seq.replace(''' '''	,      ''''''					) for seq in self.wp_tokenizer.batch_decode(_lowerCamelCase					)]
								return decode_strs










"""simple docstring"""


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 ViTImageProcessor


class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     List[str],       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     Union[str, Any]=13,       _lowerCamelCase :     Any=3,       _lowerCamelCase :     Optional[int]=2_24,       _lowerCamelCase :     str=30,       _lowerCamelCase :     Dict=4_00,       _lowerCamelCase :     Union[str, Any]=True,       _lowerCamelCase :     Any=None,       _lowerCamelCase :     Optional[Any]=True,       _lowerCamelCase :     Any=[0.5, 0.5, 0.5],       _lowerCamelCase :     List[str]=[0.5, 0.5, 0.5],       ):

     '''simple docstring'''
     __A									= size if size is not None else {'''height''': 18, '''width''': 18}
     __A									= parent
     __A									= batch_size
     __A									= num_channels
     __A									= image_size
     __A									= min_resolution
     __A									= max_resolution
     __A									= do_resize
     __A									= size
     __A									= do_normalize
     __A									= image_mean
     __A									= image_std






   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
     }





@require_torch
@require_vision
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	str						 = ViTImageProcessor if is_vision_available() else None


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= EfficientFormerImageProcessorTester(self  )


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     return self.image_proc_tester.prepare_image_processor_dict()


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_mean'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_std'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_normalize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_resize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''size'''  )  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     pass


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       Image.Image  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random numpy tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       numpify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       np.ndarray  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )






   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       torchify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       torch.Tensor  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )




