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   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'char'
							_UpperCAmelCase						    =     'bpe'
							_UpperCAmelCase						    =     'wp'


A__           =							(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     ['image_processor', 'char_tokenizer']
							_UpperCAmelCase						    =     'ViTImageProcessor'
							_UpperCAmelCase						    =     'MgpstrTokenizer'







							def __init__(				self     :    List[str]	,					__snake_case     :    Dict=None	,					__snake_case     :    Tuple=None	,					**__snake_case     :    Tuple		):
										lowerCamelCase						:Optional[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.'''	,					__snake_case	,					)
													lowerCamelCase						:List[Any]						  =     kwargs.pop('''feature_extractor'''		)

										lowerCamelCase						:List[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						:Dict						  =     tokenizer
										lowerCamelCase						:Any						  =     AutoTokenizer.from_pretrained('''gpt2'''		)
										lowerCamelCase						:Optional[int]						  =     AutoTokenizer.from_pretrained('''bert-base-uncased'''		)

										super().__init__(__snake_case	,					__snake_case		)







							def __call__(				self     :    Optional[Any]	,					__snake_case     :    Tuple=None	,					__snake_case     :    Union[str, Any]=None	,					__snake_case     :    Dict=None	,					**__snake_case     :    Tuple		):
										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						:List[str]						  =     self.image_processor(__snake_case	,					return_tensors=__snake_case	,					**__snake_case		)
										if text is not None:
													lowerCamelCase						:Any						  =     self.char_tokenizer(__snake_case	,					return_tensors=__snake_case	,					**__snake_case		)

										if text is None:
													return inputs
										elif images is None:
													return encodings
										else:
													lowerCamelCase						:Tuple						  =     encodings['''input_ids''']
													return inputs







							def 				snake_case  (				self     :    Optional[int]	,					__snake_case     :    Tuple		):
										lowerCamelCase						:Any						  =     sequences
										lowerCamelCase						:Union[str, Any]						  =     char_preds.size(0		)

										lowerCamelCase						:Tuple						  =     self._decode_helper(__snake_case	,					'''char'''		)
										lowerCamelCase						:Tuple						  =     self._decode_helper(__snake_case	,					'''bpe'''		)
										lowerCamelCase						:Optional[Any]						  =     self._decode_helper(__snake_case	,					'''wp'''		)

										lowerCamelCase						:Optional[Any]						  =     []
										lowerCamelCase						:Dict						  =     []
										for i in range(__snake_case		):
													lowerCamelCase						:Optional[int]						  =     [char_scores[i], bpe_scores[i], wp_scores[i]]
													lowerCamelCase						:Optional[int]						  =     [char_strs[i], bpe_strs[i], wp_strs[i]]
													lowerCamelCase						:Dict						  =     scores.index(max(__snake_case		)		)
													final_strs.append(strs[max_score_index]		)
													final_scores.append(scores[max_score_index]		)

										lowerCamelCase						:Tuple						  =     {}
										lowerCamelCase						:int						  =     final_strs
										lowerCamelCase						:str						  =     final_scores
										lowerCamelCase						:Dict						  =     char_strs
										lowerCamelCase						:Optional[int]						  =     bpe_strs
										lowerCamelCase						:str						  =     wp_strs
										return out







							def 				snake_case  (				self     :    List[str]	,					__snake_case     :    Tuple	,					__snake_case     :    List[Any]		):
										if format == DecodeType.CHARACTER:
													lowerCamelCase						:List[Any]						  =     self.char_decode
													lowerCamelCase						:List[Any]						  =     1
													lowerCamelCase						:Tuple						  =     '''[s]'''
										elif format == DecodeType.BPE:
													lowerCamelCase						:Tuple						  =     self.bpe_decode
													lowerCamelCase						:Dict						  =     2
													lowerCamelCase						:str						  =     '''#'''
										elif format == DecodeType.WORDPIECE:
													lowerCamelCase						:Dict						  =     self.wp_decode
													lowerCamelCase						:List[Any]						  =     102
													lowerCamelCase						:List[Any]						  =     '''[SEP]'''
										else:
													raise ValueError(F"Format {format} is not supported."		)

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

										for index in range(__snake_case		):
													lowerCamelCase						:str						  =     preds_str[index].find(__snake_case		)
													lowerCamelCase						:List[Any]						  =     preds_str[index][:pred_eos]
													lowerCamelCase						:Union[str, Any]						  =     preds_index[index].cpu().tolist()
													lowerCamelCase						:Dict						  =     pred_index.index(__snake_case		) if eos_token in pred_index else -1
													lowerCamelCase						:Optional[Any]						  =     preds_max_prob[index][: pred_eos_index + 1]
													lowerCamelCase						:Any						  =     pred_max_prob.cumprod(dim=0		)[-1] if pred_max_prob.nelement() != 0 else 0.0
													dec_strs.append(__snake_case		)
													conf_scores.append(__snake_case		)

										return dec_strs, conf_scores







							def 				snake_case  (				self     :    Union[str, Any]	,					__snake_case     :    Dict		):
										lowerCamelCase						:int						  =     [seq.replace(''' '''	,					''''''		) for seq in self.char_tokenizer.batch_decode(__snake_case		)]
										return decode_strs







							def 				snake_case  (				self     :    List[Any]	,					__snake_case     :    Tuple		):
										return self.bpe_tokenizer.batch_decode(__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    str		):
										lowerCamelCase						:Dict						  =     [seq.replace(''' '''	,					''''''		) for seq in self.wp_tokenizer.batch_decode(__snake_case		)]
										return decode_strs




import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class   _lowerCAmelCase		(     unittest.TestCase				):







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:List[Any]						  =     inspect.getfile(accelerate.test_utils		)
										lowerCamelCase						:Dict						  =     os.path.sep.join(
										    mod_file.split(os.path.sep		)[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']		)

										from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

										lowerCamelCase						:Any						  =     test_metrics







							@require_cpu
							def 				snake_case  (				self     :    Dict		):
										debug_launcher(self.test_metrics.main	,					num_processes=1		)







							@require_cpu
							def 				snake_case  (				self     :    int		):
										debug_launcher(self.test_metrics.main		)







							@require_single_gpu
							def 				snake_case  (				self     :    Any		):
										self.test_metrics.main()







							@require_multi_gpu
							def 				snake_case  (				self     :    Optional[int]		):
										print(F"Found {torch.cuda.device_count()} devices."		)
										lowerCamelCase						:Optional[int]						  =     ['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
										with patch_environment(omp_num_threads=1		):
													execute_subprocess_async(__snake_case	,					env=os.environ.copy()		)



