


'''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        __snake_case	(       _SCREAMING_SNAKE_CASE):







				"""simple docstring"""



				lowercase       			= 'char'
				lowercase       			= 'bpe'
				lowercase       			= 'wp'







__A :  List[str]    							=				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







				"""simple docstring"""



				lowercase       			= ['image_processor', 'char_tokenizer']
				lowercase       			= 'ViTImageProcessor'
				lowercase       			= 'MgpstrTokenizer'

				def __init__(       self   :  List[Any]   ,						lowerCamelCase   :  List[str]=None   ,						lowerCamelCase   :  Any=None   ,						**lowerCamelCase   :  List[Any]		)       ->  Union[str, Any]:
						lowerCAmelCase_      :				Union[str, 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_      :				Dict      =       kwargs.pop("""feature_extractor"""		)

						lowerCAmelCase_      :				Optional[int]      =       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_      :				Optional[Any]      =       tokenizer
						lowerCAmelCase_      :				Dict      =       AutoTokenizer.from_pretrained("""gpt2"""		)
						lowerCAmelCase_      :				int      =       AutoTokenizer.from_pretrained("""bert-base-uncased"""		)

						super().__init__(lowerCamelCase_   ,						lowerCamelCase_		)

				def __call__(       self   :  Dict   ,						lowerCamelCase   :  Dict=None   ,						lowerCamelCase   :  Optional[int]=None   ,						lowerCamelCase   :  Any=None   ,						**lowerCamelCase   :  Any		)       ->  Union[str, Any]:

						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_      :				Union[str, Any]      =       self.image_processor(lowerCamelCase_   ,						return_tensors=lowerCamelCase_   ,						**lowerCamelCase_		)
						if text is not None:
								lowerCAmelCase_      :				Any      =       self.char_tokenizer(lowerCamelCase_   ,						return_tensors=lowerCamelCase_   ,						**lowerCamelCase_		)

						if text is None:
								return inputs
						elif images is None:
								return encodings
						else:
								lowerCAmelCase_      :				Any      =       encodings["""input_ids"""]
								return inputs

				def __lowercase       (       self   :  int   ,						lowerCamelCase   :  str		)       ->  Dict:
						lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				Optional[int]      =       sequences
						lowerCAmelCase_      :				Union[str, Any]      =       char_preds.size(0		)

						lowerCAmelCase_,							lowerCAmelCase_      :				Optional[int]      =       self._decode_helper(lowerCamelCase_   ,						"""char"""		)
						lowerCAmelCase_,							lowerCAmelCase_      :				Optional[Any]      =       self._decode_helper(lowerCamelCase_   ,						"""bpe"""		)
						lowerCAmelCase_,							lowerCAmelCase_      :				Optional[int]      =       self._decode_helper(lowerCamelCase_   ,						"""wp"""		)

						lowerCAmelCase_      :				List[str]      =       []
						lowerCAmelCase_      :				Tuple      =       []
						for i in range(lowerCamelCase_		):
								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_      :				Tuple      =       scores.index(max(lowerCamelCase_		)		)
								final_strs.append(strs[max_score_index]		)
								final_scores.append(scores[max_score_index]		)

						lowerCAmelCase_      :				List[str]      =       {}
						lowerCAmelCase_      :				Optional[int]      =       final_strs
						lowerCAmelCase_      :				List[Any]      =       final_scores
						lowerCAmelCase_      :				Union[str, Any]      =       char_strs
						lowerCAmelCase_      :				int      =       bpe_strs
						lowerCAmelCase_      :				int      =       wp_strs
						return out

				def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  int		)       ->  Optional[Any]:

						if format == DecodeType.CHARACTER:
								lowerCAmelCase_      :				Optional[Any]      =       self.char_decode
								lowerCAmelCase_      :				int      =       1
								lowerCAmelCase_      :				str      =       """[s]"""
						elif format == DecodeType.BPE:
								lowerCAmelCase_      :				List[Any]      =       self.bpe_decode
								lowerCAmelCase_      :				str      =       2
								lowerCAmelCase_      :				List[Any]      =       """#"""
						elif format == DecodeType.WORDPIECE:
								lowerCAmelCase_      :				Optional[Any]      =       self.wp_decode
								lowerCAmelCase_      :				Dict      =       1_02
								lowerCAmelCase_      :				Dict      =       """[SEP]"""
						else:
								raise ValueError(F'Format {format} is not supported.'		)

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

						for index in range(lowerCamelCase_		):
								lowerCAmelCase_      :				Union[str, Any]      =       preds_str[index].find(lowerCamelCase_		)
								lowerCAmelCase_      :				Dict      =       preds_str[index][:pred_eos]
								lowerCAmelCase_      :				Any      =       preds_index[index].cpu().tolist()
								lowerCAmelCase_      :				str      =       pred_index.index(lowerCamelCase_		) if eos_token in pred_index else -1
								lowerCAmelCase_      :				Tuple      =       preds_max_prob[index][: pred_eos_index + 1]
								lowerCAmelCase_      :				Optional[int]      =       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 __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  int		)       ->  str:
						lowerCAmelCase_      :				List[str]      =       [seq.replace(""" """   ,						""""""		) for seq in self.char_tokenizer.batch_decode(lowerCamelCase_		)]
						return decode_strs

				def __lowercase       (       self   :  Dict   ,						lowerCamelCase   :  List[str]		)       ->  Dict:
						return self.bpe_tokenizer.batch_decode(lowerCamelCase_		)





				def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  Dict		)       ->  int:
						lowerCAmelCase_      :				Optional[int]      =       [seq.replace(""" """   ,						""""""		) for seq in self.wp_tokenizer.batch_decode(lowerCamelCase_		)]
						return decode_strs







'''simple docstring'''






from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .notes_encoder import SpectrogramNotesEncoder
  from .continous_encoder import SpectrogramContEncoder
  from .pipeline_spectrogram_diffusion import (
      SpectrogramContEncoder,
      SpectrogramDiffusionPipeline,
      TaFilmDecoder,
  )

try:
  if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
  from .midi_utils import MidiProcessor



