

'''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 _UpperCamelCase							(							lowerCAmelCase__   ):
			'''simple docstring'''







			lowerCamelCase__           ="char"
			lowerCamelCase__           ="bpe"
			lowerCamelCase__           ="wp"

a_						     =    (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class _UpperCamelCase							(							lowerCAmelCase__   ):
			'''simple docstring'''







			lowerCamelCase__           =["image_processor", "char_tokenizer"]
			lowerCamelCase__           ="ViTImageProcessor"
			lowerCamelCase__           ="MgpstrTokenizer"
			def __init__(			self							: int      ,      a							: List[Any]=None      ,      a							: Optional[Any]=None      ,      **a							: Tuple	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      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."      ,      _SCREAMING_SNAKE_CASE      ,      )
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       kwargs.pop("feature_extractor"	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       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`."	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       tokenizer
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       AutoTokenizer.from_pretrained("gpt2"	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       AutoTokenizer.from_pretrained("bert-base-uncased"	)

				super().__init__(_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE	)
			def __call__(			self							: List[str]      ,      a							: Union[str, Any]=None      ,      a							: Tuple=None      ,      a							: List[Any]=None      ,      **a							: int	)							->       Optional[int]:

				"""simple docstring"""

				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:
					SCREAMING_SNAKE_CASE   :      str				     =       self.image_processor(_SCREAMING_SNAKE_CASE      ,      return_tensors=_SCREAMING_SNAKE_CASE      ,      **_SCREAMING_SNAKE_CASE	)
				if text is not None:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       self.char_tokenizer(_SCREAMING_SNAKE_CASE      ,      return_tensors=_SCREAMING_SNAKE_CASE      ,      **_SCREAMING_SNAKE_CASE	)

				if text is None:
					return inputs
				elif images is None:
					return encodings
				else:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       encodings["input_ids"]
					return inputs
			def 							__UpperCamelCase (			self							: int      ,      a							: List[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       sequences
				SCREAMING_SNAKE_CASE   :      int				     =       char_preds.size(0	)

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       self._decode_helper(_SCREAMING_SNAKE_CASE      ,      "char"	)
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       self._decode_helper(_SCREAMING_SNAKE_CASE      ,      "bpe"	)
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self._decode_helper(_SCREAMING_SNAKE_CASE      ,      "wp"	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       []
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       []
				for i in range(_SCREAMING_SNAKE_CASE	):
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [char_scores[i], bpe_scores[i], wp_scores[i]]
					SCREAMING_SNAKE_CASE   :      str				     =       [char_strs[i], bpe_strs[i], wp_strs[i]]
					SCREAMING_SNAKE_CASE   :      Any				     =       scores.index(max(_SCREAMING_SNAKE_CASE	)	)
					final_strs.append(strs[max_score_index]	)
					final_scores.append(scores[max_score_index]	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       {}
				SCREAMING_SNAKE_CASE   :      List[str]				     =       final_strs
				SCREAMING_SNAKE_CASE   :      Dict				     =       final_scores
				SCREAMING_SNAKE_CASE   :      Any				     =       char_strs
				SCREAMING_SNAKE_CASE   :      Tuple				     =       bpe_strs
				SCREAMING_SNAKE_CASE   :      Any				     =       wp_strs
				return out
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Optional[int]      ,      a							: Union[str, Any]	)							->       List[Any]:

				"""simple docstring"""

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

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

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

				return dec_strs, conf_scores
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Dict	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       [seq.replace(" "      ,      ""	) for seq in self.char_tokenizer.batch_decode(_SCREAMING_SNAKE_CASE	)]
				return decode_strs
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: str	)							->       Any:

				"""simple docstring"""

				return self.bpe_tokenizer.batch_decode(_SCREAMING_SNAKE_CASE	)






			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: Tuple	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       [seq.replace(" "      ,      ""	) for seq in self.wp_tokenizer.batch_decode(_SCREAMING_SNAKE_CASE	)]
				return decode_strs


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


a_						     =    logging.get_logger(__name__)

a_						     =    {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           ='gptj'
			lowerCamelCase__           ={
			    'max_position_embeddings': 'n_positions',
			    'hidden_size': 'n_embd',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}
			def __init__(			self							: Dict      ,      a							: Optional[Any]=5_0400      ,      a							: List[str]=2048      ,      a							: List[Any]=4096      ,      a							: int=28      ,      a							: Union[str, Any]=16      ,      a							: List[Any]=64      ,      a							: int=None      ,      a							: Optional[int]="gelu_new"      ,      a							: Optional[Any]=0.0      ,      a							: Any=0.0      ,      a							: Union[str, Any]=0.0      ,      a							: Union[str, Any]=1e-5      ,      a							: Any=0.02      ,      a							: Optional[int]=True      ,      a							: Tuple=5_0256      ,      a							: Union[str, Any]=5_0256      ,      a							: List[Any]=False      ,      **a							: str      ,      )							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      int				     =       n_positions
				SCREAMING_SNAKE_CASE   :      Tuple				     =       n_embd
				SCREAMING_SNAKE_CASE   :      Tuple				     =       n_layer
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       n_head
				SCREAMING_SNAKE_CASE   :      Tuple				     =       n_inner
				SCREAMING_SNAKE_CASE   :      Any				     =       rotary_dim
				SCREAMING_SNAKE_CASE   :      str				     =       activation_function
				SCREAMING_SNAKE_CASE   :      int				     =       resid_pdrop
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       embd_pdrop
				SCREAMING_SNAKE_CASE   :      Tuple				     =       attn_pdrop
				SCREAMING_SNAKE_CASE   :      List[str]				     =       layer_norm_epsilon
				SCREAMING_SNAKE_CASE   :      int				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Tuple				     =       use_cache

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       bos_token_id
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       eos_token_id

				super().__init__(
				    bos_token_id=a      ,      eos_token_id=a      ,      tie_word_embeddings=a      ,      **a	)






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def __init__(			self							: Optional[int]      ,      a							: PretrainedConfig      ,      a							: str = "default"      ,      a							: List[PatchingSpec] = None      ,      a							: bool = False      ,      )							->       Any:

				"""simple docstring"""

				super().__init__(a      ,      task=a      ,      patching_specs=a      ,      use_past=a	)
				if not getattr(self._config      ,      "pad_token_id"      ,      a	):
					# TODO: how to do that better?
					SCREAMING_SNAKE_CASE   :      Dict				     =       0
			@property
			def 							__UpperCamelCase (			self							: Any	)							->       Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}	)
				if self.use_past:
					self.fill_with_past_key_values_(a      ,      direction="inputs"	)
					SCREAMING_SNAKE_CASE   :      int				     =       {0: "batch", 1: "past_sequence + sequence"}
				else:
					SCREAMING_SNAKE_CASE   :      Any				     =       {0: "batch", 1: "sequence"}

				return common_inputs
			@property
			def 							__UpperCamelCase (			self							: Any	)							->       int:

				"""simple docstring"""

				return self._config.n_layer
			@property
			def 							__UpperCamelCase (			self							: str	)							->       int:

				"""simple docstring"""

				return self._config.n_head
			def 							__UpperCamelCase (			self							: str      ,      a							: PreTrainedTokenizer      ,      a							: int = -1      ,      a							: int = -1      ,      a							: bool = False      ,      a							: Optional[TensorType] = None      ,      )							->       Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       super(a      ,      self	).generate_dummy_inputs(
				    a      ,      batch_size=a      ,      seq_length=a      ,      is_pair=a      ,      framework=a	)

				# We need to order the input in the way they appears in the forward()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       OrderedDict({"input_ids": common_inputs["input_ids"]}	)

				# Need to add the past_keys
				if self.use_past:
					if not is_torch_available():
						raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
					else:
						import torch

						SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       common_inputs["input_ids"].shape
						# Not using the same length for past_key_values
						SCREAMING_SNAKE_CASE   :      Any				     =       seqlen + 2
						SCREAMING_SNAKE_CASE   :      Dict				     =       (
						    batch,
						    self.num_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // self.num_attention_heads,
						)
						SCREAMING_SNAKE_CASE   :      str				     =       [
						    (torch.zeros(a	), torch.zeros(a	)) for _ in range(self.num_layers	)
						]

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       common_inputs["attention_mask"]
				if self.use_past:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       ordered_inputs["attention_mask"].dtype
					SCREAMING_SNAKE_CASE   :      Any				     =       torch.cat(
					    [ordered_inputs["attention_mask"], torch.ones(a      ,      a      ,      dtype=a	)]      ,      dim=1	)

				return ordered_inputs






			@property
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       int:

				"""simple docstring"""

				return 13