import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
						import tensorflow as tf

						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

A     :  Dict					     =       logging.get_logger(__name__)


class   lowerCamelCase					(enum.Enum					):
		"""simple docstring"""







		lowerCamelCase__     =						0
		lowerCamelCase__     =						1



@add_end_docstrings(__A					)
class   lowerCamelCase					(__A					):
		"""simple docstring"""







		lowerCamelCase__     =						"""generated"""







		def __init__(							self :       List[str]  ,     *__magic_name__ :       Dict  ,     **__magic_name__ :       Optional[int]   )  ->	Tuple:
								super().__init__(*UpperCamelCase__  ,     **UpperCamelCase__   )

								self.check_model_type(
								    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
								    if self.framework == "tf"
								    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING   )







		def    __A     (							self :       Tuple  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       int=None  ,     __magic_name__ :       Union[str, Any]=None  ,     __magic_name__ :       List[str]=None  ,     __magic_name__ :       List[str]=None  ,     __magic_name__ :       Tuple=None  ,     **__magic_name__ :       List[Any]  ,     )  ->	Optional[int]:
								SCREAMING_SNAKE_CASE_          =  {}
								if truncation is not None:
														SCREAMING_SNAKE_CASE_          =  truncation

								SCREAMING_SNAKE_CASE_          =  generate_kwargs

								SCREAMING_SNAKE_CASE_          =  {}
								if return_tensors is not None and return_type is None:
														SCREAMING_SNAKE_CASE_          =  ReturnType.TENSORS if return_tensors else ReturnType.TEXT
								if return_type is not None:
														SCREAMING_SNAKE_CASE_          =  return_type

								if clean_up_tokenization_spaces is not None:
														SCREAMING_SNAKE_CASE_          =  clean_up_tokenization_spaces

								if stop_sequence is not None:
														SCREAMING_SNAKE_CASE_          =  self.tokenizer.encode(UpperCamelCase__  ,     add_special_tokens=UpperCamelCase__   )
														if len(UpperCamelCase__   ) > 1:
																				warnings.warn(
																				    "Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
																				    " the stop sequence will be used as the stop sequence string in the interim."   )
														SCREAMING_SNAKE_CASE_          =  stop_sequence_ids[0]

								return preprocess_params, forward_params, postprocess_params







		def    __A     (							self :       str  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       Optional[Any]   )  ->	int:
								return True







		def    __A     (							self :       List[Any]  ,     *__magic_name__ :       Dict  ,     __magic_name__ :       Union[str, Any]   )  ->	str:
								SCREAMING_SNAKE_CASE_          =  self.model.config.prefix if self.model.config.prefix is not None else ''
								if isinstance(args[0]  ,     UpperCamelCase__   ):
														if self.tokenizer.pad_token_id is None:
																				raise ValueError("Please make sure that the tokenizer has a pad_token_id when using a batch input"   )
														SCREAMING_SNAKE_CASE_          =  ([prefix + arg for arg in args[0]],)
														SCREAMING_SNAKE_CASE_          =  True

								elif isinstance(args[0]  ,     UpperCamelCase__   ):
														SCREAMING_SNAKE_CASE_          =  (prefix + args[0],)
														SCREAMING_SNAKE_CASE_          =  False
								else:
														raise ValueError(
														    F''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''   )
								SCREAMING_SNAKE_CASE_          =  self.tokenizer(*UpperCamelCase__  ,     padding=UpperCamelCase__  ,     truncation=UpperCamelCase__  ,     return_tensors=self.framework   )
								# This is produced by tokenizers but is an invalid generate kwargs
								if "token_type_ids" in inputs:
														del inputs["token_type_ids"]
								return inputs







		def __call__(							self :       Dict  ,     *__magic_name__ :       Union[str, Any]  ,     **__magic_name__ :       int   )  ->	Dict:
								SCREAMING_SNAKE_CASE_          =  super().__call__(*UpperCamelCase__  ,     **UpperCamelCase__   )
								if (
								    isinstance(args[0]  ,     UpperCamelCase__   )
								    and all(isinstance(UpperCamelCase__  ,     UpperCamelCase__   ) for el in args[0]   )
								    and all(len(UpperCamelCase__   ) == 1 for res in result   )
								):
														return [res[0] for res in result]
								return result







		def    __A     (							self :       Dict  ,     __magic_name__ :       Dict  ,     __magic_name__ :       Dict=TruncationStrategy.DO_NOT_TRUNCATE  ,     **__magic_name__ :       Optional[Any]   )  ->	List[Any]:
								SCREAMING_SNAKE_CASE_          =  self._parse_and_tokenize(UpperCamelCase__  ,     truncation=UpperCamelCase__  ,     **UpperCamelCase__   )
								return inputs







		def    __A     (							self :       List[str]  ,     __magic_name__ :       int  ,     **__magic_name__ :       Optional[int]   )  ->	Optional[int]:
								if self.framework == "pt":
														SCREAMING_SNAKE_CASE_          =  model_inputs['input_ids'].shape
								elif self.framework == "tf":
														SCREAMING_SNAKE_CASE_          =  tf.shape(model_inputs["input_ids"]   ).numpy()

								SCREAMING_SNAKE_CASE_          =  generate_kwargs.get("min_length"  ,     self.model.config.min_length   )
								SCREAMING_SNAKE_CASE_          =  generate_kwargs.get("max_length"  ,     self.model.config.max_length   )
								self.check_inputs(UpperCamelCase__  ,     generate_kwargs["min_length"]  ,     generate_kwargs["max_length"]   )
								SCREAMING_SNAKE_CASE_          =  self.model.generate(**UpperCamelCase__  ,     **UpperCamelCase__   )
								SCREAMING_SNAKE_CASE_          =  output_ids.shape[0]
								if self.framework == "pt":
														SCREAMING_SNAKE_CASE_          =  output_ids.reshape(UpperCamelCase__  ,     out_b // in_b  ,     *output_ids.shape[1:]   )
								elif self.framework == "tf":
														SCREAMING_SNAKE_CASE_          =  tf.reshape(UpperCamelCase__  ,     (in_b, out_b // in_b, *output_ids.shape[1:])   )
								return {"output_ids": output_ids}







		def    __A     (							self :       Optional[int]  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       List[str]=ReturnType.TEXT  ,     __magic_name__ :       Optional[Any]=False   )  ->	Dict:
								SCREAMING_SNAKE_CASE_          =  []
								for output_ids in model_outputs["output_ids"][0]:
														if return_type == ReturnType.TENSORS:
																				SCREAMING_SNAKE_CASE_          =  {F'''{self.return_name}_token_ids''': output_ids}
														elif return_type == ReturnType.TEXT:
																				SCREAMING_SNAKE_CASE_          =  {
																				    F'''{self.return_name}_text''': self.tokenizer.decode(
																				        UpperCamelCase__  ,     skip_special_tokens=UpperCamelCase__  ,     clean_up_tokenization_spaces=UpperCamelCase__  ,     )
																				}
														records.append(UpperCamelCase__   )
								return records



@add_end_docstrings(__A					)
class   lowerCamelCase					(__A					):
		"""simple docstring"""







		lowerCamelCase__     =						"""summary"""







		def __call__(							self :       Any  ,     *__magic_name__ :       int  ,     **__magic_name__ :       Optional[int]   )  ->	str:
								return super().__call__(*UpperCamelCase__  ,     **UpperCamelCase__   )







		def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Dict  ,     __magic_name__ :       Any  ,     __magic_name__ :       Union[str, Any]   )  ->	Tuple:
								if max_length < min_length:
														logger.warning(F'''Your min_length={min_length} must be inferior than your max_length={max_length}.'''   )

								if input_length < max_length:
														logger.warning(
														    F'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
														    "a summarization task, where outputs shorter than the input are typically wanted, you might "
														    F'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'''   )



@add_end_docstrings(__A					)
class   lowerCamelCase					(__A					):
		"""simple docstring"""







		lowerCamelCase__     =						"""translation"""







		def    __A     (							self :       int  ,     __magic_name__ :       Any  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       List[str]   )  ->	Dict:
								if input_length > 0.9 * max_length:
														logger.warning(
														    F'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
														    "increasing your max_length manually, e.g. translator(\'...\', max_length=400)"   )
								return True







		def    __A     (							self :       List[str]  ,     *__magic_name__ :       int  ,     __magic_name__ :       List[Any]=TruncationStrategy.DO_NOT_TRUNCATE  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       Union[str, Any]=None   )  ->	Optional[Any]:
								if getattr(self.tokenizer  ,     "_build_translation_inputs"  ,     UpperCamelCase__   ):
														return self.tokenizer._build_translation_inputs(
														    *UpperCamelCase__  ,     return_tensors=self.framework  ,     truncation=UpperCamelCase__  ,     src_lang=UpperCamelCase__  ,     tgt_lang=UpperCamelCase__   )
								else:
														return super()._parse_and_tokenize(*UpperCamelCase__  ,     truncation=UpperCamelCase__   )







		def    __A     (							self :       Any  ,     __magic_name__ :       Tuple=None  ,     __magic_name__ :       List[Any]=None  ,     **__magic_name__ :       Any   )  ->	int:
								SCREAMING_SNAKE_CASE_          =  super()._sanitize_parameters(**UpperCamelCase__   )
								if src_lang is not None:
														SCREAMING_SNAKE_CASE_          =  src_lang
								if tgt_lang is not None:
														SCREAMING_SNAKE_CASE_          =  tgt_lang
								if src_lang is None and tgt_lang is None:
														# Backward compatibility, direct arguments use is preferred.
														SCREAMING_SNAKE_CASE_          =  kwargs.get("task"  ,     self.task   )
														SCREAMING_SNAKE_CASE_          =  task.split("_"   )
														if task and len(UpperCamelCase__   ) == 4:
																				# translation, XX, to YY
																				SCREAMING_SNAKE_CASE_          =  items[1]
																				SCREAMING_SNAKE_CASE_          =  items[3]
								return preprocess_params, forward_params, postprocess_params







		def __call__(							self :       Optional[int]  ,     *__magic_name__ :       str  ,     **__magic_name__ :       List[Any]   )  ->	Any:
								return super().__call__(*UpperCamelCase__  ,     **UpperCamelCase__   )



import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


A     :  Any					     =       logging.getLogger(__name__)


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       List[Any]  ,     __magic_name__ :       Optional[Any]=-1   )  ->	Optional[Any]:
        # in NER datasets, the last column is usually reserved for NER label
        SCREAMING_SNAKE_CASE_          =  label_idx







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Union[Split, str]   )  ->	List[InputExample]:
        if isinstance(__magic_name__  ,     __magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  mode.value
        SCREAMING_SNAKE_CASE_          =  os.path.join(__magic_name__  ,     F'''{mode}.txt'''   )
        SCREAMING_SNAKE_CASE_          =  1
        SCREAMING_SNAKE_CASE_          =  []
        with open(__magic_name__  ,     encoding="utf-8"   ) as f:
              SCREAMING_SNAKE_CASE_          =  []
              SCREAMING_SNAKE_CASE_          =  []
              for line in f:
                    if line.startswith("-DOCSTART-"   ) or line == "" or line == "\n":
                          if words:
                                examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,     words=__magic_name__  ,     labels=__magic_name__   )   )
                                guid_index += 1
                                SCREAMING_SNAKE_CASE_          =  []
                                SCREAMING_SNAKE_CASE_          =  []
                    else:
                          SCREAMING_SNAKE_CASE_          =  line.split(" "   )
                          words.append(splits[0]   )
                          if len(__magic_name__   ) > 1:
                                labels.append(splits[self.label_idx].replace("\n"  ,     ""   )   )
                          else:
                                # Examples could have no label for mode = "test"
                                labels.append("O"   )
              if words:
                    examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,     words=__magic_name__  ,     labels=__magic_name__   )   )
        return examples







  def    __A     (							self :       Tuple  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       List   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  0
        for line in test_input_reader:
              if line.startswith("-DOCSTART-"   ) or line == "" or line == "\n":
                    writer.write(__magic_name__   )
                    if not preds_list[example_id]:
                          example_id += 1
              elif preds_list[example_id]:
                    SCREAMING_SNAKE_CASE_          =  line.split()[0] + " " + preds_list[example_id].pop(0   ) + "\n"
                    writer.write(__magic_name__   )
              else:
                    logger.warning("Maximum sequence length exceeded: No prediction for '%s'."  ,     line.split()[0]   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       str   )  ->	List[str]:
        if path:
              with open(__magic_name__  ,     "r"   ) as f:
                    SCREAMING_SNAKE_CASE_          =  f.read().splitlines()
              if "O" not in labels:
                    SCREAMING_SNAKE_CASE_          =  ["O"] + labels
              return labels
        else:
              return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       Optional[int]   )  ->	str:
        # in CONLL2003 dataset chunk column is second-to-last
        super().__init__(label_idx=-2   )







  def    __A     (							self :       Any  ,     __magic_name__ :       str   )  ->	List[str]:
        if path:
              with open(__magic_name__  ,     "r"   ) as f:
                    SCREAMING_SNAKE_CASE_          =  f.read().splitlines()
              if "O" not in labels:
                    SCREAMING_SNAKE_CASE_          =  ["O"] + labels
              return labels
        else:
              return [
                  "O",
                  "B-ADVP",
                  "B-INTJ",
                  "B-LST",
                  "B-PRT",
                  "B-NP",
                  "B-SBAR",
                  "B-VP",
                  "B-ADJP",
                  "B-CONJP",
                  "B-PP",
                  "I-ADVP",
                  "I-INTJ",
                  "I-LST",
                  "I-PRT",
                  "I-NP",
                  "I-SBAR",
                  "I-VP",
                  "I-ADJP",
                  "I-CONJP",
                  "I-PP",
              ]



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def    __A     (							self :       str  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Union[Split, str]   )  ->	List[InputExample]:
        if isinstance(__magic_name__  ,     __magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  mode.value
        SCREAMING_SNAKE_CASE_          =  os.path.join(__magic_name__  ,     F'''{mode}.txt'''   )
        SCREAMING_SNAKE_CASE_          =  1
        SCREAMING_SNAKE_CASE_          =  []

        with open(__magic_name__  ,     encoding="utf-8"   ) as f:
              for sentence in parse_incr(__magic_name__   ):
                    SCREAMING_SNAKE_CASE_          =  []
                    SCREAMING_SNAKE_CASE_          =  []
                    for token in sentence:
                          words.append(token["form"]   )
                          labels.append(token["upos"]   )
                    assert len(__magic_name__   ) == len(__magic_name__   )
                    if words:
                          examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,     words=__magic_name__  ,     labels=__magic_name__   )   )
                          guid_index += 1
        return examples







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       List   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  0
        for sentence in parse_incr(__magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  preds_list[example_id]
              SCREAMING_SNAKE_CASE_          =  ""
              for token in sentence:
                    out += F'''{token["form"]} ({token["upos"]}|{s_p.pop(0   )}) '''
              out += "\n"
              writer.write(__magic_name__   )
              example_id += 1







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       str   )  ->	List[str]:
        if path:
              with open(__magic_name__  ,     "r"   ) as f:
                    return f.read().splitlines()
        else:
              return [
                  "ADJ",
                  "ADP",
                  "ADV",
                  "AUX",
                  "CCONJ",
                  "DET",
                  "INTJ",
                  "NOUN",
                  "NUM",
                  "PART",
                  "PRON",
                  "PROPN",
                  "PUNCT",
                  "SCONJ",
                  "SYM",
                  "VERB",
                  "X",
              ]


