






"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_snake_case          =   logging.get_logger(__name__)

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  if isinstance(UpperCamelCase__     ,   np.ndarray				):
    return list(tensor.shape				)

  _a  :       int             =	tf.shape(UpperCamelCase__				)

  if tensor.shape == tf.TensorShape(UpperCamelCase__				):
    return dynamic

  _a  :       int             =	tensor.shape.as_list()

  return [dynamic[i] if s is None else s for i, s in enumerate(UpperCamelCase__				)]

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__ = None     ,   UpperCamelCase__ = None				):



  '''simple docstring'''
  return tf.nn.softmax(logits=logits + 1e-9     ,   axis=UpperCamelCase__     ,   name=UpperCamelCase__				)

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=1e-5     ,   UpperCamelCase__=-1				):



  '''simple docstring'''
  # This is a very simplified functional layernorm, designed to duplicate
  # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
  # models in Transformers.

  if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCamelCase__     ,   UpperCamelCase__				):
    raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis."""				)

  # Get mean and variance on the axis to be normalized
  _a      ,  _a  :       Tuple             =	tf.nn.moments(UpperCamelCase__     ,   axes=[axis]     ,   keepdims=UpperCamelCase__				)

  if axis != -1:
    # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
    # on every dimension except axis
    _a  :       Optional[Any]             =	[1] * inputs.shape.rank
    _a  :       Union[str, Any]             =	shape_list(UpperCamelCase__				)[axis]
    _a  :       List[Any]             =	tf.reshape(UpperCamelCase__     ,   UpperCamelCase__				)
    _a  :       Tuple             =	tf.reshape(UpperCamelCase__     ,   UpperCamelCase__				)

  # Compute layer normalization using the batch_normalization
  # function.
  _a  :       int             =	tf.nn.batch_normalization(
      UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   offset=UpperCamelCase__     ,   scale=UpperCamelCase__     ,   variance_epsilon=UpperCamelCase__     ,   )
  return outputs

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__=0     ,   UpperCamelCase__=-1				):



  '''simple docstring'''
  # Replicates the behavior of torch.flatten in TF

  # If end_dim or start_dim is negative, count them from the end
  if end_dim < 0:
    end_dim += input.shape.rank
  if start_dim < 0:
    start_dim += input.shape.rank

  if start_dim == end_dim:
    return input

  _a  :       List[str]             =	tf.shape(UpperCamelCase__				)
  _a  :       Tuple             =	tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]				)
  _a  :       str             =	tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]     ,   axis=0				)
  return tf.reshape(UpperCamelCase__     ,   UpperCamelCase__				)

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  if not isinstance(UpperCamelCase__     ,   tf.Tensor				):
    _a  :       int             =	tf.convert_to_tensor(UpperCamelCase__				)  # Catches stray NumPy inputs
  if encoder_attention_mask.shape.rank == 3:
    _a  :       Optional[Any]             =	encoder_attention_mask[:, None, :, :]
  if encoder_attention_mask.shape.rank == 2:
    _a  :       Dict             =	encoder_attention_mask[:, None, None, :]
  # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
  # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
  # /transformer/transformer_layers.py#L270
  # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
  # encoder_extended_attention_mask.transpose(-1, -2))
  _a  :       List[str]             =	(
      tf.cast(1     ,   encoder_attention_mask.dtype				) - encoder_extended_attention_mask
  ) * encoder_extended_attention_mask.dtype.min

  return encoder_extended_attention_mask

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__ = "input_ids"				):



  '''simple docstring'''
  tf.debugging.assert_less(
      UpperCamelCase__     ,   tf.cast(UpperCamelCase__     ,   dtype=tensor.dtype				)     ,   message=(
          F"""The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCamelCase__				)}) must be smaller than the embedding """
          F"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
      )     ,   )

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Optional[Any]             =	6_4_5_1_2
  # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
  # because in that case even chunking the array would not make the saving
  # possible.
  _a  :       Any             =	[x for x in data if len(UpperCamelCase__				) > HDF5_OBJECT_HEADER_LIMIT]

  # Expecting this to never be true.
  if bad_attributes:
    raise RuntimeError(
        """The following attributes cannot be saved to HDF5 file because """
        F"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
        F"""bytes: {bad_attributes}"""				)

  _a  :       str             =	np.asarray(UpperCamelCase__				)

  _a  :       List[Any]             =	1
  _a  :       Tuple             =	np.array_split(UpperCamelCase__     ,   UpperCamelCase__				)

  # This will never loop forever thanks to the test above.
  while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data				):
    num_chunks += 1
    _a  :       str             =	np.array_split(UpperCamelCase__     ,   UpperCamelCase__				)

  if num_chunks > 1:
    for chunk_id, chunk_data in enumerate(UpperCamelCase__				):
      _a  :       Tuple             =	chunk_data
  else:
    _a  :       str             =	data

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  if name in group.attrs:
    _a  :       Dict             =	[n.decode("""utf8"""				) if hasattr(UpperCamelCase__     ,   """decode"""				) else n for n in group.attrs[name]]
  else:
    _a  :       Optional[int]             =	[]
    _a  :       int             =	0
    while "%s%d" % (name, chunk_id) in group.attrs:
      data.extend(
          [n.decode("""utf8"""				) if hasattr(UpperCamelCase__     ,   """decode"""				) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]]				)
      chunk_id += 1
  return data



def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''

  def _expand_single_ad_tensor(UpperCamelCase__				):
    if isinstance(UpperCamelCase__     ,   tf.Tensor				) and t.shape.rank == 1:
      return tf.expand_dims(UpperCamelCase__     ,   axis=-1				)
    return t

  return tf.nest.map_structure(_expand_single_ad_tensor     ,   UpperCamelCase__				)









"""simple docstring"""




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

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


_snake_case          =   logging.getLogger(__name__)






class        UpperCamelCase			(	snake_case_      ):


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


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Union[Split, str]							)					->       List[InputExample]:
   if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
     _a  :       Any             =	mode.value
   _a  :       Optional[int]             =	os.path.join(UpperCAmelCase__							,						f"""{mode}.txt"""							)
   _a  :       int             =	1
   _a  :       int             =	[]
   with open(UpperCAmelCase__							,						encoding="""utf-8"""							) as f:
     _a  :       str             =	[]
     _a  :       str             =	[]
     for line in f:
       if line.startswith("""-DOCSTART-"""							) or line == "" or line == "\n":
         if words:
           examples.append(InputExample(guid=f"""{mode}-{guid_index}"""							,						words=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
           guid_index += 1
           _a  :       List[str]             =	[]
           _a  :       str             =	[]
       else:
         _a  :       List[Any]             =	line.split(""" """							)
         words.append(splits[0]							)
         if len(UpperCAmelCase__							) > 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=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
   return examples


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







 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       _a  :       List[Any]             =	f.read().splitlines()
     if "O" not in labels:
       _a  :       Union[str, Any]             =	["""O"""] + labels
     return labels
   else:
     return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class        UpperCamelCase			(	snake_case_      ):


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







 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       _a  :       Optional[int]             =	f.read().splitlines()
     if "O" not in labels:
       _a  :       Optional[Any]             =	["""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        UpperCamelCase			(	snake_case_      ):


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						List[Any]							,						UpperCAmelCase__		:						Union[Split, str]							)					->       List[InputExample]:
   if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
     _a  :       List[Any]             =	mode.value
   _a  :       Union[str, Any]             =	os.path.join(UpperCAmelCase__							,						f"""{mode}.txt"""							)
   _a  :       List[str]             =	1
   _a  :       Optional[Any]             =	[]

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


 def _lowercase	(     self		:						Tuple							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						List							)					->       Dict:
   _a  :       Optional[Any]             =	0
   for sentence in parse_incr(UpperCAmelCase__							):
     _a  :       List[str]             =	preds_list[example_id]
     _a  :       str             =	""""""
     for token in sentence:
       out += f"""{token['form']} ({token['upos']}|{s_p.pop(0							)}) """
     out += "\n"
     writer.write(UpperCAmelCase__							)
     example_id += 1







 def _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""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",
     ]

