
'''simple docstring'''



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

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


UpperCamelCase__      :		Any       							=  logging.getLogger(__name__)

class    _a				(_lowerCamelCase):





							"""simple docstring"""




							def __init__(							self       ,							A__=-1      )    ->			str:
										# in NER datasets, the last column is usually reserved for NER label
										_SCREAMING_SNAKE_CASE      =				label_idx






							def        UpperCamelCase				(							self       ,							A__       ,							A__      )    ->			List[InputExample]:
										if isinstance(A__       ,							A__      ):
													_SCREAMING_SNAKE_CASE      =				mode.value
										_SCREAMING_SNAKE_CASE      =				os.path.join(A__       ,							F"{mode}.txt"      )
										_SCREAMING_SNAKE_CASE      =				1
										_SCREAMING_SNAKE_CASE      =				[]
										with open(A__       ,							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=A__       ,							labels=A__      )      )
																						guid_index += 1
																						_SCREAMING_SNAKE_CASE      =				[]
																						_SCREAMING_SNAKE_CASE      =				[]
																else:
																			_SCREAMING_SNAKE_CASE      =				line.split(""" """      )
																			words.append(splits[0]      )
																			if len(A__      ) > 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=A__       ,							labels=A__      )      )
										return examples






							def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__      )    ->			Optional[Any]:
										_SCREAMING_SNAKE_CASE      =				0
										for line in test_input_reader:
													if line.startswith("""-DOCSTART-"""      ) or line == "" or line == "\n":
																writer.write(A__      )
																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(A__      )
													else:
																logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""       ,							line.split()[0]      )






							def        UpperCamelCase				(							self       ,							A__      )    ->			List[str]:
										if path:
													with open(A__       ,							"""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    _a				(_lowerCamelCase):





							"""simple docstring"""




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






							def        UpperCamelCase				(							self       ,							A__      )    ->			List[str]:
										if path:
													with open(A__       ,							"""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    _a				(_lowerCamelCase):





							"""simple docstring"""




							def        UpperCamelCase				(							self       ,							A__       ,							A__      )    ->			List[InputExample]:
										if isinstance(A__       ,							A__      ):
													_SCREAMING_SNAKE_CASE      =				mode.value
										_SCREAMING_SNAKE_CASE      =				os.path.join(A__       ,							F"{mode}.txt"      )
										_SCREAMING_SNAKE_CASE      =				1
										_SCREAMING_SNAKE_CASE      =				[]

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






							def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__      )    ->			Dict:
										_SCREAMING_SNAKE_CASE      =				0
										for sentence in parse_incr(A__      ):
													_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(A__      )
													example_id += 1






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








'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
     import jax.numpy as jnp

if is_tf_available():
     import tensorflow as tf

if is_torch_available():
     import torch

class    _a				(unittest.TestCase):





       """simple docstring"""




       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				{
              """task_specific_params""": {
                  """summarization""": {"""length_penalty""": 1.0, """max_length""": 1_28, """min_length""": 12, """num_beams""": 4},
                  """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 1_42, """min_length""": 56, """num_beams""": 4},
                  """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
              }
          }
          _SCREAMING_SNAKE_CASE      =				{
              """task_specific_params.summarization.length_penalty""": 1.0,
              """task_specific_params.summarization.max_length""": 1_28,
              """task_specific_params.summarization.min_length""": 12,
              """task_specific_params.summarization.num_beams""": 4,
              """task_specific_params.summarization_cnn.length_penalty""": 2.0,
              """task_specific_params.summarization_cnn.max_length""": 1_42,
              """task_specific_params.summarization_cnn.min_length""": 56,
              """task_specific_params.summarization_cnn.num_beams""": 4,
              """task_specific_params.summarization_xsum.length_penalty""": 1.0,
              """task_specific_params.summarization_xsum.max_length""": 62,
              """task_specific_params.summarization_xsum.min_length""": 11,
              """task_specific_params.summarization_xsum.num_beams""": 6,
          }

          self.assertEqual(flatten_dict(A__      )       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          self.assertTrue(np.allclose(transpose(A__      )       ,							x.transpose()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          self.assertTrue(np.allclose(transpose(A__       ,							axes=(1, 2, 0)      )       ,							x.transpose((1, 2, 0)      )      )      )






       @require_torch
       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(transpose(A__      )       ,							transpose(A__      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(transpose(A__       ,							axes=(1, 2, 0)      )       ,							transpose(A__       ,							axes=(1, 2, 0)      ).numpy()      )      )






       @require_tf
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(transpose(A__      )       ,							transpose(A__      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(transpose(A__       ,							axes=(1, 2, 0)      )       ,							transpose(A__       ,							axes=(1, 2, 0)      ).numpy()      )      )






       @require_flax
       def        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(transpose(A__      )       ,							np.asarray(transpose(A__      )      )      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(transpose(A__       ,							axes=(1, 2, 0)      )       ,							np.asarray(transpose(A__       ,							axes=(1, 2, 0)      )      )      )      )






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          self.assertTrue(np.allclose(reshape(A__       ,							(4, 3)      )       ,							np.reshape(A__       ,							(4, 3)      )      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          self.assertTrue(np.allclose(reshape(A__       ,							(12, 5)      )       ,							np.reshape(A__       ,							(12, 5)      )      )      )






       @require_torch
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(4, 3)      )       ,							reshape(A__       ,							(4, 3)      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(12, 5)      )       ,							reshape(A__       ,							(12, 5)      ).numpy()      )      )






       @require_tf
       def        UpperCamelCase				(							self      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(4, 3)      )       ,							reshape(A__       ,							(4, 3)      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(12, 5)      )       ,							reshape(A__       ,							(12, 5)      ).numpy()      )      )






       @require_flax
       def        UpperCamelCase				(							self      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(4, 3)      )       ,							np.asarray(reshape(A__       ,							(4, 3)      )      )      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(12, 5)      )       ,							np.asarray(reshape(A__       ,							(12, 5)      )      )      )      )






       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							3       ,							4      )
          self.assertTrue(np.allclose(squeeze(A__      )       ,							np.squeeze(A__      )      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							4       ,							1       ,							5      )
          self.assertTrue(np.allclose(squeeze(A__       ,							axis=2      )       ,							np.squeeze(A__       ,							axis=2      )      )      )






       @require_torch
       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(squeeze(A__      )       ,							squeeze(A__      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							4       ,							1       ,							5      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(squeeze(A__       ,							axis=2      )       ,							squeeze(A__       ,							axis=2      ).numpy()      )      )






       @require_tf
       def        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(squeeze(A__      )       ,							squeeze(A__      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							4       ,							1       ,							5      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(squeeze(A__       ,							axis=2      )       ,							squeeze(A__       ,							axis=2      ).numpy()      )      )






       @require_flax
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(squeeze(A__      )       ,							np.asarray(squeeze(A__      )      )      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							4       ,							1       ,							5      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(squeeze(A__       ,							axis=2      )       ,							np.asarray(squeeze(A__       ,							axis=2      )      )      )      )






       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          self.assertTrue(np.allclose(expand_dims(A__       ,							axis=1      )       ,							np.expand_dims(A__       ,							axis=1      )      )      )






       @require_torch
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(expand_dims(A__       ,							axis=1      )       ,							expand_dims(A__       ,							axis=1      ).numpy()      )      )






       @require_tf
       def        UpperCamelCase				(							self      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(expand_dims(A__       ,							axis=1      )       ,							expand_dims(A__       ,							axis=1      ).numpy()      )      )






       @require_flax
       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(expand_dims(A__       ,							axis=1      )       ,							np.asarray(expand_dims(A__       ,							axis=1      )      )      )      )






