



"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union

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


_lowerCAmelCase    :List[Any]     =					logging.getLogger(__name__)
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



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





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					List[InputExample]:
         if isinstance(A       ,	A		):
             _UpperCAmelCase							:  Union[str, Any]											=    mode.value
         _UpperCAmelCase							:  Any											=    os.path.join(A       ,	f'{mode}.txt'		)
         _UpperCAmelCase							:  Optional[int]											=    1
         _UpperCAmelCase							:  List[str]											=    []
         with open(A       ,	encoding='''utf-8'''		) as f:
             _UpperCAmelCase							:  Dict											=    []
             _UpperCAmelCase							:  Optional[Any]											=    []
             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
                         _UpperCAmelCase							:  List[Any]											=    []
                         _UpperCAmelCase							:  Tuple											=    []
                 else:
                     _UpperCAmelCase							:  Any											=    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  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A		)							->					Optional[int]:
         _UpperCAmelCase							:  Dict											=    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]:
                 _UpperCAmelCase							:  Optional[Any]											=    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  __lowerCAmelCase	(					self       ,	A		)							->					List[str]:
         if path:
             with open(A       ,	'''r'''		) as f:
                 _UpperCAmelCase							:  Optional[int]											=    f.read().splitlines()
             if "O" not in labels:
                 _UpperCAmelCase							:  List[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							( a      ):





     '''simple docstring'''



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





     def  __lowerCAmelCase	(					self       ,	A		)							->					List[str]:
         if path:
             with open(A       ,	'''r'''		) as f:
                 _UpperCAmelCase							:  Union[str, Any]											=    f.read().splitlines()
             if "O" not in labels:
                 _UpperCAmelCase							:  Optional[int]											=    ['''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							( a      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					List[InputExample]:
         if isinstance(A       ,	A		):
             _UpperCAmelCase							:  Dict											=    mode.value
         _UpperCAmelCase							:  Optional[int]											=    os.path.join(A       ,	f'{mode}.txt'		)
         _UpperCAmelCase							:  Optional[Any]											=    1
         _UpperCAmelCase							:  Dict											=    []

         with open(A       ,	encoding='''utf-8'''		) as f:
             for sentence in parse_incr(A		):
                 _UpperCAmelCase							:  int											=    []
                 _UpperCAmelCase							:  Tuple											=    []
                 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  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A		)							->					List[Any]:
         _UpperCAmelCase							:  Optional[int]											=    0
         for sentence in parse_incr(A		):
             _UpperCAmelCase							:  Dict											=    preds_list[example_id]
             _UpperCAmelCase							:  Dict											=    ''''''
             for token in sentence:
                 out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0		)}) '
             out += "\n"
             writer.write(A		)
             example_id += 1





     def  __lowerCAmelCase	(					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
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers import AutoModelForCausalLM
@require_torch
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Dict											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[str]											=    -1

         _UpperCAmelCase							:  List[str]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[str]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  List[Any]											=    tokenizer.decode(greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  str											=    TextStreamer(A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  List[str]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  List[str]											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  List[Any]											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    -1

         _UpperCAmelCase							:  Union[str, Any]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  str											=    tokenizer.decode(greedy_ids[0]		)

         _UpperCAmelCase							:  Union[str, Any]											=    TextIteratorStreamer(A		)
         _UpperCAmelCase							:  Any											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Any											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()
         _UpperCAmelCase							:  Any											=    ''''''
         for new_text in streamer:
             streamer_text += new_text

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  str											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Any											=    -1

         _UpperCAmelCase							:  Dict											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  Dict											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  Dict											=    greedy_ids[:, input_ids.shape[1] :]
         _UpperCAmelCase							:  List[str]											=    tokenizer.decode(new_greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Any											=    TextStreamer(A       ,	skip_prompt=A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  Union[str, Any]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
         # with actual models -- the dummy models' tokenizers are not aligned with their models, and
         # `skip_special_tokens=True` has no effect on them
         _UpperCAmelCase							:  int											=    AutoTokenizer.from_pretrained('''distilgpt2'''		)
         _UpperCAmelCase							:  Union[str, Any]											=    AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(A		)
         _UpperCAmelCase							:  Tuple											=    -1

         _UpperCAmelCase							:  int											=    torch.ones((1, 5)       ,	device=A		).long() * model.config.bos_token_id
         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Optional[Any]											=    TextStreamer(A       ,	skip_special_tokens=A		)
             model.generate(A       ,	max_new_tokens=1       ,	do_sample=A       ,	streamer=A		)

         # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
         # re-tokenized, must only contain one token
         _UpperCAmelCase							:  Tuple											=    cs.out[:-1]  # Remove the final "\n"
         _UpperCAmelCase							:  int											=    tokenizer(A       ,	return_tensors='''pt'''		)
         self.assertEqual(streamer_text_tokenized.input_ids.shape       ,	(1, 1)		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Any											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Dict											=    -1

         _UpperCAmelCase							:  str											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    TextIteratorStreamer(A       ,	timeout=0.001		)
         _UpperCAmelCase							:  Union[str, Any]											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Optional[Any]											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()

         # The streamer will timeout after 0.001 seconds, so an exception will be raised
         with self.assertRaises(A		):
             _UpperCAmelCase							:  Optional[Any]											=    ''''''
             for new_text in streamer:
                 streamer_text += new_text


