

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 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    )
      snake_case							:		List[str]        =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[str]        =		-1

      snake_case							:		Union[str, Any]        =		ids_tensor((1, 5)   ,vocab_size=model.config.vocab_size    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		model.generate(SCREAMING_SNAKE_CASE_   ,max_new_tokens=10   ,do_sample=SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		tokenizer.decode(greedy_ids[0]    )

      with CaptureStdout() as cs:
          snake_case							:		Any        =		TextStreamer(SCREAMING_SNAKE_CASE_    )
          model.generate(SCREAMING_SNAKE_CASE_   ,max_new_tokens=10   ,do_sample=SCREAMING_SNAKE_CASE_   ,streamer=SCREAMING_SNAKE_CASE_    )
      # The greedy text should be printed to stdout, except for the final "\n" in the streamer
      snake_case							:		Any        =		cs.out[:-1]

      self.assertEqual(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Union[str, Any]        =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    )
      snake_case							:		Dict        =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		-1

      snake_case							:		Optional[int]        =		ids_tensor((1, 5)   ,vocab_size=model.config.vocab_size    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		model.generate(SCREAMING_SNAKE_CASE_   ,max_new_tokens=10   ,do_sample=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Any        =		tokenizer.decode(greedy_ids[0]    )

      snake_case							:		List[str]        =		TextIteratorStreamer(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		{"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
      snake_case							:		List[Any]        =		Thread(target=model.generate   ,kwargs=SCREAMING_SNAKE_CASE_    )
      thread.start()
      snake_case							:		Tuple        =		""""""
      for new_text in streamer:
          streamer_text += new_text

      self.assertEqual(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    )
      snake_case							:		Optional[int]        =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Any        =		-1

      snake_case							:		Dict        =		ids_tensor((1, 5)   ,vocab_size=model.config.vocab_size    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		model.generate(SCREAMING_SNAKE_CASE_   ,max_new_tokens=10   ,do_sample=SCREAMING_SNAKE_CASE_    )
      snake_case							:		str        =		greedy_ids[:, input_ids.shape[1] :]
      snake_case							:		Dict        =		tokenizer.decode(new_greedy_ids[0]    )

      with CaptureStdout() as cs:
          snake_case							:		str        =		TextStreamer(SCREAMING_SNAKE_CASE_   ,skip_prompt=SCREAMING_SNAKE_CASE_    )
          model.generate(SCREAMING_SNAKE_CASE_   ,max_new_tokens=10   ,do_sample=SCREAMING_SNAKE_CASE_   ,streamer=SCREAMING_SNAKE_CASE_    )
      # The greedy text should be printed to stdout, except for the final "\n" in the streamer
      snake_case							:		str        =		cs.out[:-1]

      self.assertEqual(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      # 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
      snake_case							:		Dict        =		AutoTokenizer.from_pretrained("""distilgpt2"""    )
      snake_case							:		Tuple        =		AutoModelForCausalLM.from_pretrained("""distilgpt2"""    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		-1

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

      # 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
      snake_case							:		Dict        =		cs.out[:-1]  # Remove the final "\n"
      snake_case							:		Tuple        =		tokenizer(SCREAMING_SNAKE_CASE_   ,return_tensors="""pt"""    )
      self.assertEqual(streamer_text_tokenized.input_ids.shape   ,(1, 1)    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    )
      snake_case							:		Optional[int]        =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Tuple        =		-1

      snake_case							:		int        =		ids_tensor((1, 5)   ,vocab_size=model.config.vocab_size    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[str]        =		TextIteratorStreamer(SCREAMING_SNAKE_CASE_   ,timeout=0.0_01    )
      snake_case							:		Optional[int]        =		{"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
      snake_case							:		List[str]        =		Thread(target=model.generate   ,kwargs=SCREAMING_SNAKE_CASE_    )
      thread.start()

      # The streamer will timeout after 0.001 seconds, so an exception will be raised
      with self.assertRaises(SCREAMING_SNAKE_CASE_    ):
          snake_case							:		Tuple        =		""""""
          for new_text in streamer:
              streamer_text += new_text







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A   :    str											= {
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  pass

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A   :    Optional[int]											= [
      "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoCBertForCausalLM",
      "RoCBertForMaskedLM",
      "RoCBertForMultipleChoice",
      "RoCBertForPreTraining",
      "RoCBertForQuestionAnswering",
      "RoCBertForSequenceClassification",
      "RoCBertForTokenClassification",
      "RoCBertLayer",
      "RoCBertModel",
      "RoCBertPreTrainedModel",
      "load_tf_weights_in_roc_bert",
  ]

if TYPE_CHECKING:
  from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
  from .tokenization_roc_bert import RoCBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    raise OptionalDependencyNotAvailable()

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roc_bert import (
        ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoCBertForCausalLM,
        RoCBertForMaskedLM,
        RoCBertForMultipleChoice,
        RoCBertForPreTraining,
        RoCBertForQuestionAnswering,
        RoCBertForSequenceClassification,
        RoCBertForTokenClassification,
        RoCBertLayer,
        RoCBertModel,
        RoCBertPreTrainedModel,
        load_tf_weights_in_roc_bert,
    )


else:
  import sys

  __A   :    Optional[int]											= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



