

"""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        _lowercase					(     unittest.TestCase     ):







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
      __magic_name__     =      AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       ).to(UpperCamelCase_       )
      __magic_name__     =      -1

      __magic_name__     =      ids_tensor((1, 5) ,						vocab_size=model.config.vocab_size       ).to(UpperCamelCase_       )
      __magic_name__     =      model.generate(UpperCamelCase_ ,						max_new_tokens=10 ,						do_sample=UpperCamelCase_       )
      __magic_name__     =      tokenizer.decode(greedy_ids[0]       )

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

      self.assertEqual(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
      __magic_name__     =      AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       ).to(UpperCamelCase_       )
      __magic_name__     =      -1

      __magic_name__     =      ids_tensor((1, 5) ,						vocab_size=model.config.vocab_size       ).to(UpperCamelCase_       )
      __magic_name__     =      model.generate(UpperCamelCase_ ,						max_new_tokens=10 ,						do_sample=UpperCamelCase_       )
      __magic_name__     =      tokenizer.decode(greedy_ids[0]       )

      __magic_name__     =      TextIteratorStreamer(UpperCamelCase_       )
      __magic_name__     =      {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
      __magic_name__     =      Thread(target=model.generate ,						kwargs=UpperCamelCase_       )
      thread.start()
      __magic_name__     =      """"""
      for new_text in streamer:
       streamer_text += new_text

      self.assertEqual(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
      __magic_name__     =      AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       ).to(UpperCamelCase_       )
      __magic_name__     =      -1

      __magic_name__     =      ids_tensor((1, 5) ,						vocab_size=model.config.vocab_size       ).to(UpperCamelCase_       )
      __magic_name__     =      model.generate(UpperCamelCase_ ,						max_new_tokens=10 ,						do_sample=UpperCamelCase_       )
      __magic_name__     =      greedy_ids[:, input_ids.shape[1] :]
      __magic_name__     =      tokenizer.decode(new_greedy_ids[0]       )

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

      self.assertEqual(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      AutoTokenizer.from_pretrained('''distilgpt2'''       )
      __magic_name__     =      AutoModelForCausalLM.from_pretrained('''distilgpt2'''       ).to(UpperCamelCase_       )
      __magic_name__     =      -1

      __magic_name__     =      torch.ones((1, 5) ,						device=UpperCamelCase_       ).long() * model.config.bos_token_id
      with CaptureStdout() as cs:
       __magic_name__     =      TextStreamer(UpperCamelCase_ ,						skip_special_tokens=UpperCamelCase_       )
       model.generate(UpperCamelCase_ ,						max_new_tokens=1 ,						do_sample=UpperCamelCase_ ,						streamer=UpperCamelCase_       )

      # 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
      __magic_name__     =      cs.out[:-1]  # Remove the final "\n"
      __magic_name__     =      tokenizer(UpperCamelCase_ ,						return_tensors='''pt'''       )
      self.assertEqual(streamer_text_tokenized.input_ids.shape ,						(1, 1)       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
      __magic_name__     =      AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       ).to(UpperCamelCase_       )
      __magic_name__     =      -1

      __magic_name__     =      ids_tensor((1, 5) ,						vocab_size=model.config.vocab_size       ).to(UpperCamelCase_       )
      __magic_name__     =      TextIteratorStreamer(UpperCamelCase_ ,						timeout=0.0_0_1       )
      __magic_name__     =      {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
      __magic_name__     =      Thread(target=model.generate ,						kwargs=UpperCamelCase_       )
      thread.start()

      # The streamer will timeout after 0.001 seconds, so an exception will be raised
      with self.assertRaises(UpperCamelCase_       ):
       __magic_name__     =      """"""
       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


__lowerCamelCase										=		{
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCamelCase										=		[
     "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
     "M2M100ForConditionalGeneration",
     "M2M100Model",
     "M2M100PreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
 from .tokenization_mam_aaa import MaMaaaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mam_aaa import (
      M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
      MaMaaaForConditionalGeneration,
      MaMaaaModel,
      MaMaaaPreTrainedModel,
  )


else:
 import sys

 __lowerCamelCase										=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

