


"""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     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


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

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

          with CaptureStdout() as cs:
               _a    	=				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
          _a    	=				cs.out[:-1]

          self.assertEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							)
          _a    	=				AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							).to(__UpperCAmelCase							)
          _a    	=				-1

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

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

          self.assertEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							)
          _a    	=				AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							).to(__UpperCAmelCase							)
          _a    	=				-1

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

          with CaptureStdout() as cs:
               _a    	=				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
          _a    	=				cs.out[:-1]

          self.assertEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							str:
          # 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
          _a    	=				AutoTokenizer.from_pretrained('''distilgpt2'''							)
          _a    	=				AutoModelForCausalLM.from_pretrained('''distilgpt2'''							).to(__UpperCAmelCase							)
          _a    	=				-1

          _a    	=				torch.ones((1, 5)    ,				device=__UpperCAmelCase							).long() * model.config.bos_token_id
          with CaptureStdout() as cs:
               _a    	=				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
          _a    	=				cs.out[:-1]  # Remove the final "\n"
          _a    	=				tokenizer(__UpperCAmelCase    ,				return_tensors='''pt'''							)
          self.assertEqual(streamer_text_tokenized.input_ids.shape    ,				(1, 1)							)





     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							)
          _a    	=				AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							).to(__UpperCAmelCase							)
          _a    	=				-1

          _a    	=				ids_tensor((1, 5)    ,				vocab_size=model.config.vocab_size							).to(__UpperCAmelCase							)
          _a    	=				TextIteratorStreamer(__UpperCAmelCase    ,				timeout=0.001							)
          _a    	=				{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
          _a    	=				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							):
               _a    	=				""
               for new_text in streamer:
                    streamer_text += new_text



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case		=       {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ViTMAEForPreTraining''',
       '''ViTMAELayer''',
       '''ViTMAEModel''',
       '''ViTMAEPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''TFViTMAEForPreTraining''',
       '''TFViTMAEModel''',
       '''TFViTMAEPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_mae import (
          VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMAEForPreTraining,
          ViTMAELayer,
          ViTMAEModel,
          ViTMAEPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
   import sys

   __snake_case		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)