

'''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 ):




  """simple docstring"""




  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''     )
   __lowercase				    =					AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''     ).to(lowercase__     )
   __lowercase				    =					-1

   __lowercase				    =					ids_tensor((1, 5)   ,vocab_size=model.config.vocab_size     ).to(lowercase__     )
   __lowercase				    =					model.generate(lowercase__   ,max_new_tokens=1_0   ,do_sample=lowercase__     )
   __lowercase				    =					tokenizer.decode(greedy_ids[0]     )

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

   self.assertEqual(lowercase__   ,lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   __lowercase				    =					AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''     )
   __lowercase				    =					AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''     ).to(lowercase__     )
   __lowercase				    =					-1

   __lowercase				    =					ids_tensor((1, 5)   ,vocab_size=model.config.vocab_size     ).to(lowercase__     )
   __lowercase				    =					model.generate(lowercase__   ,max_new_tokens=1_0   ,do_sample=lowercase__     )
   __lowercase				    =					tokenizer.decode(greedy_ids[0]     )

   __lowercase				    =					TextIteratorStreamer(lowercase__     )
   __lowercase				    =					{'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
   __lowercase				    =					Thread(target=model.generate   ,kwargs=lowercase__     )
   thread.start()
   __lowercase				    =					''''''
   for new_text in streamer:
    streamer_text += new_text

   self.assertEqual(lowercase__   ,lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase				    =					AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''     )
   __lowercase				    =					AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''     ).to(lowercase__     )
   __lowercase				    =					-1

   __lowercase				    =					ids_tensor((1, 5)   ,vocab_size=model.config.vocab_size     ).to(lowercase__     )
   __lowercase				    =					model.generate(lowercase__   ,max_new_tokens=1_0   ,do_sample=lowercase__     )
   __lowercase				    =					greedy_ids[:, input_ids.shape[1] :]
   __lowercase				    =					tokenizer.decode(new_greedy_ids[0]     )

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

   self.assertEqual(lowercase__   ,lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   # 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
   __lowercase				    =					AutoTokenizer.from_pretrained('''distilgpt2'''     )
   __lowercase				    =					AutoModelForCausalLM.from_pretrained('''distilgpt2'''     ).to(lowercase__     )
   __lowercase				    =					-1

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

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






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   __lowercase				    =					AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''     )
   __lowercase				    =					AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''     ).to(lowercase__     )
   __lowercase				    =					-1

   __lowercase				    =					ids_tensor((1, 5)   ,vocab_size=model.config.vocab_size     ).to(lowercase__     )
   __lowercase				    =					TextIteratorStreamer(lowercase__   ,timeout=0.0_0_1     )
   __lowercase				    =					{'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
   __lowercase				    =					Thread(target=model.generate   ,kwargs=lowercase__     )
   thread.start()

   # The streamer will timeout after 0.001 seconds, so an exception will be raised
   with self.assertRaises(lowercase__     ):
    __lowercase				    =					''''''
    for new_text in streamer:
     streamer_text += new_text






import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a__   :					Optional[Any] 						=      logging.get_logger(__name__)



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   def __init__(    self	:      Optional[Any]      ,					*lowerCAmelCase	:      Optional[Any]      ,					**lowerCAmelCase	:      List[Any])						->					None:




         """simple docstring"""

         warnings.warn(
             'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use GLPNImageProcessor instead.'      ,					lowerCAmelCase      ,					)
         super().__init__(*lowerCAmelCase      ,					**lowerCAmelCase)
