





'''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 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				__lowercase										=							AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
				__lowercase										=							AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
				__lowercase										=							-1

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

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

				self.assertEqual(_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				__lowercase										=							AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
				__lowercase										=							AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
				__lowercase										=							-1

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

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

				self.assertEqual(_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
				__lowercase										=							AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
				__lowercase										=							-1

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

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

				self.assertEqual(_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							AutoTokenizer.from_pretrained('''distilgpt2''' )
				__lowercase										=							AutoModelForCausalLM.from_pretrained('''distilgpt2''' ).to(_lowerCamelCase )
				__lowercase										=							-1

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

				# 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(_lowerCamelCase     ,return_tensors='''pt''' )
				self.assertEqual(streamer_text_tokenized.input_ids.shape     ,(1, 1) )


	def 						_UpperCAmelCase     (self )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
				__lowercase										=							AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
				__lowercase										=							-1

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

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












'''simple docstring'''



import warnings

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


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							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 )





