






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     SCREAMING_SNAKE_CASE					(			unittest.TestCase       ):





							"""simple docstring"""

							def    __A			(	self: Union[str, Any] )     ->					Union[str, Any]:
												_A										=			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
												_A										=			AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(a_ )
												_A										=			-1

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

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

												self.assertEqual(a_		,  a_ )

							def    __A			(	self: int )     ->					Any:
												_A										=			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
												_A										=			AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(a_ )
												_A										=			-1

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

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

												self.assertEqual(a_		,  a_ )

							def    __A			(	self: List[Any] )     ->					Union[str, Any]:
												_A										=			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
												_A										=			AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(a_ )
												_A										=			-1

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

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

												self.assertEqual(a_		,  a_ )

							def    __A			(	self: int )     ->					Optional[Any]:
												_A										=			AutoTokenizer.from_pretrained('''distilgpt2''' )
												_A										=			AutoModelForCausalLM.from_pretrained('''distilgpt2''' ).to(a_ )
												_A										=			-1

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

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



							def    __A			(	self: Tuple )     ->					Dict:
												_A										=			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
												_A										=			AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(a_ )
												_A										=			-1

												_A										=			ids_tensor((1, 5)		,  vocab_size=model.config.vocab_size ).to(a_ )
												_A										=			TextIteratorStreamer(a_		,  timeout=0.001 )
												_A										=			{'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
												_A										=			Thread(target=model.generate		,  kwargs=a_ )
												thread.start()

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






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import _LazyModule


_a					:			Tuple							=    {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}


if TYPE_CHECKING:
  from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
  import sys

  _a					:			int							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


