"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_     =	{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CanineForMultipleChoice''',
			    '''CanineForQuestionAnswering''',
			    '''CanineForSequenceClassification''',
			    '''CanineForTokenClassification''',
			    '''CanineLayer''',
			    '''CanineModel''',
			    '''CaninePreTrainedModel''',
			    '''load_tf_weights_in_canine''',
			]


if TYPE_CHECKING:
			from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
			from .tokenization_canine import CanineTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_canine import (
						    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CanineForMultipleChoice,
						    CanineForQuestionAnswering,
						    CanineForSequenceClassification,
						    CanineForTokenClassification,
						    CanineLayer,
						    CanineModel,
						    CaninePreTrainedModel,
						    load_tf_weights_in_canine,
						)


else:
			import sys

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



"""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  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""")
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    -1

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    tokenizer.decode(greedy_ids[0])

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

											self.assertEqual(_A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""")
											_SCREAMING_SNAKE_CASE		:						Any			  =    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    -1

											_SCREAMING_SNAKE_CASE		:						str			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    tokenizer.decode(greedy_ids[0])

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    TextIteratorStreamer(_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    {"""input_ids""": input_ids, """max_new_tokens""": 1_0, """do_sample""": False, """streamer""": streamer}
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    Thread(target=model.generate      ,    kwargs=_A)
											thread.start()
											_SCREAMING_SNAKE_CASE		:						Any			  =    """"""
											for new_text in streamer:
																		streamer_text += new_text

											self.assertEqual(_A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""")
											_SCREAMING_SNAKE_CASE		:						Dict			  =    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    -1

											_SCREAMING_SNAKE_CASE		:						Any			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    greedy_ids[:, input_ids.shape[1] :]
											_SCREAMING_SNAKE_CASE		:						Dict			  =    tokenizer.decode(new_greedy_ids[0])

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

											self.assertEqual(_A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    AutoTokenizer.from_pretrained("""distilgpt2""")
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoModelForCausalLM.from_pretrained("""distilgpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    -1

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.ones((1, 5)      ,    device=_A).long() * model.config.bos_token_id
											with CaptureStdout() as cs:
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    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
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    cs.out[:-1]  # Remove the final "\n"
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    tokenizer(_A      ,    return_tensors="""pt""")
											self.assertEqual(streamer_text_tokenized.input_ids.shape      ,    (1, 1))





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""")
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    -1

											_SCREAMING_SNAKE_CASE		:						Dict			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    TextIteratorStreamer(_A      ,    timeout=0.001)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    {"""input_ids""": input_ids, """max_new_tokens""": 1_0, """do_sample""": False, """streamer""": streamer}
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    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):
																		_SCREAMING_SNAKE_CASE		:						str			  =    """"""
																		for new_text in streamer:
																									streamer_text += new_text


