

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			):







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''      )
										lowercase   :       Optional[Any]								=AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''      ).to(A_      )
										lowercase   :       Union[str, Any]								=-1

										lowercase   :       Any								=ids_tensor((1, 5)		,				vocab_size=model.config.vocab_size      ).to(A_      )
										lowercase   :       Dict								=model.generate(A_		,				max_new_tokens=10		,				do_sample=A_      )
										lowercase   :       List[Any]								=tokenizer.decode(greedy_ids[0]      )

										with CaptureStdout() as cs:
																lowercase   :       List[str]								=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
										lowercase   :       Any								=cs.out[:-1]

										self.assertEqual(A_		,				A_      )







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''      )
										lowercase   :       int								=AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''      ).to(A_      )
										lowercase   :       Optional[int]								=-1

										lowercase   :       int								=ids_tensor((1, 5)		,				vocab_size=model.config.vocab_size      ).to(A_      )
										lowercase   :       Optional[int]								=model.generate(A_		,				max_new_tokens=10		,				do_sample=A_      )
										lowercase   :       str								=tokenizer.decode(greedy_ids[0]      )

										lowercase   :       int								=TextIteratorStreamer(A_      )
										lowercase   :       str								={"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
										lowercase   :       List[Any]								=Thread(target=model.generate		,				kwargs=A_      )
										thread.start()
										lowercase   :       int								=""
										for new_text in streamer:
																streamer_text += new_text

										self.assertEqual(A_		,				A_      )







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''      )
										lowercase   :       Dict								=AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''      ).to(A_      )
										lowercase   :       List[str]								=-1

										lowercase   :       List[str]								=ids_tensor((1, 5)		,				vocab_size=model.config.vocab_size      ).to(A_      )
										lowercase   :       Optional[Any]								=model.generate(A_		,				max_new_tokens=10		,				do_sample=A_      )
										lowercase   :       Any								=greedy_ids[:, input_ids.shape[1] :]
										lowercase   :       Union[str, Any]								=tokenizer.decode(new_greedy_ids[0]      )

										with CaptureStdout() as cs:
																lowercase   :       int								=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
										lowercase   :       Any								=cs.out[:-1]

										self.assertEqual(A_		,				A_      )







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=AutoTokenizer.from_pretrained('''distilgpt2'''      )
										lowercase   :       Any								=AutoModelForCausalLM.from_pretrained('''distilgpt2'''      ).to(A_      )
										lowercase   :       Union[str, Any]								=-1

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







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       Any								=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''      )
										lowercase   :       Dict								=AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''      ).to(A_      )
										lowercase   :       str								=-1

										lowercase   :       int								=ids_tensor((1, 5)		,				vocab_size=model.config.vocab_size      ).to(A_      )
										lowercase   :       List[Any]								=TextIteratorStreamer(A_		,				timeout=0.0_01      )
										lowercase   :       Tuple								={"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
										lowercase   :       Any								=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_      ):
																lowercase   :       Tuple								=""
																for new_text in streamer:
																						streamer_text += new_text








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_							=					{"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PLBartConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_							=					["""PLBartTokenizer"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_							=					[
							    """PLBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """PLBartForCausalLM""",
							    """PLBartForConditionalGeneration""",
							    """PLBartForSequenceClassification""",
							    """PLBartModel""",
							    """PLBartPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_plbart import PLBartTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_plbart import (
														    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PLBartForCausalLM,
														    PLBartForConditionalGeneration,
														    PLBartForSequenceClassification,
														    PLBartModel,
														    PLBartPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase_							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure)





