
"""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   __lowerCAmelCase (						self    :Optional[Any]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					).to(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= -1

			SCREAMING_SNAKE_CASE       :     Any				= ids_tensor((1, 5)		, vocab_size=model.config.vocab_size					).to(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= model.generate(lowerCamelCase_		, max_new_tokens=10		, do_sample=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= tokenizer.decode(greedy_ids[0]					)

			with CaptureStdout() as cs:
					SCREAMING_SNAKE_CASE       :     Tuple				= 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
			SCREAMING_SNAKE_CASE       :     Tuple				= cs.out[:-1]

			self.assertEqual(lowerCamelCase_		, lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Dict					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					).to(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Tuple				= -1

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

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

			self.assertEqual(lowerCamelCase_		, lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :List[str]					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					)
			SCREAMING_SNAKE_CASE       :     Tuple				= AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					).to(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Dict				= -1

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

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

			self.assertEqual(lowerCamelCase_		, lowerCamelCase_					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= AutoTokenizer.from_pretrained('''distilgpt2'''					)
			SCREAMING_SNAKE_CASE       :     Tuple				= AutoModelForCausalLM.from_pretrained('''distilgpt2'''					).to(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= -1

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







	def   __lowerCAmelCase (						self    :Any					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					)
			SCREAMING_SNAKE_CASE       :     Dict				= AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					).to(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= -1

			SCREAMING_SNAKE_CASE       :     int				= ids_tensor((1, 5)		, vocab_size=model.config.vocab_size					).to(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= TextIteratorStreamer(lowerCamelCase_		, timeout=0.0_0_1					)
			SCREAMING_SNAKE_CASE       :     Tuple				= {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 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_					):
					SCREAMING_SNAKE_CASE       :     List[Any]				= ''''''
					for new_text in streamer:
							streamer_text += new_text







"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__ :       Tuple						     =			logging.get_logger(__name__)

lowerCamelCase__ :       str						     =			{
    "studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
    "studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json",
}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """luke"""





	def __init__(						self    :Optional[int]		, lowerCamelCase_    :Union[str, Any]=5_02_67		, lowerCamelCase_    :int=50_00_00		, lowerCamelCase_    :Tuple=7_68		, lowerCamelCase_    :List[str]=2_56		, lowerCamelCase_    :Dict=12		, lowerCamelCase_    :Optional[int]=12		, lowerCamelCase_    :Optional[Any]=30_72		, lowerCamelCase_    :List[Any]="gelu"		, lowerCamelCase_    :Any=0.1		, lowerCamelCase_    :Union[str, Any]=0.1		, lowerCamelCase_    :List[str]=5_12		, lowerCamelCase_    :Optional[int]=2		, lowerCamelCase_    :Tuple=0.0_2		, lowerCamelCase_    :Optional[int]=1E-12		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :List[str]=None		, lowerCamelCase_    :str=1		, lowerCamelCase_    :Any=0		, lowerCamelCase_    :str=2		, **lowerCamelCase_    :List[Any]		, )							->			Optional[int]:







			'''simple docstring'''




			super().__init__(pad_token_id=lowerCamelCase_		, bos_token_id=lowerCamelCase_		, eos_token_id=lowerCamelCase_		, **lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Dict				= vocab_size
			SCREAMING_SNAKE_CASE       :     List[str]				= entity_vocab_size
			SCREAMING_SNAKE_CASE       :     str				= hidden_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= entity_emb_size
			SCREAMING_SNAKE_CASE       :     List[Any]				= num_hidden_layers
			SCREAMING_SNAKE_CASE       :     Optional[int]				= num_attention_heads
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= hidden_act
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= intermediate_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= hidden_dropout_prob
			SCREAMING_SNAKE_CASE       :     Dict				= attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE       :     Dict				= max_position_embeddings
			SCREAMING_SNAKE_CASE       :     Tuple				= type_vocab_size
			SCREAMING_SNAKE_CASE       :     str				= initializer_range
			SCREAMING_SNAKE_CASE       :     Dict				= layer_norm_eps
			SCREAMING_SNAKE_CASE       :     Optional[int]				= use_entity_aware_attention
			SCREAMING_SNAKE_CASE       :     List[str]				= classifier_dropout





