
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  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Optional[int]:
								__UpperCamelCase	:   List[Any]			   =					AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"						)
								__UpperCamelCase	:   Optional[Any]			   =					AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"						).to(a						)
								__UpperCamelCase	:   List[Any]			   =					-1

								__UpperCamelCase	:   List[str]			   =					ids_tensor((1, 5)     ,   vocab_size=model.config.vocab_size						).to(a						)
								__UpperCamelCase	:   int			   =					model.generate(a     ,   max_new_tokens=1_0     ,   do_sample=a						)
								__UpperCamelCase	:   Union[str, Any]			   =					tokenizer.decode(greedy_ids[0]						)

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

								self.assertEqual(a     ,   a						)







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> int:
								__UpperCamelCase	:   List[Any]			   =					AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"						)
								__UpperCamelCase	:   Optional[int]			   =					AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"						).to(a						)
								__UpperCamelCase	:   Any			   =					-1

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

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

								self.assertEqual(a     ,   a						)







	def    _lowerCamelCase				(					self		:Any						)		-> Dict:
								__UpperCamelCase	:   Any			   =					AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"						)
								__UpperCamelCase	:   str			   =					AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"						).to(a						)
								__UpperCamelCase	:   List[Any]			   =					-1

								__UpperCamelCase	:   Union[str, Any]			   =					ids_tensor((1, 5)     ,   vocab_size=model.config.vocab_size						).to(a						)
								__UpperCamelCase	:   Any			   =					model.generate(a     ,   max_new_tokens=1_0     ,   do_sample=a						)
								__UpperCamelCase	:   List[str]			   =					greedy_ids[:, input_ids.shape[1] :]
								__UpperCamelCase	:   str			   =					tokenizer.decode(new_greedy_ids[0]						)

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

								self.assertEqual(a     ,   a						)







	def    _lowerCamelCase				(					self		:Any						)		-> Tuple:
								# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
								# with actual models -- the dummy models' tokenizers are not aligned with their models, and
								# `skip_special_tokens=True` has no effect on them
								__UpperCamelCase	:   List[str]			   =					AutoTokenizer.from_pretrained("distilgpt2"						)
								__UpperCamelCase	:   Any			   =					AutoModelForCausalLM.from_pretrained("distilgpt2"						).to(a						)
								__UpperCamelCase	:   Tuple			   =					-1

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







	def    _lowerCamelCase				(					self		:str						)		-> List[Any]:
								__UpperCamelCase	:   int			   =					AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"						)
								__UpperCamelCase	:   List[str]			   =					AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"						).to(a						)
								__UpperCamelCase	:   List[str]			   =					-1

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

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


lowercase  :					List[Any]													=       logging.get_logger(__name__)

lowercase  :					Any													=       {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     'pegasus'
	_A								=     ['past_key_values']
	_A								=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







	def __init__(					self		:Dict     ,   a		:Dict=5_0_2_6_5     ,   a		:Dict=1_0_2_4     ,   a		:Union[str, Any]=1_2     ,   a		:Any=4_0_9_6     ,   a		:str=1_6     ,   a		:str=1_2     ,   a		:Optional[Any]=4_0_9_6     ,   a		:int=1_6     ,   a		:Optional[int]=0.0     ,   a		:Optional[int]=0.0     ,   a		:List[Any]=True     ,   a		:Union[str, Any]=True     ,   a		:int="gelu"     ,   a		:Dict=1_0_2_4     ,   a		:List[Any]=0.1     ,   a		:List[str]=0.0     ,   a		:List[Any]=0.0     ,   a		:str=0.02     ,   a		:int=0     ,   a		:Any=False     ,   a		:Dict=0     ,   a		:int=1     ,   a		:Optional[Any]=1     ,   **a		:Optional[int]     ,   )		-> str:
								__UpperCamelCase	:   List[Any]			   =					vocab_size
								__UpperCamelCase	:   Union[str, Any]			   =					max_position_embeddings
								__UpperCamelCase	:   str			   =					d_model
								__UpperCamelCase	:   Dict			   =					encoder_ffn_dim
								__UpperCamelCase	:   int			   =					encoder_layers
								__UpperCamelCase	:   int			   =					encoder_attention_heads
								__UpperCamelCase	:   List[Any]			   =					decoder_ffn_dim
								__UpperCamelCase	:   List[Any]			   =					decoder_layers
								__UpperCamelCase	:   List[str]			   =					decoder_attention_heads
								__UpperCamelCase	:   str			   =					dropout
								__UpperCamelCase	:   Union[str, Any]			   =					attention_dropout
								__UpperCamelCase	:   List[str]			   =					activation_dropout
								__UpperCamelCase	:   Optional[Any]			   =					activation_function
								__UpperCamelCase	:   Tuple			   =					init_std
								__UpperCamelCase	:   Optional[int]			   =					encoder_layerdrop
								__UpperCamelCase	:   Union[str, Any]			   =					decoder_layerdrop
								__UpperCamelCase	:   Optional[Any]			   =					use_cache
								__UpperCamelCase	:   Union[str, Any]			   =					encoder_layers
								__UpperCamelCase	:   int			   =					scale_embedding  # scale factor will be sqrt(d_model) if True
								super().__init__(
								    pad_token_id=a     ,   eos_token_id=a     ,   is_encoder_decoder=a     ,   decoder_start_token_id=a     ,   forced_eos_token_id=a     ,   **a     ,   )







	@property
	def    _lowerCamelCase				(					self		:Dict						)		-> int:
								return self.encoder_attention_heads







	@property
	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> int:
								return self.d_model