


'''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 	lowerCAmelCase__			(			unittest.TestCase	):




			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Any:



					'''simple docstring'''







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

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

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

					self.assertEqual(lowerCamelCase_	,  lowerCamelCase_ )




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Tuple:



					'''simple docstring'''







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

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

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

					self.assertEqual(lowerCamelCase_	,  lowerCamelCase_ )




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



					'''simple docstring'''







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

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

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

					self.assertEqual(lowerCamelCase_	,  lowerCamelCase_ )




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



					'''simple docstring'''







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

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






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



					'''simple docstring'''







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

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










'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__    	=     {
    'configuration_instructblip': [
        'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InstructBlipConfig',
        'InstructBlipQFormerConfig',
        'InstructBlipVisionConfig',
    ],
    'processing_instructblip': ['InstructBlipProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'InstructBlipQFormerModel',
						    'InstructBlipPreTrainedModel',
						    'InstructBlipForConditionalGeneration',
						    'InstructBlipVisionModel',
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

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



