




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

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

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

								self.assertEqual(__snake_case  ,     __snake_case		)







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

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

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

								self.assertEqual(__snake_case  ,     __snake_case		)







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

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

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

								self.assertEqual(__snake_case  ,     __snake_case		)







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Optional[int]:
								# 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[Any]         =      AutoTokenizer.from_pretrained("distilgpt2"		)
								__UpperCamelCase			:     Union[str, Any]         =      AutoModelForCausalLM.from_pretrained("distilgpt2"		).to(__snake_case		)
								__UpperCamelCase			:     Optional[int]         =      -1

								__UpperCamelCase			:     Union[str, Any]         =      torch.ones((1, 5)  ,     device=__snake_case		).long() * model.config.bos_token_id
								with CaptureStdout() as cs:
															__UpperCamelCase			:     Dict         =      TextStreamer(__snake_case  ,     skip_special_tokens=__snake_case		)
															model.generate(__snake_case  ,     max_new_tokens=1  ,     do_sample=__snake_case  ,     streamer=__snake_case		)

								# 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			:     Tuple         =      cs.out[:-1]  # Remove the final "\n"
								__UpperCamelCase			:     int         =      tokenizer(__snake_case  ,     return_tensors="pt"		)
								self.assertEqual(streamer_text_tokenized.input_ids.shape  ,     (1, 1)		)







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

								__UpperCamelCase			:     Tuple         =      ids_tensor((1, 5)  ,     vocab_size=model.config.vocab_size		).to(__snake_case		)
								__UpperCamelCase			:     List[Any]         =      TextIteratorStreamer(__snake_case  ,     timeout=0.001		)
								__UpperCamelCase			:     Dict         =      {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
								__UpperCamelCase			:     Tuple         =      Thread(target=model.generate  ,     kwargs=__snake_case		)
								thread.start()

								# The streamer will timeout after 0.001 seconds, so an exception will be raised
								with self.assertRaises(__snake_case		):
															__UpperCamelCase			:     Dict         =      ''''''
															for new_text in streamer:
																						streamer_text += new_text

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase						:      Optional[Any]									= {
    'configuration_blip_2': [
        'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Blip2Config',
        'Blip2QFormerConfig',
        'Blip2VisionConfig',
    ],
    'processing_blip_2': ['Blip2Processor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Tuple									= [
		    'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Blip2Model',
		    'Blip2QFormerModel',
		    'Blip2PreTrainedModel',
		    'Blip2ForConditionalGeneration',
		    'Blip2VisionModel',
		]

if TYPE_CHECKING:
		from .configuration_blip_a import (
		    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlipaConfig,
		    BlipaQFormerConfig,
		    BlipaVisionConfig,
		)
		from .processing_blip_a import BlipaProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip_a import (
				    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipaForConditionalGeneration,
				    BlipaModel,
				    BlipaPreTrainedModel,
				    BlipaQFormerModel,
				    BlipaVisionModel,
				)

else:
		import sys

		lowercase						:      Dict									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)