


"""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       ):




		'''simple docstring'''







		def 				__A		(      self							)	->				List[str]:
								SCREAMING_SNAKE_CASE						=   AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
								SCREAMING_SNAKE_CASE						=   AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'							).to(__UpperCamelCase							)
								SCREAMING_SNAKE_CASE						=   -1

								SCREAMING_SNAKE_CASE						=   ids_tensor((1, 5)		,			vocab_size=model.config.vocab_size							).to(__UpperCamelCase							)
								SCREAMING_SNAKE_CASE						=   model.generate(__UpperCamelCase		,			max_new_tokens=10		,			do_sample=__UpperCamelCase							)
								SCREAMING_SNAKE_CASE						=   tokenizer.decode(greedy_ids[0]							)

								with CaptureStdout() as cs:
														SCREAMING_SNAKE_CASE						=   TextStreamer(__UpperCamelCase							)
														model.generate(__UpperCamelCase		,			max_new_tokens=10		,			do_sample=__UpperCamelCase		,			streamer=__UpperCamelCase							)
								# The greedy text should be printed to stdout, except for the final "\n" in the streamer
								SCREAMING_SNAKE_CASE						=   cs.out[:-1]

								self.assertEqual(__UpperCamelCase		,			__UpperCamelCase							)







		def 				__A		(      self							)	->				List[str]:
								SCREAMING_SNAKE_CASE						=   AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
								SCREAMING_SNAKE_CASE						=   AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'							).to(__UpperCamelCase							)
								SCREAMING_SNAKE_CASE						=   -1

								SCREAMING_SNAKE_CASE						=   ids_tensor((1, 5)		,			vocab_size=model.config.vocab_size							).to(__UpperCamelCase							)
								SCREAMING_SNAKE_CASE						=   model.generate(__UpperCamelCase		,			max_new_tokens=10		,			do_sample=__UpperCamelCase							)
								SCREAMING_SNAKE_CASE						=   tokenizer.decode(greedy_ids[0]							)

								SCREAMING_SNAKE_CASE						=   TextIteratorStreamer(__UpperCamelCase							)
								SCREAMING_SNAKE_CASE						=   {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
								SCREAMING_SNAKE_CASE						=   Thread(target=model.generate		,			kwargs=__UpperCamelCase							)
								thread.start()
								SCREAMING_SNAKE_CASE						=   """"""
								for new_text in streamer:
														streamer_text += new_text

								self.assertEqual(__UpperCamelCase		,			__UpperCamelCase							)







		def 				__A		(      self							)	->				int:
								SCREAMING_SNAKE_CASE						=   AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
								SCREAMING_SNAKE_CASE						=   AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'							).to(__UpperCamelCase							)
								SCREAMING_SNAKE_CASE						=   -1

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

								with CaptureStdout() as cs:
														SCREAMING_SNAKE_CASE						=   TextStreamer(__UpperCamelCase		,			skip_prompt=__UpperCamelCase							)
														model.generate(__UpperCamelCase		,			max_new_tokens=10		,			do_sample=__UpperCamelCase		,			streamer=__UpperCamelCase							)
								# The greedy text should be printed to stdout, except for the final "\n" in the streamer
								SCREAMING_SNAKE_CASE						=   cs.out[:-1]

								self.assertEqual(__UpperCamelCase		,			__UpperCamelCase							)







		def 				__A		(      self							)	->				Optional[Any]:
								# 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
								SCREAMING_SNAKE_CASE						=   AutoTokenizer.from_pretrained('distilgpt2'							)
								SCREAMING_SNAKE_CASE						=   AutoModelForCausalLM.from_pretrained('distilgpt2'							).to(__UpperCamelCase							)
								SCREAMING_SNAKE_CASE						=   -1

								SCREAMING_SNAKE_CASE						=   torch.ones((1, 5)		,			device=__UpperCamelCase							).long() * model.config.bos_token_id
								with CaptureStdout() as cs:
														SCREAMING_SNAKE_CASE						=   TextStreamer(__UpperCamelCase		,			skip_special_tokens=__UpperCamelCase							)
														model.generate(__UpperCamelCase		,			max_new_tokens=1		,			do_sample=__UpperCamelCase		,			streamer=__UpperCamelCase							)

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







		def 				__A		(      self							)	->				List[Any]:
								SCREAMING_SNAKE_CASE						=   AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
								SCREAMING_SNAKE_CASE						=   AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'							).to(__UpperCamelCase							)
								SCREAMING_SNAKE_CASE						=   -1

								SCREAMING_SNAKE_CASE						=   ids_tensor((1, 5)		,			vocab_size=model.config.vocab_size							).to(__UpperCamelCase							)
								SCREAMING_SNAKE_CASE						=   TextIteratorStreamer(__UpperCamelCase		,			timeout=0.0_01							)
								SCREAMING_SNAKE_CASE						=   {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
								SCREAMING_SNAKE_CASE						=   Thread(target=model.generate		,			kwargs=__UpperCamelCase							)
								thread.start()

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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Optional[Any]         =	["BridgeTowerImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Tuple         =	[
	    "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BridgeTowerForContrastiveLearning",
	    "BridgeTowerForImageAndTextRetrieval",
	    "BridgeTowerForMaskedLM",
	    "BridgeTowerModel",
	    "BridgeTowerPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_bridgetower import (
	    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BridgeTowerConfig,
	    BridgeTowerTextConfig,
	    BridgeTowerVisionConfig,
	)
	from .processing_bridgetower import BridgeTowerProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bridgetower import BridgeTowerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bridgetower import (
		    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BridgeTowerForContrastiveLearning,
		    BridgeTowerForImageAndTextRetrieval,
		    BridgeTowerForMaskedLM,
		    BridgeTowerModel,
		    BridgeTowerPreTrainedModel,
		)


else:
	import sys

	SCREAMING_SNAKE_CASE		:     List[Any]         =	_LazyModule(__name__, globals()["__file__"], _import_structure)






