




'''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 						a				(   unittest.TestCase     ):


				def   A_ (		self    :					List[str]					):
									snake_case_									=				AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					)
									snake_case_									=				AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					).to(lowercase_					)
									snake_case_									=				-1

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

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

									self.assertEqual(lowercase_       ,				lowercase_					)


				def   A_ (		self    :					Tuple					):
									snake_case_									=				AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					)
									snake_case_									=				AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					).to(lowercase_					)
									snake_case_									=				-1

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

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

									self.assertEqual(lowercase_       ,				lowercase_					)


				def   A_ (		self    :					Dict					):
									snake_case_									=				AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					)
									snake_case_									=				AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					).to(lowercase_					)
									snake_case_									=				-1

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

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

									self.assertEqual(lowercase_       ,				lowercase_					)


				def   A_ (		self    :					List[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
									snake_case_									=				AutoTokenizer.from_pretrained('''distilgpt2'''					)
									snake_case_									=				AutoModelForCausalLM.from_pretrained('''distilgpt2'''					).to(lowercase_					)
									snake_case_									=				-1

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

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


				def   A_ (		self    :					str					):
									snake_case_									=				AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					)
									snake_case_									=				AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					).to(lowercase_					)
									snake_case_									=				-1

									snake_case_									=				ids_tensor((1, 5)       ,				vocab_size=model.config.vocab_size					).to(lowercase_					)
									snake_case_									=				TextIteratorStreamer(lowercase_       ,				timeout=0.001					)
									snake_case_									=				{'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
									snake_case_									=				Thread(target=model.generate       ,				kwargs=lowercase_					)
									thread.start()

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









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A	:			Dict         ={
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			List[Any]         =[
			    '''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Swinv2ForImageClassification''',
			    '''Swinv2ForMaskedImageModeling''',
			    '''Swinv2Model''',
			    '''Swinv2PreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

			_A	:			Optional[Any]         =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

