

"""simple docstring"""






import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
					import torch

					from transformers import BlipTextModel
					from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class 				__a    :



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase=12     ,						_lowerCamelCase=7     ,						_lowerCamelCase=True     ,						_lowerCamelCase=True     ,						_lowerCamelCase=True     ,						_lowerCamelCase=99     ,						_lowerCamelCase=32     ,						_lowerCamelCase=32     ,						_lowerCamelCase=5     ,						_lowerCamelCase=4     ,						_lowerCamelCase=37     ,						_lowerCamelCase=0.1     ,						_lowerCamelCase=0.1     ,						_lowerCamelCase=512     ,						_lowerCamelCase=0.02     ,						_lowerCamelCase=0     ,						_lowerCamelCase=None     ,						) ->       Optional[int]:




										'''simple docstring'''
										__lowercase						      =					parent
										__lowercase						      =					batch_size
										__lowercase						      =					seq_length
										__lowercase						      =					is_training
										__lowercase						      =					use_input_mask
										__lowercase						      =					use_labels
										__lowercase						      =					vocab_size
										__lowercase						      =					hidden_size
										__lowercase						      =					projection_dim
										__lowercase						      =					num_hidden_layers
										__lowercase						      =					num_attention_heads
										__lowercase						      =					intermediate_size
										__lowercase						      =					dropout
										__lowercase						      =					attention_dropout
										__lowercase						      =					max_position_embeddings
										__lowercase						      =					initializer_range
										__lowercase						      =					scope
										__lowercase						      =					bos_token_id
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										__lowercase						      =					ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size					)

										__lowercase						      =					None
										if self.use_input_mask:
													__lowercase						      =					random_attention_mask([self.batch_size, self.seq_length]					)

										if input_mask is not None:
													__lowercase						      =					input_mask.shape
													__lowercase						      =					np.random.randint(1     ,						seq_length - 1     ,						size=(batch_size,)					)
													for batch_idx, start_index in enumerate(_lowerCamelCase					):
																__lowercase						      =					1
																__lowercase						      =					0

										__lowercase						      =					self.get_config()

										return config, input_ids, input_mask
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Optional[int]:




										'''simple docstring'''
										return BlipTextConfig(
										    vocab_size=self.vocab_size     ,						hidden_size=self.hidden_size     ,						projection_dim=self.projection_dim     ,						num_hidden_layers=self.num_hidden_layers     ,						num_attention_heads=self.num_attention_heads     ,						intermediate_size=self.intermediate_size     ,						dropout=self.dropout     ,						attention_dropout=self.attention_dropout     ,						max_position_embeddings=self.max_position_embeddings     ,						initializer_range=self.initializer_range     ,						bos_token_id=self.bos_token_id     ,						)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Optional[int]:




										'''simple docstring'''
										__lowercase						      =					BlipTextModel(config=_lowerCamelCase					)
										model.to(_lowerCamelCase					)
										model.eval()
										with torch.no_grad():
													__lowercase						      =					model(_lowerCamelCase     ,						attention_mask=_lowerCamelCase					)
													__lowercase						      =					model(_lowerCamelCase					)
										self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)					)
										self.parent.assertEqual(result.pooler_output.shape     ,						(self.batch_size, self.hidden_size)					)





							def      SCREAMING_SNAKE_CASE	(    self					) ->       int:




										'''simple docstring'''
										__lowercase						      =					self.prepare_config_and_inputs()
										__lowercase						      =					config_and_inputs
										__lowercase						      =					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
										return config, inputs_dict





@require_torch
class 				__a    (   __a				,      unittest.TestCase					):



							'''simple docstring'''







							_lowerCamelCase				: Optional[Any]					      =      (BlipTextModel,) if is_torch_available() else ()
							_lowerCamelCase				: Tuple					      =      False
							_lowerCamelCase				: List[Any]					      =      False
							_lowerCamelCase				: Union[str, Any]					      =      False
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					BlipTextModelTester(self					)
										__lowercase						      =					ConfigTester(self     ,						config_class=_lowerCamelCase     ,						hidden_size=37					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Any:




										'''simple docstring'''
										self.config_tester.run_common_tests()
							def      SCREAMING_SNAKE_CASE	(    self					) ->       int:




										'''simple docstring'''
										__lowercase						      =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										pass
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										pass
							@unittest.skip(reason="Blip does not use inputs_embeds"					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




										'''simple docstring'''
										pass
							@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										pass
							@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										pass
							@slow
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowercase						      =					BlipTextModel.from_pretrained(_lowerCamelCase					)
													self.assertIsNotNone(_lowerCamelCase					)





							def      SCREAMING_SNAKE_CASE	(    self					) ->       Optional[int]:




										'''simple docstring'''
										super().test_pt_tf_model_equivalence(allow_missing_keys=_lowerCamelCase					)







"""simple docstring"""






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
					from .testing_utils import (
					    floats_tensor,
					    load_hf_numpy,
					    load_image,
					    load_numpy,
					    load_pt,
					    nightly,
					    parse_flag_from_env,
					    print_tensor_test,
					    require_torch_a,
					    require_torch_gpu,
					    skip_mps,
					    slow,
					    torch_all_close,
					    torch_device,
					)
					from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_lowercase							 =							get_logger(__name__)



def     lowerCAmelCase__ (		__magic_name__			)	->Optional[int]:
			if version.parse(__magic_name__			) < version.parse(__magic_name__			):
						if "dev" in min_version:
									__lowercase						      =					(
									    "This example requires a source install from HuggingFace diffusers (see "
									    "`https://huggingface.co/docs/diffusers/installation#install-from-source`),"
									)
						else:
									__lowercase						      =					F'''This example requires a minimum version of {min_version},'''
						error_message += F''' but the version found is {__version__}.\n'''
						raise ImportError(__magic_name__			)





