






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCAmelCase     							=						{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase     							=						['OwlViTFeatureExtractor']
		__UpperCAmelCase     							=						['OwlViTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase     							=						[
		    'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'OwlViTModel',
		    'OwlViTPreTrainedModel',
		    'OwlViTTextModel',
		    'OwlViTVisionModel',
		    'OwlViTForObjectDetection',
		]

if TYPE_CHECKING:
		from .configuration_owlvit import (
		    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    OwlViTConfig,
		    OwlViTOnnxConfig,
		    OwlViTTextConfig,
		    OwlViTVisionConfig,
		)
		from .processing_owlvit import OwlViTProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_owlvit import OwlViTFeatureExtractor
				from .image_processing_owlvit import OwlViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_owlvit import (
				    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OwlViTForObjectDetection,
				    OwlViTModel,
				    OwlViTPreTrainedModel,
				    OwlViTTextModel,
				    OwlViTVisionModel,
				)

else:
		import sys

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














from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Union[str, Any]							=  '''umt5'''
		_snake_case   :   Union[str, Any]							=  ['''past_key_values''']



		def __init__(							self    ,		_UpperCamelCase=2_5_0_1_1_2    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=6_4    ,		_UpperCamelCase=1_0_2_4    ,		_UpperCamelCase=8    ,		_UpperCamelCase=None    ,		_UpperCamelCase=6    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=1E-6    ,		_UpperCamelCase=1.0    ,		_UpperCamelCase="gated-gelu"    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase="T5Tokenizer"    ,		_UpperCamelCase=True    ,		_UpperCamelCase=0    ,		_UpperCamelCase=1    ,		_UpperCamelCase=0    ,		**_UpperCamelCase    ,		)     ->							List[Any]:
							super().__init__(
							    is_encoder_decoder=_UpperCamelCase    ,		tokenizer_class=_UpperCamelCase    ,		tie_word_embeddings=_UpperCamelCase    ,		pad_token_id=_UpperCamelCase    ,		eos_token_id=_UpperCamelCase    ,		decoder_start_token_id=_UpperCamelCase    ,		**_UpperCamelCase    ,		)
							UpperCAmelCase_   :				Union[str, Any]						=						vocab_size
							UpperCAmelCase_   :				List[str]						=						d_model
							UpperCAmelCase_   :				Any						=						d_kv
							UpperCAmelCase_   :				Optional[int]						=						d_ff
							UpperCAmelCase_   :				List[Any]						=						num_layers
							UpperCAmelCase_   :				Optional[Any]						=						(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							UpperCAmelCase_   :				Optional[int]						=						num_heads
							UpperCAmelCase_   :				Optional[int]						=						relative_attention_num_buckets
							UpperCAmelCase_   :				Dict						=						relative_attention_max_distance
							UpperCAmelCase_   :				Tuple						=						dropout_rate
							UpperCAmelCase_   :				Union[str, Any]						=						layer_norm_epsilon
							UpperCAmelCase_   :				Optional[int]						=						initializer_factor
							UpperCAmelCase_   :				List[str]						=						feed_forward_proj
							UpperCAmelCase_   :				Any						=						use_cache

							UpperCAmelCase_   :				List[Any]						=						self.feed_forward_proj.split('-'       )
							UpperCAmelCase_   :				List[Any]						=						act_info[-1]
							UpperCAmelCase_   :				Union[str, Any]						=						act_info[0] == 'gated'

							if len(_UpperCamelCase       ) > 1 and act_info[0] != "gated" or len(_UpperCamelCase       ) > 2:
												raise ValueError(
												    f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
												    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
												    '\'gated-gelu\' or \'relu\''       )

							if feed_forward_proj == "gated-gelu":
												UpperCAmelCase_   :				Optional[int]						=						'gelu_new'



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return self.d_model



		@property
		def  __UpperCAmelCase      (							self       )     ->							Any:
							return self.num_heads



		@property
		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							return self.num_layers





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
		def  __UpperCAmelCase      (							self       )     ->							Mapping[str, Mapping[int, str]]:
							UpperCAmelCase_   :				str						=						{
							    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
							    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
							}
							if self.use_past:
												UpperCAmelCase_   :				Optional[int]						=						'past_encoder_sequence + sequence'
												UpperCAmelCase_   :				str						=						{0: 'batch'}
												UpperCAmelCase_   :				Optional[int]						=						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
												UpperCAmelCase_   :				Optional[int]						=						{0: 'batch', 1: 'decoder_sequence'}
												UpperCAmelCase_   :				Union[str, Any]						=						{0: 'batch', 1: 'decoder_sequence'}

							if self.use_past:
												self.fill_with_past_key_values_(_UpperCamelCase    ,		direction='inputs'       )

							return common_inputs



		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def  __UpperCAmelCase      (							self       )     ->							int:
							return 1_3



		@property
		def  __UpperCAmelCase      (							self       )     ->							float:
							return 5E-4






