"""simple docstring"""




from typing import Mapping

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


__lowerCamelCase											=  logging.get_logger(__name__)

__lowerCamelCase											=  {
    "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     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						int													=		'umt5'
						lowerCAmelCase__  :						Optional[int]													=		['past_key_values']





						def __init__(       self		,__UpperCAmelCase=25_01_12		,__UpperCAmelCase=5_12		,__UpperCAmelCase=64		,__UpperCAmelCase=10_24		,__UpperCAmelCase=8		,__UpperCAmelCase=None		,__UpperCAmelCase=6		,__UpperCAmelCase=32		,__UpperCAmelCase=1_28		,__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		,)					->				Dict:
													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		,)
													A__	     =							vocab_size
													A__	     =							d_model
													A__	     =							d_kv
													A__	     =							d_ff
													A__	     =							num_layers
													A__	     =							(
													    num_decoder_layers if num_decoder_layers is not None else self.num_layers
													)  # default = symmetry
													A__	     =							num_heads
													A__	     =							relative_attention_num_buckets
													A__	     =							relative_attention_max_distance
													A__	     =							dropout_rate
													A__	     =							layer_norm_epsilon
													A__	     =							initializer_factor
													A__	     =							feed_forward_proj
													A__	     =							use_cache

													A__	     =							self.feed_forward_proj.split('-'     )
													A__	     =							act_info[-1]
													A__	     =							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":
																				A__	     =							'gelu_new'





						@property
						def 				snake_case__	(       self     )					->				str:
													return self.d_model





						@property
						def 				snake_case__	(       self     )					->				str:
													return self.num_heads





						@property
						def 				snake_case__	(       self     )					->				Optional[int]:
													return self.num_layers






class     UpperCamelCase__(			__A ):





						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def 				snake_case__	(       self     )					->				Mapping[str, Mapping[int, str]]:
													A__	     =							{
													    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
													    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
													}
													if self.use_past:
																				A__	     =							'past_encoder_sequence + sequence'
																				A__	     =							{0: 'batch'}
																				A__	     =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
													else:
																				A__	     =							{0: 'batch', 1: 'decoder_sequence'}
																				A__	     =							{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 				snake_case__	(       self     )					->				int:
													return 13





						@property
						def 				snake_case__	(       self     )					->				float:
													return 5e-4






"""simple docstring"""




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		'M-CLIP'





						def __init__(       self		,__UpperCAmelCase=10_24		,__UpperCAmelCase=7_68		,**__UpperCAmelCase     )					->				List[str]:
													A__	     =							transformerDimSize
													A__	     =							imageDimSize
													super().__init__(**__UpperCAmelCase     )






class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		MCLIPConfig





						def __init__(       self		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[Any]:
													super().__init__(__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )
													A__	     =							XLMRobertaModel(__UpperCAmelCase     )
													A__	     =							torch.nn.Linear(
													    in_features=config.transformerDimensions		,out_features=config.numDims     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Tuple:
													A__	     =							self.transformer(input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase     )[0]
													A__	     =							(embs * attention_mask.unsqueeze(2     )).sum(dim=1     ) / attention_mask.sum(dim=1     )[:, None]
													return self.LinearTransformation(__UpperCAmelCase     ), embs





