



'''simple docstring'''




from typing import Mapping

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


lowerCamelCase							:Dict				    =					logging.get_logger(__name__)

lowerCamelCase							:Dict				    =					{
    '''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							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		Tuple					=					'umt5'
	__SCREAMING_SNAKE_CASE   :		Tuple					=					['past_key_values']


	def __init__(self       ,      lowercase=250112       ,      lowercase=512       ,      lowercase=64       ,      lowercase=1024       ,      lowercase=8       ,      lowercase=None       ,      lowercase=6       ,      lowercase=32       ,      lowercase=128       ,      lowercase=0.1       ,      lowercase=1E-6       ,      lowercase=1.0       ,      lowercase="gated-gelu"       ,      lowercase=True       ,      lowercase=True       ,      lowercase="T5Tokenizer"       ,      lowercase=True       ,      lowercase=0       ,      lowercase=1       ,      lowercase=0       ,      **lowercase       ,      ):
				super().__init__(
				    is_encoder_decoder=lowercase       ,      tokenizer_class=lowercase       ,      tie_word_embeddings=lowercase       ,      pad_token_id=lowercase       ,      eos_token_id=lowercase       ,      decoder_start_token_id=lowercase       ,      **lowercase       ,      )
				A_	:						int	=     vocab_size
				A_	:						Optional[int]	=     d_model
				A_	:						Tuple	=     d_kv
				A_	:						List[str]	=     d_ff
				A_	:						List[Any]	=     num_layers
				A_	:						str	=     (
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				A_	:						Tuple	=     num_heads
				A_	:						Any	=     relative_attention_num_buckets
				A_	:						Any	=     relative_attention_max_distance
				A_	:						List[str]	=     dropout_rate
				A_	:						Any	=     layer_norm_epsilon
				A_	:						Any	=     initializer_factor
				A_	:						List[Any]	=     feed_forward_proj
				A_	:						Tuple	=     use_cache

				A_	:						Tuple	=     self.feed_forward_proj.split("""-"""							)
				A_	:						Tuple	=     act_info[-1]
				A_	:						Any	=     act_info[0] == """gated"""

				if len(lowercase							) > 1 and act_info[0] != "gated" or len(lowercase							) > 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_	:						str	=     """gelu_new"""


	@property
	def     _a	(self							):
				return self.d_model


	@property
	def     _a	(self							):
				return self.num_heads


	@property
	def     _a	(self							):
				return self.num_layers







class 	_lowerCAmelCase							(			__UpperCAmelCase ):


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

				if self.use_past:
							self.fill_with_past_key_values_(lowercase       ,      direction="""inputs"""							)

				return common_inputs


	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def     _a	(self							):
				return 13


	@property
	def     _a	(self							):
				return 5E-4




'''simple docstring'''




import torch

from diffusers import StableDiffusionPipeline


lowerCamelCase							:Tuple				    =					'''path-to-your-trained-model'''
lowerCamelCase							:Optional[int]				    =					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowerCamelCase							:Optional[int]				    =					'''A photo of sks dog in a bucket'''
lowerCamelCase							:List[Any]				    =					pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')