


from typing import Mapping

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


_lowercase       :      str	=	logging.get_logger(__name__)

_lowercase       :      Union[str, Any]	=	{
    "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    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 'umt5'
				lowerCAmelCase 		= ['past_key_values']

				def __init__(						self				,   a__=25_0112				,   a__=512				,   a__=64				,   a__=1024				,   a__=8				,   a__=None				,   a__=6				,   a__=32				,   a__=128				,   a__=0.1				,   a__=1e-6				,   a__=1.0				,   a__="gated-gelu"				,   a__=True				,   a__=True				,   a__="T5Tokenizer"				,   a__=True				,   a__=0				,   a__=1				,   a__=0				,   **a__				,   )	->				List[Any]:
					super().__init__(
					    is_encoder_decoder=a__				,   tokenizer_class=a__				,   tie_word_embeddings=a__				,   pad_token_id=a__				,   eos_token_id=a__				,   decoder_start_token_id=a__				,   **a__				,   )
					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(a__   ) > 1 and act_info[0] != "gated" or len(a__   ) > 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      _UpperCAmelCase					(						self   )	->				Any:
					return self.d_model

				@property
				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					return self.num_heads

				@property
				def      _UpperCAmelCase					(						self   )	->				Tuple:
					return self.num_layers






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
				def      _UpperCAmelCase					(						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_(a__				,   direction="""inputs"""   )

					return common_inputs

				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
				def      _UpperCAmelCase					(						self   )	->				int:
					return 13

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







import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch






@require_torch
class       _UpperCamelCase    :

				"""simple docstring"""







				@property
				def      _UpperCAmelCase					(						self   )	->				Any:
					return self.get_dummy_input()

				@property
				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					if self.block_type == "down":
						return (4, 32, 16, 16)
					elif self.block_type == "mid":
						return (4, 32, 32, 32)
					elif self.block_type == "up":
						return (4, 32, 64, 64)

					raise ValueError(f'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'   )

				def      _UpperCAmelCase					(						self				,   a__=True				,   a__=False				,   a__=False				,   a__=False				,   )	->				Optional[Any]:
					A								=				4
					A								=				32
					A								=				(32, 32)

					A								=				torch.manual_seed(0   )
					A								=				torch.device(a__   )
					A								=				(batch_size, num_channels) + sizes
					A								=				randn_tensor(a__				,   generator=a__				,   device=a__   )
					A								=				{"""hidden_states""": hidden_states}

					if include_temb:
						A								=				128
						A								=				randn_tensor((batch_size, temb_channels)				,   generator=a__				,   device=a__   )

					if include_res_hidden_states_tuple:
						A								=				torch.manual_seed(1   )
						A								=				(randn_tensor(a__				,   generator=a__				,   device=a__   ),)

					if include_encoder_hidden_states:
						A								=				floats_tensor((batch_size, 32, 32)   ).to(a__   )

					if include_skip_sample:
						A								=				randn_tensor(((batch_size, 3) + sizes)				,   generator=a__				,   device=a__   )

					return dummy_input

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				{
					    """in_channels""": 32,
					    """out_channels""": 32,
					    """temb_channels""": 128,
					}
					if self.block_type == "up":
						A								=				32

					if self.block_type == "mid":
						init_dict.pop("""out_channels"""   )

					A								=				self.dummy_input
					return init_dict, inputs_dict

				def      _UpperCAmelCase					(						self				,   a__   )	->				Optional[int]:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					unet_block.to(a__   )
					unet_block.eval()

					with torch.no_grad():
						A								=				unet_block(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					self.assertEqual(output.shape				,   self.output_shape   )

					A								=				output[0, -1, -3:, -3:]
					A								=				torch.tensor(a__   ).to(a__   )
					assert torch_all_close(output_slice.flatten()				,   a__				,   atol=5e-3   )

				@unittest.skipIf(torch_device == """mps"""				,   """Training is not supported in mps"""   )
				def      _UpperCAmelCase					(						self   )	->				str:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					model.to(a__   )
					model.train()
					A								=				model(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					A								=				torch.device(a__   )
					A								=				randn_tensor(output.shape				,   device=a__   )
					A								=				torch.nn.functional.mse_loss(a__				,   a__   )
					loss.backward()



