






'''simple docstring'''







from typing import Mapping

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


_A			:    Dict					  =logging.get_logger(__name__)

_A			:    Optional[int]					  ={
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}

class _lowercase			(       _lowercase	):
			a          = """t5"""
			a          = ["""past_key_values"""]
			a          = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




			def __init__(							self:       Any    ,							UpperCamelCase__:       Any=32_128    ,							UpperCamelCase__:       Optional[int]=512    ,							UpperCamelCase__:       str=64    ,							UpperCamelCase__:       str=2_048    ,							UpperCamelCase__:       Any=6    ,							UpperCamelCase__:       Tuple=None    ,							UpperCamelCase__:       Dict=8    ,							UpperCamelCase__:       List[str]=32    ,							UpperCamelCase__:       int=128    ,							UpperCamelCase__:       Any=0.1    ,							UpperCamelCase__:       Union[str, Any]=1e-6    ,							UpperCamelCase__:       Tuple=1.0    ,							UpperCamelCase__:       str="relu"    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       List[Any]=True    ,							UpperCamelCase__:       Any=0    ,							UpperCamelCase__:       int=1    ,							**UpperCamelCase__:       List[str]    ,							):
										lowerCamelCase__      :			int 						= vocab_size
										lowerCamelCase__      :			str 						= d_model
										lowerCamelCase__      :			Tuple 						= d_kv
										lowerCamelCase__      :			str 						= d_ff
										lowerCamelCase__      :			List[Any] 						= num_layers
										lowerCamelCase__      :			Tuple 						= (
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										lowerCamelCase__      :			str 						= num_heads
										lowerCamelCase__      :			Dict 						= relative_attention_num_buckets
										lowerCamelCase__      :			int 						= relative_attention_max_distance
										lowerCamelCase__      :			List[str] 						= dropout_rate
										lowerCamelCase__      :			Any 						= layer_norm_epsilon
										lowerCamelCase__      :			Dict 						= initializer_factor
										lowerCamelCase__      :			Tuple 						= feed_forward_proj
										lowerCamelCase__      :			Dict 						= use_cache

										lowerCamelCase__      :			List[Any] 						= self.feed_forward_proj.split("""-"""  )
										lowerCamelCase__      :			List[Any] 						= act_info[-1]
										lowerCamelCase__      :			Optional[int] 						= 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'"""  )

										# for backwards compatibility
										if feed_forward_proj == "gated-gelu":
																	lowerCamelCase__      :			List[str] 						= """gelu_new"""

										super().__init__(
										    pad_token_id=UpperCamelCase__    ,							eos_token_id=UpperCamelCase__    ,							is_encoder_decoder=UpperCamelCase__    ,							**UpperCamelCase__    ,							)



class _lowercase			(       _lowercase	):




			@property
			def 	lowerCamelCase_       (							self:       List[str]  ):
										lowerCamelCase__      :			Optional[int] 						= {
										    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
										    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
										}
										if self.use_past:
																	lowerCamelCase__      :			List[str] 						= """past_encoder_sequence + sequence"""
																	lowerCamelCase__      :			Dict 						= {0: """batch"""}
																	lowerCamelCase__      :			Dict 						= {0: """batch""", 1: """past_decoder_sequence + sequence"""}
										else:
																	lowerCamelCase__      :			int 						= {0: """batch""", 1: """decoder_sequence"""}
																	lowerCamelCase__      :			Optional[Any] 						= {0: """batch""", 1: """decoder_sequence"""}

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

										return common_inputs




			@property
			def 	lowerCamelCase_       (							self:       Optional[Any]  ):
										return 13









'''simple docstring'''







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_A			:    Union[str, Any]					  =False

class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       str=32  ):
														set_seed(0  )
														lowerCamelCase__      :			Optional[int] 						= UNetaDModel(sample_size=UpperCamelCase__    ,							in_channels=3    ,							out_channels=3  )
														lowerCamelCase__      :			List[Any] 						= torch.optim.SGD(model.parameters()    ,							lr=0.0_001  )
														return model, optimizer




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
														lowerCamelCase__      :			List[Any] 						= DDPMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Any 						= DDIMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)

														assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

														# shared batches for DDPM and DDIM
														set_seed(0  )
														lowerCamelCase__      :			str 						= [torch.randn((4, 3, 32, 32)  ).clip(-1    ,							1  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randn((4, 3, 32, 32)  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randint(0    ,							1_000    ,							(4,)  ).long().to(UpperCamelCase__  ) for _ in range(4  )]

														# train with a DDPM scheduler
														lowerCamelCase__						,	lowerCamelCase__      :			Any 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			str 						= ddpm_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			str 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Tuple 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														# recreate the model and optimizer, and retry with DDIM
														lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			Optional[Any] 						= ddim_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			Dict 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Union[str, Any] 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )
														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )

