





'''simple docstring'''


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block

@dataclass
class 		__lowercase			(					_lowercase      ):
			lowerCamelCase     :						torch.FloatTensor

class 		__lowercase			(					_lowercase ,       _lowercase      ):

			@register_to_config
			def __init__(self     ,				A = None     ,				A = 3     ,				A = 3     ,				A = False     ,				A = "positional"     ,				A = 0     ,				A = True     ,				A = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")     ,				A = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")     ,				A = (2_2_4, 4_4_8, 6_7_2, 8_9_6)     ,				A = 2     ,				A = 1     ,				A = 1     ,				A = "conv"     ,				A = "conv"     ,				A = "silu"     ,				A = 8     ,				A = 3_2     ,				A = 1E-5     ,				A = "default"     ,				A = True     ,				A = None     ,				A = None     ,				):
								super().__init__()

								lowerCamelCase_   : Optional[int]       =						sample_size
								lowerCamelCase_   : int       =						block_out_channels[0] * 4

								# Check inputs
								if len(A ) != len(A ):
													raise ValueError(
													    F"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.""" )

								if len(A ) != len(A ):
													raise ValueError(
													    F"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.""" )

								# input
								lowerCamelCase_   : Optional[int]       =						nn.Convad(A     ,				block_out_channels[0]     ,				kernel_size=3     ,				padding=(1, 1) )

								# time
								if time_embedding_type == "fourier":
													lowerCamelCase_   : Optional[Any]       =						GaussianFourierProjection(embedding_size=block_out_channels[0]     ,				scale=1_6 )
													lowerCamelCase_   : List[str]       =						2 * block_out_channels[0]
								elif time_embedding_type == "positional":
													lowerCamelCase_   : Tuple       =						Timesteps(block_out_channels[0]     ,				A     ,				A )
													lowerCamelCase_   : List[Any]       =						block_out_channels[0]

								lowerCamelCase_   : Union[str, Any]       =						TimestepEmbedding(A     ,				A )

								# class embedding
								if class_embed_type is None and num_class_embeds is not None:
													lowerCamelCase_   : Any       =						nn.Embedding(A     ,				A )
								elif class_embed_type == "timestep":
													lowerCamelCase_   : Union[str, Any]       =						TimestepEmbedding(A     ,				A )
								elif class_embed_type == "identity":
													lowerCamelCase_   : List[Any]       =						nn.Identity(A     ,				A )
								else:
													lowerCamelCase_   : List[str]       =						None

								lowerCamelCase_   : Tuple       =						nn.ModuleList([] )
								lowerCamelCase_   : Union[str, Any]       =						None
								lowerCamelCase_   : Any       =						nn.ModuleList([] )

								# down
								lowerCamelCase_   : int       =						block_out_channels[0]
								for i, down_block_type in enumerate(A ):
													lowerCamelCase_   : Optional[Any]       =						output_channel
													lowerCamelCase_   : Optional[Any]       =						block_out_channels[i]
													lowerCamelCase_   : str       =						i == len(A ) - 1

													lowerCamelCase_   : List[Any]       =						get_down_block(
													    A     ,				num_layers=A     ,				in_channels=A     ,				out_channels=A     ,				temb_channels=A     ,				add_downsample=not is_final_block     ,				resnet_eps=A     ,				resnet_act_fn=A     ,				resnet_groups=A     ,				attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel     ,				downsample_padding=A     ,				resnet_time_scale_shift=A     ,				downsample_type=A     ,				)
													self.down_blocks.append(A )

								# mid
								lowerCamelCase_   : Any       =						UNetMidBlockaD(
								    in_channels=block_out_channels[-1]     ,				temb_channels=A     ,				resnet_eps=A     ,				resnet_act_fn=A     ,				output_scale_factor=A     ,				resnet_time_scale_shift=A     ,				attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]     ,				resnet_groups=A     ,				add_attention=A     ,				)

								# up
								lowerCamelCase_   : List[str]       =						list(reversed(A ) )
								lowerCamelCase_   : Optional[int]       =						reversed_block_out_channels[0]
								for i, up_block_type in enumerate(A ):
													lowerCamelCase_   : int       =						output_channel
													lowerCamelCase_   : List[Any]       =						reversed_block_out_channels[i]
													lowerCamelCase_   : Optional[Any]       =						reversed_block_out_channels[min(i + 1     ,				len(A ) - 1 )]

													lowerCamelCase_   : int       =						i == len(A ) - 1

													lowerCamelCase_   : Any       =						get_up_block(
													    A     ,				num_layers=layers_per_block + 1     ,				in_channels=A     ,				out_channels=A     ,				prev_output_channel=A     ,				temb_channels=A     ,				add_upsample=not is_final_block     ,				resnet_eps=A     ,				resnet_act_fn=A     ,				resnet_groups=A     ,				attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel     ,				resnet_time_scale_shift=A     ,				upsample_type=A     ,				)
													self.up_blocks.append(A )
													lowerCamelCase_   : Any       =						output_channel

								# out
								lowerCamelCase_   : Union[str, Any]       =						norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4     ,				3_2 )
								lowerCamelCase_   : List[Any]       =						nn.GroupNorm(num_channels=block_out_channels[0]     ,				num_groups=A     ,				eps=A )
								lowerCamelCase_   : Dict       =						nn.SiLU()
								lowerCamelCase_   : Dict       =						nn.Convad(block_out_channels[0]     ,				A     ,				kernel_size=3     ,				padding=1 )






			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A = None     ,				A = True     ,				):
								# 0. center input if necessary
								if self.config.center_input_sample:
													lowerCamelCase_   : Dict       =						2 * sample - 1.0

								# 1. time
								lowerCamelCase_   : Dict       =						timestep
								if not torch.is_tensor(A ):
													lowerCamelCase_   : Any       =						torch.tensor([timesteps]     ,				dtype=torch.long     ,				device=sample.device )
								elif torch.is_tensor(A ) and len(timesteps.shape ) == 0:
													lowerCamelCase_   : Tuple       =						timesteps[None].to(sample.device )

								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								lowerCamelCase_   : List[Any]       =						timesteps * torch.ones(sample.shape[0]     ,				dtype=timesteps.dtype     ,				device=timesteps.device )

								lowerCamelCase_   : List[Any]       =						self.time_proj(A )

								# timesteps does not contain any weights and will always return f32 tensors
								# but time_embedding might actually be running in fp16. so we need to cast here.
								# there might be better ways to encapsulate this.
								lowerCamelCase_   : Tuple       =						t_emb.to(dtype=self.dtype )
								lowerCamelCase_   : Union[str, Any]       =						self.time_embedding(A )

								if self.class_embedding is not None:
													if class_labels is None:
																		raise ValueError('''class_labels should be provided when doing class conditioning''' )

													if self.config.class_embed_type == "timestep":
																		lowerCamelCase_   : Optional[Any]       =						self.time_proj(A )

													lowerCamelCase_   : Union[str, Any]       =						self.class_embedding(A ).to(dtype=self.dtype )
													lowerCamelCase_   : Optional[Any]       =						emb + class_emb

								# 2. pre-process
								lowerCamelCase_   : Tuple       =						sample
								lowerCamelCase_   : int       =						self.conv_in(A )

								# 3. down
								lowerCamelCase_   : Optional[int]       =						(sample,)
								for downsample_block in self.down_blocks:
													if hasattr(A     ,				'''skip_conv''' ):
																		lowerCamelCase_   : List[str]       =						downsample_block(
																		    hidden_states=A     ,				temb=A     ,				skip_sample=A )
													else:
																		lowerCamelCase_   : Union[str, Any]       =						downsample_block(hidden_states=A     ,				temb=A )

													down_block_res_samples += res_samples

								# 4. mid
								lowerCamelCase_   : Dict       =						self.mid_block(A     ,				A )

								# 5. up
								lowerCamelCase_   : int       =						None
								for upsample_block in self.up_blocks:
													lowerCamelCase_   : Tuple       =						down_block_res_samples[-len(upsample_block.resnets ) :]
													lowerCamelCase_   : Optional[int]       =						down_block_res_samples[: -len(upsample_block.resnets )]

													if hasattr(A     ,				'''skip_conv''' ):
																		lowerCamelCase_   : List[Any]       =						upsample_block(A     ,				A     ,				A     ,				A )
													else:
																		lowerCamelCase_   : List[str]       =						upsample_block(A     ,				A     ,				A )

        # 6. post-process
								lowerCamelCase_   : Optional[int]       =						self.conv_norm_out(A )
								lowerCamelCase_   : str       =						self.conv_act(A )
								lowerCamelCase_   : str       =						self.conv_out(A )

								if skip_sample is not None:
													sample += skip_sample

								if self.config.time_embedding_type == "fourier":
													lowerCamelCase_   : Any       =						timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:] ))) )
													lowerCamelCase_   : Any       =						sample / timesteps

								if not return_dict:
													return (sample,)

								return UNetaDOutput(sample=A )




'''simple docstring'''


import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__lowercase							:				Optional[Any]					=       logging.getLogger()




def lowercase_		(				)       ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_   : Any       =						argparse.ArgumentParser()
					parser.add_argument('''-f'''			)
					lowerCamelCase_   : Optional[int]       =						parser.parse_args()
					return args.f






class 		__lowercase			(					_lowercase      ):

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Dict       =						logging.StreamHandler(sys.stdout )
								logger.addHandler(A )

								lowerCamelCase_   : int       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : Dict       =						F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

								with patch.object(A     ,				'''argv'''     ,				A ):
													lowerCamelCase_   : Optional[Any]       =						run_glue_with_pabee.main()
													for value in result.values():
																		self.assertGreaterEqual(A     ,				0.75 )


