


"""simple docstring"""






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

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin


@dataclass
class 			__lowerCamelCase     (    a__ ):

     '''simple docstring'''
     A_				:		torch.FloatTensor







class 			__lowerCamelCase     (    a__     ,    a__ ):

     '''simple docstring'''


     @register_to_config
     def __init__(					self			, __UpperCAmelCase = 32			, __UpperCAmelCase = 64			, __UpperCAmelCase = 20			, __UpperCAmelCase = 768			, __UpperCAmelCase=77			, __UpperCAmelCase=4			, __UpperCAmelCase = 0.0			, __UpperCAmelCase = "silu"			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = "linear"			, __UpperCAmelCase = "prd"			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, )				->							Tuple:
          super().__init__()
          _a					= num_attention_heads
          _a					= attention_head_dim
          _a					= num_attention_heads * attention_head_dim
          _a					= additional_embeddings

          _a					= time_embed_dim or inner_dim
          _a					= embedding_proj_dim or embedding_dim
          _a					= clip_embed_dim or embedding_dim

          _a					= Timesteps(SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_			, 0							)
          _a					= TimestepEmbedding(SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_			, out_dim=SCREAMING_SNAKE_CASE_			, act_fn=SCREAMING_SNAKE_CASE_							)

          _a					= nn.Linear(SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_							)

          if embedding_proj_norm_type is None:
               _a					= None
          elif embedding_proj_norm_type == "layer":
               _a					= nn.LayerNorm(SCREAMING_SNAKE_CASE_							)
          else:
               raise ValueError(F'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'							)

          _a					= nn.Linear(SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_							)

          if encoder_hid_proj_type is None:
               _a					= None
          elif encoder_hid_proj_type == "linear":
               _a					= nn.Linear(SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_							)
          else:
               raise ValueError(F'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'							)

          _a					= nn.Parameter(torch.zeros(1			, num_embeddings + additional_embeddings			, SCREAMING_SNAKE_CASE_							)							)

          if added_emb_type == "prd":
               _a					= nn.Parameter(torch.zeros(1			, 1			, SCREAMING_SNAKE_CASE_							)							)
          elif added_emb_type is None:
               _a					= None
          else:
               raise ValueError(
                   F'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'							)

          _a					= nn.ModuleList(
              [
                  BasicTransformerBlock(
                      SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_			, dropout=SCREAMING_SNAKE_CASE_			, activation_fn='''gelu'''			, attention_bias=SCREAMING_SNAKE_CASE_			, )
                  for d in range(SCREAMING_SNAKE_CASE_							)
              ]							)

          if norm_in_type == "layer":
               _a					= nn.LayerNorm(SCREAMING_SNAKE_CASE_							)
          elif norm_in_type is None:
               _a					= None
          else:
               raise ValueError(F'Unsupported norm_in_type: {norm_in_type}.'							)

          _a					= nn.LayerNorm(SCREAMING_SNAKE_CASE_							)

          _a					= nn.Linear(SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_							)

          _a					= torch.full(
              [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]			, -10000.0							)
          causal_attention_mask.triu_(1							)
          _a					= causal_attention_mask[None, ...]
          self.register_buffer('''causal_attention_mask'''			, SCREAMING_SNAKE_CASE_			, persistent=SCREAMING_SNAKE_CASE_							)

          _a					= nn.Parameter(torch.zeros(1			, SCREAMING_SNAKE_CASE_							)							)
          _a					= nn.Parameter(torch.zeros(1			, SCREAMING_SNAKE_CASE_							)							)


     @property
     # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
     def 							_UpperCAmelCase					(					self							)				->							Dict[str, AttentionProcessor]:
          _a					= {}

          def fn_recursive_add_processors(__UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							):
               if hasattr(SCREAMING_SNAKE_CASE_			, '''set_processor'''							):
                    _a					= module.processor

               for sub_name, child in module.named_children():
                    fn_recursive_add_processors(F'{name}.{sub_name}'			, SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_							)

               return processors

          for name, module in self.named_children():
               fn_recursive_add_processors(SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_							)

          return processors


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							int:
          _a					= len(self.attn_processors.keys()							)

          if isinstance(SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_							) and len(SCREAMING_SNAKE_CASE_							) != count:
               raise ValueError(
                   F'A dict of processors was passed, but the number of processors {len(SCREAMING_SNAKE_CASE_							)} does not match the'
                   F' number of attention layers: {count}. Please make sure to pass {count} processor classes.'							)

          def fn_recursive_attn_processor(__UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							):
               if hasattr(SCREAMING_SNAKE_CASE_			, '''set_processor'''							):
                    if not isinstance(SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_							):
                         module.set_processor(SCREAMING_SNAKE_CASE_							)
                    else:
                         module.set_processor(processor.pop(F'{name}.processor'							)							)

               for sub_name, child in module.named_children():
                    fn_recursive_attn_processor(F'{name}.{sub_name}'			, SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_							)

          for name, module in self.named_children():
               fn_recursive_attn_processor(SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_							)


     def 							_UpperCAmelCase					(					self							)				->							Optional[int]:
          self.set_attn_processor(AttnProcessor()							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = True			, )				->							List[Any]:
          _a					= hidden_states.shape[0]

          _a					= timestep
          if not torch.is_tensor(SCREAMING_SNAKE_CASE_							):
               _a					= torch.tensor([timesteps]			, dtype=torch.long			, device=hidden_states.device							)
          elif torch.is_tensor(SCREAMING_SNAKE_CASE_							) and len(timesteps.shape							) == 0:
               _a					= timesteps[None].to(hidden_states.device							)

          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          _a					= timesteps * torch.ones(SCREAMING_SNAKE_CASE_			, dtype=timesteps.dtype			, device=timesteps.device							)

          _a					= self.time_proj(SCREAMING_SNAKE_CASE_							)

          # timesteps does not contain any weights and will always return f32 tensors
          # but time_embedding might be fp16, so we need to cast here.
          _a					= timesteps_projected.to(dtype=self.dtype							)
          _a					= self.time_embedding(SCREAMING_SNAKE_CASE_							)

          if self.embedding_proj_norm is not None:
               _a					= self.embedding_proj_norm(SCREAMING_SNAKE_CASE_							)

          _a					= self.embedding_proj(SCREAMING_SNAKE_CASE_							)
          if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
               _a					= self.encoder_hidden_states_proj(SCREAMING_SNAKE_CASE_							)
          elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
               raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'''							)

          _a					= self.proj_in(SCREAMING_SNAKE_CASE_							)

          _a					= self.positional_embedding.to(hidden_states.dtype							)

          _a					= []
          _a					= 0

          if encoder_hidden_states is not None:
               additional_embeds.append(SCREAMING_SNAKE_CASE_							)
               additional_embeddings_len += encoder_hidden_states.shape[1]

          if len(proj_embeddings.shape							) == 2:
               _a					= proj_embeddings[:, None, :]

          if len(hidden_states.shape							) == 2:
               _a					= hidden_states[:, None, :]

          _a					= additional_embeds + [
              proj_embeddings,
              time_embeddings[:, None, :],
              hidden_states,
          ]

          if self.prd_embedding is not None:
               _a					= self.prd_embedding.to(hidden_states.dtype							).expand(SCREAMING_SNAKE_CASE_			, -1			, -1							)
               additional_embeds.append(SCREAMING_SNAKE_CASE_							)

          _a					= torch.cat(
              SCREAMING_SNAKE_CASE_			, dim=1			, )

          # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
          _a					= additional_embeddings_len + proj_embeddings.shape[1] + 1
          if positional_embeddings.shape[1] < hidden_states.shape[1]:
               _a					= F.pad(
                   SCREAMING_SNAKE_CASE_			, (
                       0,
                       0,
                       additional_embeddings_len,
                       self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
                   )			, value=0.0			, )

          _a					= hidden_states + positional_embeddings

          if attention_mask is not None:
               _a					= (1 - attention_mask.to(hidden_states.dtype							)) * -10000.0
               _a					= F.pad(SCREAMING_SNAKE_CASE_			, (0, self.additional_embeddings)			, value=0.0							)
               _a					= (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype							)
               _a					= attention_mask.repeat_interleave(self.config.num_attention_heads			, dim=0							)

          if self.norm_in is not None:
               _a					= self.norm_in(SCREAMING_SNAKE_CASE_							)

          for block in self.transformer_blocks:
               _a					= block(SCREAMING_SNAKE_CASE_			, attention_mask=SCREAMING_SNAKE_CASE_							)

          _a					= self.norm_out(SCREAMING_SNAKE_CASE_							)

          if self.prd_embedding is not None:
               _a					= hidden_states[:, -1]
          else:
               _a					= hidden_states[:, additional_embeddings_len:]

          _a					= self.proj_to_clip_embeddings(SCREAMING_SNAKE_CASE_							)

          if not return_dict:
               return (predicted_image_embedding,)

          return PriorTransformerOutput(predicted_image_embedding=SCREAMING_SNAKE_CASE_							)





     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							Dict:
          _a					= (prior_latents * self.clip_std) + self.clip_mean
          return prior_latents

import argparse
import os
import re


__a  			=					'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a  			=					re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__a  			=					re.compile(r'\s*\(\s*"(\S[^"]+)"')




def 				a						(							snake_case__:					str						,   snake_case__:					bool = False					):


				'''simple docstring'''
				with open(snake_case__						,   '''r'''						,   encoding='''utf-8'''					) as f:
								lowercase_       =					f.read()

				lowercase_       =					content.split('''\n'''					)
				lowercase_       =					[]
				lowercase_       =					0
				while line_idx < len(snake_case__					):
								if _re_intro_mapping.search(lines[line_idx]					) is not None:
												lowercase_       =					len(re.search(r'''^(\s*)\S'''						,   lines[line_idx]					).groups()[0]					) + 8
												# Start of a new mapping!
												while not lines[line_idx].startswith(''' ''' * indent + '''('''					):
																new_lines.append(lines[line_idx]					)
																line_idx += 1

												lowercase_       =					[]
												while lines[line_idx].strip() != "]":
																# Blocks either fit in one line or not
																if lines[line_idx].strip() == "(":
																				lowercase_       =					line_idx
																				while not lines[line_idx].startswith(''' ''' * indent + ''')'''					):
																								line_idx += 1
																				blocks.append('''\n'''.join(lines[start_idx : line_idx + 1]					)					)
																else:
																				blocks.append(lines[line_idx]					)
																line_idx += 1

												# Sort blocks by their identifiers
												lowercase_       =					sorted(snake_case__						,   key=lambda snake_case__					: _re_identifier.search(snake_case__					).groups()[0]					)
												new_lines += blocks
								else:
												new_lines.append(lines[line_idx]					)
												line_idx += 1

				if overwrite:
								with open(snake_case__						,   '''w'''						,   encoding='''utf-8'''					) as f:
												f.write('''\n'''.join(snake_case__					)					)
				elif "\n".join(snake_case__					) != content:
								return True




def 				a						(							snake_case__:					bool = False					):


				'''simple docstring'''
				lowercase_       =					[os.path.join(snake_case__						,   snake_case__					) for f in os.listdir(snake_case__					) if f.endswith('''.py'''					)]
				lowercase_       =					[sort_auto_mapping(snake_case__						,   overwrite=snake_case__					) for fname in fnames]

				if not overwrite and any(snake_case__					):
								lowercase_       =					[f for f, d in zip(snake_case__						,   snake_case__					) if d]
								raise ValueError(
								    F'''The following files have auto mappings that need sorting: {', '.join(snake_case__					)}. Run `make style` to fix'''
								    ''' this.'''					)


if __name__ == "__main__":
						__a  			=					argparse.ArgumentParser()
						parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
						__a  			=					parser.parse_args()

						sort_all_auto_mappings(not args.check_only)




