# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


snake_case__				= get_logger(__name__)



class  UpperCamelCase				:

     '''simple docstring'''

     A_										=  'dummy_data'
     A_										=  'datasets'
     A_										=  False



     def __init__(					self				,				A_				,				A_				,				A_				,				A_ = None				,				A_ = False				,				A_ = True				,				A_ = None				,				) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				0
           _lowerCamelCase			    =				dataset_name
           _lowerCamelCase			    =				cache_dir
           _lowerCamelCase			    =				use_local_dummy_data
           _lowerCamelCase			    =				config
           # download_callbacks take a single url as input
           _lowerCamelCase			    =				download_callbacks or []
           # if False, it doesn't load existing files and it returns the paths of the dummy files relative
           # to the dummy_data zip file root
           _lowerCamelCase			    =				load_existing_dummy_data

           # TODO(PVP, QL) might need to make this more general
           _lowerCamelCase			    =				str(A_						)
           # to be downloaded
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				None



     @property
     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           if self._dummy_file is None:
                 _lowerCamelCase			    =				self.download_dummy_data()
           return self._dummy_file



     @property
     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           if self.config is not None:
                 # structure is dummy / config_name / version_name
                 return os.path.join('''dummy'''				,				self.config.name				,				self.version_name						)
           # structure is dummy / version_name
           return os.path.join('''dummy'''				,				self.version_name						)



     @property
     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           return os.path.join(self.dummy_data_folder				,				'''dummy_data.zip'''						)



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				(
               self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
           )

           _lowerCamelCase			    =				cached_path(
               A_				,				cache_dir=self.cache_dir				,				extract_compressed_file=A_				,				force_extract=A_						)

           return os.path.join(A_				,				self.dummy_file_name						)



     @property
     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           return os.path.join(self.datasets_scripts_dir				,				self.dataset_name				,				self.dummy_zip_file						)



     @property
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           if self._bucket_url is None:
                 _lowerCamelCase			    =				hf_github_url(self.dataset_name				,				self.dummy_zip_file.replace(os.sep				,				'''/'''						)						)
           return self._bucket_url



     @property
     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           # return full path if its a dir
           if os.path.isdir(self.dummy_file						):
                 return self.dummy_file
           # else cut off path to file -> example `xsum`.
           return "/".join(self.dummy_file.replace(os.sep				,				'''/'''						).split('''/'''						)[:-1]						)



     def 		UpperCamelCase_						(					self				,				A_				,				*A_						) ->				List[Any]:






           """simple docstring"""
           if self.load_existing_dummy_data:
                 # dummy data is downloaded and tested
                 _lowerCamelCase			    =				self.dummy_file
           else:
                 # dummy data cannot be downloaded and only the path to dummy file is returned
                 _lowerCamelCase			    =				self.dummy_file_name

           # special case when data_url is a dict
           if isinstance(A_				,				A_						):
                 return self.create_dummy_data_dict(A_				,				A_						)
           elif isinstance(A_				,				(list, tuple)						):
                 return self.create_dummy_data_list(A_				,				A_						)
           else:
                 return self.create_dummy_data_single(A_				,				A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				*A_						) ->				List[Any]:






           """simple docstring"""
           return self.download_and_extract(A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				Union[str, Any]:






           """simple docstring"""
           return self.download_and_extract(A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				*A_				,				**A_						) ->				Union[str, Any]:






           """simple docstring"""
           return path



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           return {}



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				{}
           for key, single_urls in data_url.items():
                 for download_callback in self.download_callbacks:
                       if isinstance(A_				,				A_						):
                             for single_url in single_urls:
                                   download_callback(A_						)
                       else:
                             _lowerCamelCase			    =				single_urls
                             download_callback(A_						)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
                 if isinstance(A_				,				A_						):
                       _lowerCamelCase			    =				[os.path.join(A_				,				urllib.parse.quote_plus(Path(A_						).name						)						) for x in single_urls]
                 else:
                       _lowerCamelCase			    =				single_urls
                       _lowerCamelCase			    =				os.path.join(A_				,				urllib.parse.quote_plus(Path(A_						).name						)						)
                 _lowerCamelCase			    =				value

           # make sure that values are unique
           if all(isinstance(A_				,				A_						) for i in dummy_data_dict.values()						) and len(set(dummy_data_dict.values()						)						) < len(
               dummy_data_dict.values()						):
                 # append key to value to make its name unique
                 _lowerCamelCase			    =				{key: value + key for key, value in dummy_data_dict.items()}

           return dummy_data_dict



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				[]
           # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
           _lowerCamelCase			    =				all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}'''				,				A_						)						) for url in data_url						)
           _lowerCamelCase			    =				all(
               url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'''						) for url in data_url						)
           if data_url and (is_tf_records or is_pubmed_records):
                 _lowerCamelCase			    =				[data_url[0]] * len(A_						)
           for single_url in data_url:
                 for download_callback in self.download_callbacks:
                       download_callback(A_						)
                 # we force the name of each key to be the last file / folder name of the url path
                 # if the url has arguments, we need to encode them with urllib.parse.quote_plus
                 _lowerCamelCase			    =				os.path.join(A_				,				urllib.parse.quote_plus(single_url.split('''/'''						)[-1]						)						)
                 dummy_data_list.append(A_						)
           return dummy_data_list



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				Any:






           """simple docstring"""
           for download_callback in self.download_callbacks:
                 download_callback(A_						)
           # we force the name of each key to be the last file / folder name of the url path
           # if the url has arguments, we need to encode them with urllib.parse.quote_plus
           _lowerCamelCase			    =				os.path.join(A_				,				urllib.parse.quote_plus(data_url.split('''/'''						)[-1]						)						)
           if os.path.exists(A_						) or not self.load_existing_dummy_data:
                 return value
           else:
                 # Backward compatibility, maybe deprecate at one point.
                 # For many datasets with single url calls to dl_manager.download_and_extract,
                 # the dummy_data.zip file is actually the zipped downloaded file
                 # while now we expected the dummy_data.zip file to be a directory containing
                 # the downloaded file.
                 return path_to_dummy_data



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           pass



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           pass



     def 		UpperCamelCase_						(					self				,				A_						) ->				List[Any]:






           """simple docstring"""
           def _iter_archive_members(A_						):
                 # this preserves the order of the members inside the ZIP archive
                 _lowerCamelCase			    =				Path(self.dummy_file						).parent
                 _lowerCamelCase			    =				path.relative_to(A_						)
                 with ZipFile(self.local_path_to_dummy_data						) as zip_file:
                       _lowerCamelCase			    =				zip_file.namelist()
                 for member in members:
                       if member.startswith(relative_path.as_posix()						):
                             yield dummy_parent_path.joinpath(A_						)

           _lowerCamelCase			    =				Path(A_						)
           _lowerCamelCase			    =				_iter_archive_members(A_						) if self.use_local_dummy_data else path.rglob('''*'''						)
           for file_path in file_paths:
                 if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')						):
                       yield file_path.relative_to(A_						).as_posix(), file_path.open('''rb'''						)





     def 		UpperCamelCase_						(					self				,				A_						) ->				Tuple:






           """simple docstring"""
           if not isinstance(A_				,				A_						):
                 _lowerCamelCase			    =				[paths]
           for path in paths:
                 if os.path.isfile(A_						):
                       if os.path.basename(A_						).startswith(('''.''', '''__''')						):
                             return
                       yield path
                 else:
                       for dirpath, dirnames, filenames in os.walk(A_						):
                             if os.path.basename(A_						).startswith(('''.''', '''__''')						):
                                   continue
                             dirnames.sort()
                             for filename in sorted(A_						):
                                   if filename.startswith(('''.''', '''__''')						):
                                         continue
                                   yield os.path.join(A_				,				A_						)
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 get_down_block, get_mid_block, get_out_block, get_up_block



@dataclass
class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  42





class  UpperCamelCase				( __lowercase   ,  __lowercase ):

     '''simple docstring'''

     @register_to_config
     def __init__(					self				,				A_ = 6_55_36				,				A_ = None				,				A_ = 2				,				A_ = 2				,				A_ = 0				,				A_ = "fourier"				,				A_ = True				,				A_ = False				,				A_ = 0.0				,				A_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")				,				A_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")				,				A_ = "UNetMidBlock1D"				,				A_ = None				,				A_ = (32, 32, 64)				,				A_ = None				,				A_ = 8				,				A_ = 1				,				A_ = False				,				) ->				Dict:






           """simple docstring"""
           super().__init__()
           _lowerCamelCase			    =				sample_size

           # time
           if time_embedding_type == "fourier":
                 _lowerCamelCase			    =				GaussianFourierProjection(
                     embedding_size=8				,				set_W_to_weight=A_				,				log=A_				,				flip_sin_to_cos=A_						)
                 _lowerCamelCase			    =				2 * block_out_channels[0]
           elif time_embedding_type == "positional":
                 _lowerCamelCase			    =				Timesteps(
                     block_out_channels[0]				,				flip_sin_to_cos=A_				,				downscale_freq_shift=A_						)
                 _lowerCamelCase			    =				block_out_channels[0]

           if use_timestep_embedding:
                 _lowerCamelCase			    =				block_out_channels[0] * 4
                 _lowerCamelCase			    =				TimestepEmbedding(
                     in_channels=A_				,				time_embed_dim=A_				,				act_fn=A_				,				out_dim=block_out_channels[0]				,				)

           _lowerCamelCase			    =				nn.ModuleList([]						)
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				nn.ModuleList([]						)
           _lowerCamelCase			    =				None

           # down
           _lowerCamelCase			    =				in_channels
           for i, down_block_type in enumerate(A_						):
                 _lowerCamelCase			    =				output_channel
                 _lowerCamelCase			    =				block_out_channels[i]

                 if i == 0:
                       input_channel += extra_in_channels

                 _lowerCamelCase			    =				i == len(A_						) - 1

                 _lowerCamelCase			    =				get_down_block(
                     A_				,				num_layers=A_				,				in_channels=A_				,				out_channels=A_				,				temb_channels=block_out_channels[0]				,				add_downsample=not is_final_block or downsample_each_block				,				)
                 self.down_blocks.append(A_						)

           # mid
           _lowerCamelCase			    =				get_mid_block(
               A_				,				in_channels=block_out_channels[-1]				,				mid_channels=block_out_channels[-1]				,				out_channels=block_out_channels[-1]				,				embed_dim=block_out_channels[0]				,				num_layers=A_				,				add_downsample=A_				,				)

           # up
           _lowerCamelCase			    =				list(reversed(A_						)						)
           _lowerCamelCase			    =				reversed_block_out_channels[0]
           if out_block_type is None:
                 _lowerCamelCase			    =				out_channels
           else:
                 _lowerCamelCase			    =				block_out_channels[0]

           for i, up_block_type in enumerate(A_						):
                 _lowerCamelCase			    =				output_channel
                 _lowerCamelCase			    =				(
                     reversed_block_out_channels[i + 1] if i < len(A_						) - 1 else final_upsample_channels
                 )

                 _lowerCamelCase			    =				i == len(A_						) - 1

                 _lowerCamelCase			    =				get_up_block(
                     A_				,				num_layers=A_				,				in_channels=A_				,				out_channels=A_				,				temb_channels=block_out_channels[0]				,				add_upsample=not is_final_block				,				)
                 self.up_blocks.append(A_						)
                 _lowerCamelCase			    =				output_channel

           # out
           _lowerCamelCase			    =				norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4				,				32						)
           _lowerCamelCase			    =				get_out_block(
               out_block_type=A_				,				num_groups_out=A_				,				embed_dim=block_out_channels[0]				,				out_channels=A_				,				act_fn=A_				,				fc_dim=block_out_channels[-1] // 4				,				)





     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_ = True				,				) ->				Union[UNetaDOutput, Tuple]:






           """simple docstring"""
           _lowerCamelCase			    =				timestep
           if not torch.is_tensor(A_						):
                 _lowerCamelCase			    =				torch.tensor([timesteps]				,				dtype=torch.long				,				device=sample.device						)
           elif torch.is_tensor(A_						) and len(timesteps.shape						) == 0:
                 _lowerCamelCase			    =				timesteps[None].to(sample.device						)

           _lowerCamelCase			    =				self.time_proj(A_						)
           if self.config.use_timestep_embedding:
                 _lowerCamelCase			    =				self.time_mlp(A_						)
           else:
                 _lowerCamelCase			    =				timestep_embed[..., None]
                 _lowerCamelCase			    =				timestep_embed.repeat([1, 1, sample.shape[2]]						).to(sample.dtype						)
                 _lowerCamelCase			    =				timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])						)

           # 2. down
           _lowerCamelCase			    =				()
           for downsample_block in self.down_blocks:
                 _lowerCamelCase    ,					_lowerCamelCase			    =				downsample_block(hidden_states=A_				,				temb=A_						)
                 down_block_res_samples += res_samples

           # 3. mid
           if self.mid_block:
                 _lowerCamelCase			    =				self.mid_block(A_				,				A_						)

           # 4. up
           for i, upsample_block in enumerate(self.up_blocks						):
                 _lowerCamelCase			    =				down_block_res_samples[-1:]
                 _lowerCamelCase			    =				down_block_res_samples[:-1]
                 _lowerCamelCase			    =				upsample_block(A_				,				res_hidden_states_tuple=A_				,				temb=A_						)

           # 5. post-process
           if self.out_block:
                 _lowerCamelCase			    =				self.out_block(A_				,				A_						)

           if not return_dict:
                 return (sample,)

           return UNetaDOutput(sample=A_						)