


import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


A  :     List[Any]    					=    datasets.utils.logging.get_logger(__name__)
@dataclass
class 						_UpperCamelCase						(     datasets.BuilderConfig	):
							'''simple docstring'''







							__UpperCAmelCase		:					int          =1_0_0_0_0
							__UpperCAmelCase		:					Optional[List[str]]          =None
							__UpperCAmelCase		:					Optional[datasets.Features]          =None
class 						_UpperCamelCase						(     datasets.ArrowBasedBuilder	):
							'''simple docstring'''







							__UpperCAmelCase		:					Optional[Any]          =ParquetConfig

							def     snake_case       (  self			):
										return datasets.DatasetInfo(features=self.config.features			)

							def     snake_case       (  self					,     __a			):
										if not self.config.data_files:
													raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"			)
										__lowerCAmelCase       					=    dl_manager.download_and_extract(self.config.data_files			)
										if isinstance(__a					,     (str, list, tuple)			):
													__lowerCAmelCase       					=    data_files
													if isinstance(__a					,     __a			):
																__lowerCAmelCase       					=    [files]
													# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
													__lowerCAmelCase       					=    [dl_manager.iter_files(__a			) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,     gen_kwargs={"files": files}			)]
										__lowerCAmelCase       					=    []
										for split_name, files in data_files.items():
													if isinstance(__a					,     __a			):
																__lowerCAmelCase       					=    [files]
													# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
													__lowerCAmelCase       					=    [dl_manager.iter_files(__a			) for file in files]
													# Infer features is they are stoed in the arrow schema
													if self.info.features is None:
																for file in itertools.chain.from_iterable(__a			):
																			with open(__a					,     "rb"			) as f:
																						__lowerCAmelCase       					=    datasets.Features.from_arrow_schema(pq.read_schema(__a			)			)
																			break
													splits.append(datasets.SplitGenerator(name=__a					,     gen_kwargs={"files": files}			)			)
										return splits

							def     snake_case       (  self					,     __a			):
										if self.info.features is not None:
													# more expensive cast to support nested features with keys in a different order
													# allows str <-> int/float or str to Audio for example
													__lowerCAmelCase       					=    table_cast(__a					,     self.info.features.arrow_schema			)
										return pa_table



							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    self.info.features.arrow_schema if self.info.features is not None else None
										if self.info.features is not None and self.config.columns is not None:
													if sorted(field.name for field in schema			) != sorted(self.config.columns			):
																raise ValueError(
																    f"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"			)
										for file_idx, file in enumerate(itertools.chain.from_iterable(__a			)			):
													with open(__a					,     "rb"			) as f:
																__lowerCAmelCase       					=    pq.ParquetFile(__a			)
																try:
																			for batch_idx, record_batch in enumerate(
																			    parquet_file.iter_batches(batch_size=self.config.batch_size					,     columns=self.config.columns			)			):
																						__lowerCAmelCase       					=    pa.Table.from_batches([record_batch]			)
																						# Uncomment for debugging (will print the Arrow table size and elements)
																						# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																						# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																						yield f"{file_idx}_{batch_idx}", self._cast_table(__a			)
																except ValueError as e:
																			logger.error(f"Failed to read file '{file}' with error {type(__a			)}: {e}"			)
																			raise








"""simple docstring"""


import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A  :     Dict    					=    logging.get_logger(__name__)  # pylint: disable=invalid-name
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=7_68			):
										super().__init__(__a			)
										__lowerCAmelCase       					=    proj_size

										__lowerCAmelCase       					=    CLIPVisionModel(__a			)
										__lowerCAmelCase       					=    PaintByExampleMapper(__a			)
										__lowerCAmelCase       					=    nn.LayerNorm(config.hidden_size			)
										__lowerCAmelCase       					=    nn.Linear(config.hidden_size					,     self.proj_size			)

										# uncondition for scaling
										__lowerCAmelCase       					=    nn.Parameter(torch.randn((1, 1, self.proj_size)			)			)



							def     snake_case       (  self					,     __a					,     __a=False			):
										__lowerCAmelCase       					=    self.model(pixel_values=__a			)
										__lowerCAmelCase       					=    clip_output.pooler_output
										__lowerCAmelCase       					=    self.mapper(latent_states[:, None]			)
										__lowerCAmelCase       					=    self.final_layer_norm(__a			)
										__lowerCAmelCase       					=    self.proj_out(__a			)
										if return_uncond_vector:
													return latent_states, self.uncond_vector

										return latent_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							def __init__(  self					,     __a			):
										super().__init__()
										__lowerCAmelCase       					=    (config.num_hidden_layers + 1) // 5
										__lowerCAmelCase       					=    config.hidden_size
										__lowerCAmelCase       					=    1
										__lowerCAmelCase       					=    nn.ModuleList(
										    [
										        BasicTransformerBlock(__a					,     __a					,     __a					,     activation_fn="gelu"					,     attention_bias=__a			)
										        for _ in range(__a			)
										    ]			)



							def     snake_case       (  self					,     __a			):
										for block in self.blocks:
													__lowerCAmelCase       					=    block(__a			)

										return hidden_states




