


import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


A							:					Union[str, Any]     						=     datasets.utils.logging.get_logger(__name__)

A							:					Tuple     						=     ['names', 'prefix']
A							:					Optional[Any]     						=     ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
A							:					Optional[Any]     						=     ['encoding_errors', 'on_bad_lines']
A							:					int     						=     ['date_format']






@dataclass
class     UpperCamelCase(  datasets.BuilderConfig   ):
					snake_case_		:				str										=	","
					snake_case_		:				Optional[str]										=	None
					snake_case_		:				Optional[Union[int, List[int], str]]										=	"infer"
					snake_case_		:				Optional[List[str]]										=	None
					snake_case_		:				Optional[List[str]]										=	None
					snake_case_		:				Optional[Union[int, str, List[int], List[str]]]										=	None
					snake_case_		:				Optional[Union[List[int], List[str]]]										=	None
					snake_case_		:				Optional[str]										=	None
					snake_case_		:				bool										=	True
					snake_case_		:				Optional[Literal["c", "python", "pyarrow"]]										=	None
					snake_case_		:				Dict[Union[int, str], Callable[[Any], Any]]										=	None
					snake_case_		:				Optional[list]										=	None
					snake_case_		:				Optional[list]										=	None
					snake_case_		:				bool										=	False
					snake_case_		:				Optional[Union[int, List[int]]]										=	None
					snake_case_		:				Optional[int]										=	None
					snake_case_		:				Optional[Union[str, List[str]]]										=	None
					snake_case_		:				bool										=	True
					snake_case_		:				bool										=	True
					snake_case_		:				bool										=	False
					snake_case_		:				bool										=	True
					snake_case_		:				Optional[str]										=	None
					snake_case_		:				str										=	"."
					snake_case_		:				Optional[str]										=	None
					snake_case_		:				str										=	'"'
					snake_case_		:				int										=	0
					snake_case_		:				Optional[str]										=	None
					snake_case_		:				Optional[str]										=	None
					snake_case_		:				Optional[str]										=	None
					snake_case_		:				Optional[str]										=	None
					snake_case_		:				bool										=	True
					snake_case_		:				bool										=	True
					snake_case_		:				int										=	0
					snake_case_		:				bool										=	True
					snake_case_		:				bool										=	False
					snake_case_		:				Optional[str]										=	None
					snake_case_		:				int										=	1_0000
					snake_case_		:				Optional[datasets.Features]										=	None
					snake_case_		:				Optional[str]										=	"strict"
					snake_case_		:				Literal["error", "warn", "skip"]										=	"error"
					snake_case_		:				Optional[str]										=	None


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	List[Any]:




											'''simple docstring'''

											if self.delimiter is not None:
																	__snake_case				=		self.delimiter
											if self.column_names is not None:
																	__snake_case				=		self.column_names






					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	int:




											'''simple docstring'''

											__snake_case				=		{
											    "sep": self.sep,
											    "header": self.header,
											    "names": self.names,
											    "index_col": self.index_col,
											    "usecols": self.usecols,
											    "prefix": self.prefix,
											    "mangle_dupe_cols": self.mangle_dupe_cols,
											    "engine": self.engine,
											    "converters": self.converters,
											    "true_values": self.true_values,
											    "false_values": self.false_values,
											    "skipinitialspace": self.skipinitialspace,
											    "skiprows": self.skiprows,
											    "nrows": self.nrows,
											    "na_values": self.na_values,
											    "keep_default_na": self.keep_default_na,
											    "na_filter": self.na_filter,
											    "verbose": self.verbose,
											    "skip_blank_lines": self.skip_blank_lines,
											    "thousands": self.thousands,
											    "decimal": self.decimal,
											    "lineterminator": self.lineterminator,
											    "quotechar": self.quotechar,
											    "quoting": self.quoting,
											    "escapechar": self.escapechar,
											    "comment": self.comment,
											    "encoding": self.encoding,
											    "dialect": self.dialect,
											    "error_bad_lines": self.error_bad_lines,
											    "warn_bad_lines": self.warn_bad_lines,
											    "skipfooter": self.skipfooter,
											    "doublequote": self.doublequote,
											    "memory_map": self.memory_map,
											    "float_precision": self.float_precision,
											    "chunksize": self.chunksize,
											    "encoding_errors": self.encoding_errors,
											    "on_bad_lines": self.on_bad_lines,
											    "date_format": self.date_format,
											}

											# some kwargs must not be passed if they don't have a default value
											# some others are deprecated and we can also not pass them if they are the default value
											for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
																	if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()     ,    SCREAMING_SNAKE_CASE						):
																							del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
											if not (datasets.config.PANDAS_VERSION.major >= 2):
																	for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
																							del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
											if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
																	for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
																							del pd_read_csv_kwargs[pd_read_csv_parameter]

											return pd_read_csv_kwargs






class     UpperCamelCase(  datasets.ArrowBasedBuilder   ):
					snake_case_		:				Optional[Any]										=	CsvConfig


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




											'''simple docstring'''

											return datasets.DatasetInfo(features=self.config.features						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		List[Any]						)			->	List[Any]:




											'''simple docstring'''

											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}'''						)
											__snake_case				=		dl_manager.download_and_extract(self.config.data_files						)
											if isinstance(SCREAMING_SNAKE_CASE     ,    (str, list, tuple)						):
																	__snake_case				=		data_files
																	if isinstance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						):
																							__snake_case				=		[files]
																	__snake_case				=		[dl_manager.iter_files(SCREAMING_SNAKE_CASE						) for file in files]
																	return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,    gen_kwargs={"files": files}						)]
											__snake_case				=		[]
											for split_name, files in data_files.items():
																	if isinstance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						):
																							__snake_case				=		[files]
																	__snake_case				=		[dl_manager.iter_files(SCREAMING_SNAKE_CASE						) for file in files]
																	splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE     ,    gen_kwargs={"files": files}						)						)
											return splits


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		pa.Table						)			->	pa.Table:




											'''simple docstring'''

											if self.config.features is not None:
																	__snake_case				=		self.config.features.arrow_schema
																	if all(not require_storage_cast(SCREAMING_SNAKE_CASE						) for feature in self.config.features.values()						):
																							# cheaper cast
																							__snake_case				=		pa.Table.from_arrays([pa_table[field.name] for field in schema]     ,    schema=SCREAMING_SNAKE_CASE						)
																	else:
																							# more expensive cast; allows str <-> int/float or str to Audio for example
																							__snake_case				=		table_cast(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
											return pa_table






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	int:




											'''simple docstring'''

											__snake_case				=		self.config.features.arrow_schema if self.config.features else None
											# dtype allows reading an int column as str
											__snake_case				=		(
											    {
											        name: dtype.to_pandas_dtype() if not require_storage_cast(SCREAMING_SNAKE_CASE						) else object
											        for name, dtype, feature in zip(schema.names     ,    schema.types     ,    self.config.features.values()						)
											    }
											    if schema is not None
											    else None
											)
											for file_idx, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE						)						):
																	__snake_case				=		pd.read_csv(SCREAMING_SNAKE_CASE     ,    iterator=SCREAMING_SNAKE_CASE     ,    dtype=SCREAMING_SNAKE_CASE     ,    **self.config.pd_read_csv_kwargs						)
																	try:
																							for batch_idx, df in enumerate(SCREAMING_SNAKE_CASE						):
																													__snake_case				=		pa.Table.from_pandas(SCREAMING_SNAKE_CASE						)
																													# 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 (file_idx, batch_idx), self._cast_table(SCREAMING_SNAKE_CASE						)
																	except ValueError as e:
																							logger.error(f'''Failed to read file \'{file}\' with error {type(SCREAMING_SNAKE_CASE						)}: {e}'''						)
																							raise









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

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


A							:					int     						=     logging.get_logger(__name__)  # pylint: disable=invalid-name






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict=7_6_8						)			->	int:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE						)
											__snake_case				=		proj_size

											__snake_case				=		CLIPVisionModel(SCREAMING_SNAKE_CASE						)
											__snake_case				=		PaintByExampleMapper(SCREAMING_SNAKE_CASE						)
											__snake_case				=		nn.LayerNorm(config.hidden_size						)
											__snake_case				=		nn.Linear(config.hidden_size     ,    self.proj_size						)

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=False						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.model(pixel_values=SCREAMING_SNAKE_CASE						)
											__snake_case				=		clip_output.pooler_output
											__snake_case				=		self.mapper(latent_states[:, None]						)
											__snake_case				=		self.final_layer_norm(SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.proj_out(SCREAMING_SNAKE_CASE						)
											if return_uncond_vector:
																	return latent_states, self.uncond_vector

											return latent_states






class     UpperCamelCase(  nn.Module   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	List[Any]:




											'''simple docstring'''

											super().__init__()
											__snake_case				=		(config.num_hidden_layers + 1) // 5
											__snake_case				=		config.hidden_size
											__snake_case				=		1
											__snake_case				=		nn.ModuleList(
											    [
											        BasicTransformerBlock(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    activation_fn="gelu"     ,    attention_bias=SCREAMING_SNAKE_CASE						)
											        for _ in range(SCREAMING_SNAKE_CASE						)
											    ]						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Optional[int]:




											'''simple docstring'''

											for block in self.blocks:
																	__snake_case				=		block(SCREAMING_SNAKE_CASE						)

											return hidden_states





