

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_ :				int						 =    datasets.utils.logging.get_logger(__name__)

A_ :				str						 =    ['names', 'prefix']
A_ :				List[Any]						 =    ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
A_ :				List[Any]						 =    ['encoding_errors', 'on_bad_lines']
A_ :				Optional[int]						 =    ['date_format']



@dataclass
class 					_a		(datasets.BuilderConfig       ):


					'''simple docstring'''






					UpperCAmelCase__:     str													= ","
					UpperCAmelCase__:     Optional[str]													= None
					UpperCAmelCase__:     Optional[Union[int, List[int], str]]													= "infer"
					UpperCAmelCase__:     Optional[List[str]]													= None
					UpperCAmelCase__:     Optional[List[str]]													= None
					UpperCAmelCase__:     Optional[Union[int, str, List[int], List[str]]]													= None
					UpperCAmelCase__:     Optional[Union[List[int], List[str]]]													= None
					UpperCAmelCase__:     Optional[str]													= None
					UpperCAmelCase__:     bool													= True
					UpperCAmelCase__:     Optional[Literal["c", "python", "pyarrow"]]													= None
					UpperCAmelCase__:     Dict[Union[int, str], Callable[[Any], Any]]													= None
					UpperCAmelCase__:     Optional[list]													= None
					UpperCAmelCase__:     Optional[list]													= None
					UpperCAmelCase__:     bool													= False
					UpperCAmelCase__:     Optional[Union[int, List[int]]]													= None
					UpperCAmelCase__:     Optional[int]													= None
					UpperCAmelCase__:     Optional[Union[str, List[str]]]													= None
					UpperCAmelCase__:     bool													= True
					UpperCAmelCase__:     bool													= True
					UpperCAmelCase__:     bool													= False
					UpperCAmelCase__:     bool													= True
					UpperCAmelCase__:     Optional[str]													= None
					UpperCAmelCase__:     str													= "."
					UpperCAmelCase__:     Optional[str]													= None
					UpperCAmelCase__:     str													= '"'
					UpperCAmelCase__:     int													= 0
					UpperCAmelCase__:     Optional[str]													= None
					UpperCAmelCase__:     Optional[str]													= None
					UpperCAmelCase__:     Optional[str]													= None
					UpperCAmelCase__:     Optional[str]													= None
					UpperCAmelCase__:     bool													= True
					UpperCAmelCase__:     bool													= True
					UpperCAmelCase__:     int													= 0
					UpperCAmelCase__:     bool													= True
					UpperCAmelCase__:     bool													= False
					UpperCAmelCase__:     Optional[str]													= None
					UpperCAmelCase__:     int													= 1_00_00
					UpperCAmelCase__:     Optional[datasets.Features]													= None
					UpperCAmelCase__:     Optional[str]													= "strict"
					UpperCAmelCase__:     Literal["error", "warn", "skip"]													= "error"
					UpperCAmelCase__:     Optional[str]													= None






					def 						__A    (				self    ):
						if self.delimiter is not None:
							A__							:					Any       =	self.delimiter
						if self.column_names is not None:
							A__							:					Optional[Any]       =	self.column_names






					@property
					def 						__A    (				self    ):
						A__							:					Tuple       =	{
						    """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()					, A__    ):
								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 					_a		(datasets.ArrowBasedBuilder       ):


					'''simple docstring'''






					UpperCAmelCase__:     Tuple													= CsvConfig






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






					def 						__A    (				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}"""    )
						A__							:					Any       =	dl_manager.download_and_extract(self.config.data_files    )
						if isinstance(A__					, (str, list, tuple)    ):
							A__							:					Dict       =	data_files
							if isinstance(A__					, A__    ):
								A__							:					Any       =	[files]
							A__							:					Dict       =	[dl_manager.iter_files(A__    ) for file in files]
							return [datasets.SplitGenerator(name=datasets.Split.TRAIN					, gen_kwargs={"""files""": files}    )]
						A__							:					List[Any]       =	[]
						for split_name, files in data_files.items():
							if isinstance(A__					, A__    ):
								A__							:					Dict       =	[files]
							A__							:					int       =	[dl_manager.iter_files(A__    ) for file in files]
							splits.append(datasets.SplitGenerator(name=A__					, gen_kwargs={"""files""": files}    )    )
						return splits






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






					def 						__A    (				self					, A__    ):
						A__							:					Any       =	self.config.features.arrow_schema if self.config.features else None
						# dtype allows reading an int column as str
						A__							:					Optional[int]       =	(
						    {
						        name: dtype.to_pandas_dtype() if not require_storage_cast(A__    ) 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(A__    )    ):
							A__							:					Union[str, Any]       =	pd.read_csv(A__					, iterator=A__					, dtype=A__					, **self.config.pd_read_csv_kwargs    )
							try:
								for batch_idx, df in enumerate(A__    ):
									A__							:					Optional[int]       =	pa.Table.from_pandas(A__    )
									# 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(A__    )
							except ValueError as e:
								logger.error(F"""Failed to read file '{file}' with error {type(A__    )}: {e}"""    )
								raise






from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 					_a		(__magic_name__	,     __magic_name__	,     __magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     str													= [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']






					@register_to_config
					def __init__(				self					, A__					, A__					, A__ = None					, A__ = 5_0257					, A__ = 1024					, A__ = 768					, A__ = 12					, A__ = 12					, A__ = None					, A__ = "gelu_new"					, A__ = 0.1					, A__ = 0.1					, A__ = 0.1					, A__ = 1e-5					, A__ = 0.0_2					, A__ = True					, A__ = True					, A__ = False					, A__ = False					, ):
						super().__init__()

						A__							:					Union[str, Any]       =	prefix_length

						if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
							raise ValueError(
							    F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
							    F""" `n_embd`: {n_embd} are not equal."""    )

						A__							:					str       =	prefix_inner_dim
						A__							:					Optional[Any]       =	prefix_hidden_dim

						A__							:					Tuple       =	(
						    nn.Linear(self.prefix_inner_dim					, self.prefix_hidden_dim    )
						    if self.prefix_hidden_dim is not None
						    else nn.Identity()
						)
						A__							:					int       =	(
						    nn.Linear(self.prefix_hidden_dim					, A__    ) if self.prefix_hidden_dim is not None else nn.Identity()
						)

						A__							:					Tuple       =	GPTaConfig(
						    vocab_size=A__					, n_positions=A__					, n_embd=A__					, n_layer=A__					, n_head=A__					, n_inner=A__					, activation_function=A__					, resid_pdrop=A__					, embd_pdrop=A__					, attn_pdrop=A__					, layer_norm_epsilon=A__					, initializer_range=A__					, scale_attn_weights=A__					, use_cache=A__					, scale_attn_by_inverse_layer_idx=A__					, reorder_and_upcast_attn=A__					, )
						A__							:					int       =	GPTaLMHeadModel(A__    )






					def 						__A    (				self					, A__					, A__					, A__ = None					, A__ = None					, ):
						A__							:					List[str]       =	self.transformer.transformer.wte(A__    )
						A__							:					int       =	self.encode_prefix(A__    )
						A__							:					int       =	self.decode_prefix(A__    )
						A__							:					Optional[Any]       =	torch.cat((prefix_embeds, embedding_text)					, dim=1    )

						if labels is not None:
							A__							:					Any       =	self.get_dummy_token(input_ids.shape[0]					, input_ids.device    )
							A__							:					List[Any]       =	torch.cat((dummy_token, input_ids)					, dim=1    )
						A__							:					List[str]       =	self.transformer(inputs_embeds=A__					, labels=A__					, attention_mask=A__    )
						if self.prefix_hidden_dim is not None:
							return out, hidden
						else:
							return out






					def 						__A    (				self					, A__					, A__    ):
						return torch.zeros(A__					, self.prefix_length					, dtype=torch.intaa					, device=A__    )






					def 						__A    (				self					, A__    ):
						return self.encode_prefix(A__    )






					@torch.no_grad()
					def 						__A    (				self					, A__					, A__					, A__    ):
						A__							:					List[Any]       =	torch.split(A__					, 1					, dim=0    )
						A__							:					Optional[int]       =	[]
						A__							:					str       =	[]
						for feature in features:
							A__							:					Dict       =	self.decode_prefix(feature.to(A__    )    )  # back to the clip feature
							# Only support beam search for now
							A__     ,     A__							:					Union[str, Any]       =	self.generate_beam(
							    input_embeds=A__					, device=A__					, eos_token_id=A__    )
							generated_tokens.append(output_tokens[0]    )
							generated_seq_lengths.append(seq_lengths[0]    )
						A__							:					int       =	torch.stack(A__    )
						A__							:					List[Any]       =	torch.stack(A__    )
						return generated_tokens, generated_seq_lengths






					@torch.no_grad()
					def 						__A    (				self					, A__=None					, A__=None					, A__=None					, A__ = 5					, A__ = 67					, A__ = 1.0					, A__ = None					, ):
						A__							:					Any       =	eos_token_id
						A__							:					Any       =	None
						A__							:					Optional[int]       =	None
						A__							:					Optional[Any]       =	torch.ones(A__					, device=A__					, dtype=torch.int    )
						A__							:					Any       =	torch.zeros(A__					, device=A__					, dtype=torch.bool    )

						if input_embeds is not None:
							A__							:					Dict       =	input_embeds
						else:
							A__							:					str       =	self.transformer.transformer.wte(A__    )

						for i in range(A__    ):
							A__							:					Dict       =	self.transformer(inputs_embeds=A__    )
							A__							:					str       =	outputs.logits
							A__							:					Union[str, Any]       =	logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
							A__							:					Any       =	logits.softmax(-1    ).log()

							if scores is None:
								A__     ,     A__							:					Optional[int]       =	logits.topk(A__					, -1    )
								A__							:					List[Any]       =	generated.expand(A__					, *generated.shape[1:]    )
								A__     ,     A__							:					List[Any]       =	next_tokens.permute(1					, 0    ), scores.squeeze(0    )
								if tokens is None:
									A__							:					Optional[Any]       =	next_tokens
								else:
									A__							:					List[Any]       =	tokens.expand(A__					, *tokens.shape[1:]    )
									A__							:					int       =	torch.cat((tokens, next_tokens)					, dim=1    )
							else:
								A__							:					Optional[int]       =	-float(np.inf    )
								A__							:					List[Any]       =	0
								A__							:					str       =	scores[:, None] + logits
								seq_lengths[~is_stopped] += 1
								A__							:					Dict       =	scores_sum / seq_lengths[:, None]
								A__     ,     A__							:					List[Any]       =	scores_sum_average.view(-1    ).topk(A__					, -1    )
								A__							:					Tuple       =	next_tokens // scores_sum.shape[1]
								A__							:					Optional[Any]       =	seq_lengths[next_tokens_source]
								A__							:					List[str]       =	next_tokens % scores_sum.shape[1]
								A__							:					Optional[int]       =	next_tokens.unsqueeze(1    )
								A__							:					int       =	tokens[next_tokens_source]
								A__							:					List[Any]       =	torch.cat((tokens, next_tokens)					, dim=1    )
								A__							:					str       =	generated[next_tokens_source]
								A__							:					Optional[Any]       =	scores_sum_average * seq_lengths
								A__							:					Union[str, Any]       =	is_stopped[next_tokens_source]

							A__							:					str       =	self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]					, 1					, -1    )
							A__							:					Optional[int]       =	torch.cat((generated, next_token_embed)					, dim=1    )
							A__							:					List[str]       =	is_stopped + next_tokens.eq(A__    ).squeeze()
							if is_stopped.all():
								break

						A__							:					Dict       =	scores / seq_lengths
						A__							:					Dict       =	scores.argsort(descending=A__    )
						# tokens tensors are already padded to max_seq_length
						A__							:					Union[str, Any]       =	[tokens[i] for i in order]
						A__							:					Any       =	torch.stack(A__					, dim=0    )
						A__							:					Dict       =	torch.tensor([seq_lengths[i] for i in order]					, dtype=seq_lengths.dtype    )
						return output_texts, seq_lengths



