






import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput





class 						SCREAMING_SNAKE_CASE				( _a					):





							"""simple docstring"""

							def __init__(  self:   List[Any]		,     __A:   Any		,     __A:   List[Any]		,     __A:   Union[str, Any]   )    ->				Union[str, Any]:
												_A								= dataset
												_A								= process
												_A								= params

							def __len__(  self:   Dict   )    ->				Any:
												return len(self.dataset   )



							def __getitem__(  self:   Tuple		,     __A:   Optional[Any]   )    ->				List[Any]:
												_A								= self.dataset[i]
												_A								= self.process(snake_case_		,     **self.params   )
												return processed





class 						SCREAMING_SNAKE_CASE				( _a					):





							"""simple docstring"""

							def __init__(  self:   Optional[Any]		,     __A:   List[Any]		,     __A:   Dict		,     __A:   List[Any]		,     __A:   Tuple=None   )    ->				Dict:
												_A								= loader
												_A								= infer
												_A								= params
												if loader_batch_size == 1:
																	# Let's spare some time by deactivating altogether
																	_A								= None
												_A								= loader_batch_size

												# Internal bookkeeping
												_A								= None
												_A								= None

							def __len__(  self:   Union[str, Any]   )    ->				Tuple:
												return len(self.loader   )

							def __iter__(  self:   Any   )    ->				Optional[Any]:
												_A								= iter(self.loader   )
												return self

							def       __A (  self:   List[Any]   )    ->				Optional[int]:
												if isinstance(self._loader_batch_data		,     torch.Tensor   ):
																	# Batch data is simple tensor, just fetch the slice
																	_A								= self._loader_batch_data[self._loader_batch_index]
												else:
																	# Batch data is assumed to be BaseModelOutput (or dict)
																	_A								= {}
																	for k, element in self._loader_batch_data.items():
																						if isinstance(snake_case_		,     snake_case_   ):
																											# Convert ModelOutput to tuple first
																											_A								= element.to_tuple()
																											if isinstance(element[0]		,     torch.Tensor   ):
																																_A								= tuple(el[self._loader_batch_index].unsqueeze(0   ) for el in element   )
																											elif isinstance(element[0]		,     np.ndarray   ):
																																_A								= tuple(np.expand_dims(el[self._loader_batch_index]		,     0   ) for el in element   )
																											continue
																						if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(snake_case_		,     snake_case_   ):
																											# Those are stored as lists of tensors so need specific unbatching.
																											if isinstance(element[0]		,     torch.Tensor   ):
																																_A								= tuple(el[self._loader_batch_index].unsqueeze(0   ) for el in element   )
																											elif isinstance(element[0]		,     np.ndarray   ):
																																_A								= tuple(np.expand_dims(el[self._loader_batch_index]		,     0   ) for el in element   )
																											continue
																						if element is None:
																											# This can happen for optional data that get passed around
																											_A								= None
																						elif isinstance(element[self._loader_batch_index]		,     torch.Tensor   ):
																											# Take correct batch data, but make it looked like batch_size=1
																											# For compatibility with other methods within transformers

																											_A								= element[self._loader_batch_index].unsqueeze(0   )
																						elif isinstance(element[self._loader_batch_index]		,     np.ndarray   ):
																											# Take correct batch data, but make it looked like batch_size=1
																											# For compatibility with other methods within transformers
																											_A								= np.expand_dims(element[self._loader_batch_index]		,     0   )
																						else:
																											# This is typically a list, so no need to `unsqueeze`.
																											_A								= element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																	_A								= self._loader_batch_data.__class__(snake_case_   )
												self._loader_batch_index += 1
												return result



							def       __A (  self:   int   )    ->				List[Any]:
												if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																	# We are currently unrolling a batch so we just need to return
																	# the current item within a batch
																	return self.loader_batch_item()

												# We're out of items within a batch
												_A								= next(self.iterator   )
												_A								= self.infer(snake_case_		,     **self.params   )
												# We now have a batch of "inferred things".
												if self.loader_batch_size is not None:
																	# Try to infer the size of the batch
																	if isinstance(snake_case_		,     torch.Tensor   ):
																						_A								= processed
																	else:
																						_A								= list(processed.keys()   )[0]
																						_A								= processed[key]
																	if isinstance(snake_case_		,     snake_case_   ):
																						_A								= len(snake_case_   )
																	else:
																						_A								= first_tensor.shape[0]
																	if 0 < observed_batch_size < self.loader_batch_size:
																						# could be last batch so we can't unroll as many
																						# elements.
																						_A								= observed_batch_size
																	# Setting internal index to unwrap the batch
																	_A								= processed
																	_A								= 0
																	return self.loader_batch_item()
												else:
																	# We're not unrolling batches
																	return processed





class 						SCREAMING_SNAKE_CASE				( _a					):





							"""simple docstring"""

							def __init__(  self:   Any		,     __A:   Tuple		,     __A:   int		,     __A:   Dict		,     __A:   Union[str, Any]=None   )    ->				int:
												super().__init__(snake_case_		,     snake_case_		,     snake_case_   )

							def __iter__(  self:   Tuple   )    ->				Union[str, Any]:
												_A								= iter(self.loader   )
												_A								= None
												return self



							def       __A (  self:   Optional[int]   )    ->				Any:
												if self.subiterator is None:
																	_A								= self.infer(next(self.iterator   )		,     **self.params   )
												try:
																	# Try to return next item
																	_A								= next(self.subiterator   )
												except StopIteration:
																	# When a preprocess iterator ends, we can start lookig at the next item
																	# ChunkIterator will keep feeding until ALL elements of iterator
																	# all have created their subiterator and have been iterating against.
																	#
																	# Another way to look at it, is we're basically flattening lists of lists
																	# into a single list, but with generators
																	_A								= self.infer(next(self.iterator   )		,     **self.params   )
																	_A								= next(self.subiterator   )
												return processed





class 						SCREAMING_SNAKE_CASE				( _a					):





							"""simple docstring"""

							def __iter__(  self:   Optional[Any]   )    ->				str:
												_A								= iter(self.loader   )
												return self



							def       __A (  self:   str   )    ->				Tuple:
												# Extremely similar to PipelineIterator in its unpacking mechanism
												# BUT, we have an extra required item which is the presence of `is_last`
												# That is because everything is flattened by `PipelineChunkIterator` we
												# need to keep track of how to regroup here in the original `process`
												# boundaries so that `process` and `postprocess` see the same data.

												# This iterator accumulates items (possibly while unbatching) until it
												# its a `is_last` and then just passes it on to the caller.
												_A								= False
												_A								= []
												if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																	while self._loader_batch_index < self.loader_batch_size:
																						_A								= self.loader_batch_item()
																						_A								= item.pop('''is_last'''   )
																						accumulator.append(snake_case_   )
																						if is_last:
																											return accumulator

												while not is_last:
																	_A								= self.infer(next(self.iterator   )		,     **self.params   )
																	if self.loader_batch_size is not None:
																						if isinstance(snake_case_		,     torch.Tensor   ):
																											_A								= processed
																						else:
																											_A								= list(processed.keys()   )[0]
																											_A								= processed[key]
																						if isinstance(snake_case_		,     snake_case_   ):
																											_A								= len(snake_case_   )
																						else:
																											_A								= first_tensor.shape[0]
																						if 0 < observed_batch_size < self.loader_batch_size:
																											# could be last batch so we can't unroll as many
																											# elements.
																											_A								= observed_batch_size
																						_A								= processed
																						_A								= 0
																						while self._loader_batch_index < self.loader_batch_size:
																											_A								= self.loader_batch_item()
																											_A								= item.pop('''is_last'''   )
																											accumulator.append(snake_case_   )
																											if is_last:
																																return accumulator
																	else:
																						_A								= processed
																						_A								= item.pop('''is_last'''   )
																						accumulator.append(snake_case_   )
												return accumulator





class 						SCREAMING_SNAKE_CASE				( _a					):





							"""simple docstring"""

							def __init__(  self:   str		,     __A:   Any		,     __A:   Union[str, Any]   )    ->				Any:
												_A								= dataset
												_A								= key

							def __len__(  self:   Tuple   )    ->				int:
												return len(self.dataset   )



							def __getitem__(  self:   Tuple		,     __A:   Tuple   )    ->				Dict:
												return self.dataset[i][self.key]





class 						SCREAMING_SNAKE_CASE				( _a					):





							"""simple docstring"""

							def __init__(  self:   Optional[Any]		,     __A:   Union[str, Any]		,     __A:   Optional[Any]		,     __A:   int   )    ->				Optional[int]:
												_A								= dataset
												_A								= keya
												_A								= keya

							def __len__(  self:   Optional[int]   )    ->				List[str]:
												return len(self.dataset   )



							def __getitem__(  self:   Tuple		,     __A:   Dict   )    ->				Any:
												return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}











import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


__A      =				NewType('DataClass', Any)
__A      =				NewType('DataClassType', Any)




def      __A					(     _lowercase ):

					'''simple docstring'''







					if isinstance(_lowercase					,			_lowercase ):
										return v
					if v.lower() in ("yes", "true", "t", "y", "1"):
										return True
					elif v.lower() in ("no", "false", "f", "n", "0"):
										return False
					else:
										raise ArgumentTypeError(
										    f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= {str(_lowercase ): choice for choice in choices}
					return lambda _lowercase : str_to_choice.get(_lowercase					,			_lowercase )




def      __A					(     *,
    _lowercase = None					,			_lowercase = None					,			_lowercase = dataclasses.MISSING					,			_lowercase = dataclasses.MISSING					,			_lowercase = None					,			**_lowercase					,			):

					'''simple docstring'''







					if metadata is None:
										# Important, don't use as default param in function signature because dict is mutable and shared across function calls
										_A								= {}
					if aliases is not None:
										_A								= aliases
					if help is not None:
										_A								= help

					return dataclasses.field(metadata=_lowercase					,			default=_lowercase					,			default_factory=_lowercase					,			**_lowercase )





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					42

							def __init__(  self:   Optional[Any]		,     __A:   Union[DataClassType, Iterable[DataClassType]]		,     **__A:   List[Any]   )    ->				str:
												# To make the default appear when using --help
												if "formatter_class" not in kwargs:
																	_A								= ArgumentDefaultsHelpFormatter
												super().__init__(**__A   )
												if dataclasses.is_dataclass(__A   ):
																	_A								= [dataclass_types]
												_A								= list(__A   )
												for dtype in self.dataclass_types:
																	self._add_dataclass_arguments(__A   )

							@staticmethod
							def       __A (  __A:   ArgumentParser		,     __A:   dataclasses.Field   )    ->				str:
												_A								= f"""--{field.name}"""
												_A								= field.metadata.copy()
												# field.metadata is not used at all by Data Classes,
												# it is provided as a third-party extension mechanism.
												if isinstance(field.type		,     __A   ):
																	raise RuntimeError(
																	    '''Unresolved type detected, which should have been done with the help of '''
																	    '''`typing.get_type_hints` method by default'''   )

												_A								= kwargs.pop('''aliases'''		,     []   )
												if isinstance(__A		,     __A   ):
																	_A								= [aliases]

												_A								= getattr(field.type		,     '''__origin__'''		,     field.type   )
												if origin_type is Union or (hasattr(__A		,     '''UnionType'''   ) and isinstance(__A		,     types.UnionType   )):
																	if str not in field.type.__args__ and (
																	    len(field.type.__args__   ) != 2 or type(__A   ) not in field.type.__args__
																	):
																						raise ValueError(
																						    '''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
																						    ''' the argument parser only supports one type per argument.'''
																						    f""" Problem encountered in field '{field.name}'."""   )
																	if type(__A   ) not in field.type.__args__:
																						# filter `str` in Union
																						_A								= field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
																						_A								= getattr(field.type		,     '''__origin__'''		,     field.type   )
																	elif bool not in field.type.__args__:
																						# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
																						_A								= (
																						    field.type.__args__[0] if isinstance(__A		,     field.type.__args__[1]   ) else field.type.__args__[1]
																						)
																						_A								= getattr(field.type		,     '''__origin__'''		,     field.type   )

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
												_A								= {}
												if origin_type is Literal or (isinstance(field.type		,     __A   ) and issubclass(field.type		,     __A   )):
																	if origin_type is Literal:
																						_A								= field.type.__args__
																	else:
																						_A								= [x.value for x in field.type]

																	_A								= make_choice_type_function(kwargs['''choices''']   )

																	if field.default is not dataclasses.MISSING:
																						_A								= field.default
																	else:
																						_A								= True
												elif field.type is bool or field.type == Optional[bool]:
																	# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
																	# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
																	_A								= copy(__A   )

																	# Hack because type=bool in argparse does not behave as we want.
																	_A								= string_to_bool
																	if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
																						# Default value is False if we have no default when of type bool.
																						_A								= False if field.default is dataclasses.MISSING else field.default
																						# This is the value that will get picked if we don't include --field_name in any way
																						_A								= default
																						# This tells argparse we accept 0 or 1 value after --field_name
																						_A								= '''?'''
																						# This is the value that will get picked if we do --field_name (without value)
																						_A								= True
												elif isclass(__A   ) and issubclass(__A		,     __A   ):
																	_A								= field.type.__args__[0]
																	_A								= '''+'''
																	if field.default_factory is not dataclasses.MISSING:
																						_A								= field.default_factory()
																	elif field.default is dataclasses.MISSING:
																						_A								= True
												else:
																	_A								= field.type
																	if field.default is not dataclasses.MISSING:
																						_A								= field.default
																	elif field.default_factory is not dataclasses.MISSING:
																						_A								= field.default_factory()
																	else:
																						_A								= True
												parser.add_argument(__A		,     *__A		,     **__A   )

												# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
												# Order is important for arguments with the same destination!
												# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
												# here and we do not need those changes/additional keys.
												if field.default is True and (field.type is bool or field.type == Optional[bool]):
																	_A								= False
																	parser.add_argument(f"""--no_{field.name}"""		,     action='''store_false'''		,     dest=field.name		,     **__A   )

							def       __A (  self:   Dict		,     __A:   DataClassType   )    ->				List[Any]:
												if hasattr(__A		,     '''_argument_group_name'''   ):
																	_A								= self.add_argument_group(dtype._argument_group_name   )
												else:
																	_A								= self

												try:
																	_A								= get_type_hints(__A   )
												except NameError:
																	raise RuntimeError(
																	    f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
																	    '''removing line of `from __future__ import annotations` which opts in Postponed '''
																	    '''Evaluation of Annotations (PEP 563)'''   )
												except TypeError as ex:
																	# Remove this block when we drop Python 3.9 support
																	if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__A   ):
																						_A								= '''.'''.join(map(__A		,     sys.version_info[:3]   )   )
																						raise RuntimeError(
																						    f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
																						    '''line of `from __future__ import annotations` which opts in union types as '''
																						    '''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
																						    '''support Python versions that lower than 3.10, you need to use '''
																						    '''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
																						    '''`X | None`.'''   ) from ex
																	raise

												for field in dataclasses.fields(__A   ):
																	if not field.init:
																						continue
																	_A								= type_hints[field.name]
																	self._parse_dataclass_field(__A		,     __A   )

							def       __A (  self:   int		,     __A:   Any=None		,     __A:   int=False		,     __A:   Any=True		,     __A:   Optional[Any]=None		,     __A:   Any=None		,     )    ->				Tuple[DataClass, ...]:

												if args_file_flag or args_filename or (look_for_args_file and len(sys.argv   )):
																	_A								= []

																	if args_filename:
																						args_files.append(Path(__A   )   )
																	elif look_for_args_file and len(sys.argv   ):
																						args_files.append(Path(sys.argv[0]   ).with_suffix('''.args'''   )   )

																	# args files specified via command line flag should overwrite default args files so we add them last
																	if args_file_flag:
																						# Create special parser just to extract the args_file_flag values
																						_A								= ArgumentParser()
																						args_file_parser.add_argument(__A		,     type=__A		,     action='''append'''   )

																						# Use only remaining args for further parsing (remove the args_file_flag)
																						_A							,_A								= args_file_parser.parse_known_args(args=__A   )
																						_A								= vars(__A   ).get(args_file_flag.lstrip('''-'''   )		,     __A   )

																						if cmd_args_file_paths:
																											args_files.extend([Path(__A   ) for p in cmd_args_file_paths]   )

																	_A								= []
																	for args_file in args_files:
																						if args_file.exists():
																											file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
																	_A								= file_args + args if args is not None else file_args + sys.argv[1:]
												_A							,_A								= self.parse_known_args(args=__A   )
												_A								= []
												for dtype in self.dataclass_types:
																	_A								= {f.name for f in dataclasses.fields(__A   ) if f.init}
																	_A								= {k: v for k, v in vars(__A   ).items() if k in keys}
																	for k in keys:
																						delattr(__A		,     __A   )
																	_A								= dtype(**__A   )
																	outputs.append(__A   )
												if len(namespace.__dict__   ) > 0:
																	# additional namespace.
																	outputs.append(__A   )
												if return_remaining_strings:
																	return (*outputs, remaining_args)
												else:
																	if remaining_args:
																						raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}"""   )

																	return (*outputs,)

							def       __A (  self:   Tuple		,     __A:   Dict[str, Any]		,     __A:   bool = False   )    ->				Tuple[DataClass, ...]:
												_A								= set(args.keys()   )
												_A								= []
												for dtype in self.dataclass_types:
																	_A								= {f.name for f in dataclasses.fields(__A   ) if f.init}
																	_A								= {k: v for k, v in args.items() if k in keys}
																	unused_keys.difference_update(inputs.keys()   )
																	_A								= dtype(**__A   )
																	outputs.append(__A   )
												if not allow_extra_keys and unused_keys:
																	raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__A   )}"""   )
												return tuple(__A   )

							def       __A (  self:   Tuple		,     __A:   str		,     __A:   bool = False   )    ->				Tuple[DataClass, ...]:
												with open(Path(__A   )		,     encoding='''utf-8'''   ) as open_json_file:
																	_A								= json.loads(open_json_file.read()   )
												_A								= self.parse_dict(__A		,     allow_extra_keys=__A   )
												return tuple(__A   )



							def       __A (  self:   List[Any]		,     __A:   str		,     __A:   bool = False   )    ->				Tuple[DataClass, ...]:
												_A								= self.parse_dict(yaml.safe_load(Path(__A   ).read_text()   )		,     allow_extra_keys=__A   )
												return tuple(__A   )



