'''simple docstring'''





import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text
def 						__A							(     UpperCAmelCase					)     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Optional[Any]   			=						[]
						for line in lines:
												_UpperCamelCase			:			Dict   			=						re.sub(R"#.*"     ,""     ,UpperCAmelCase					)  # remove comments
												if line:
																		filtered_lines.append(UpperCAmelCase					)
						_UpperCamelCase			:			Optional[Any]   			=						"\n".join(UpperCAmelCase					)

						# Make a hash from all this code
						_UpperCamelCase			:			Dict   			=						full_str.encode("utf-8"					)
						return shaaaa(UpperCAmelCase					).hexdigest()


# get importable module names and hash for caching
lowerCAmelCase_	:		Tuple			=     {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
lowerCAmelCase_	:		str			=     {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
lowerCAmelCase_	:		int			=     {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
lowerCAmelCase_	:		Dict[str, List[str]]			=     {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
			_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")






_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")



'''simple docstring'''





import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


lowerCAmelCase_	:		Optional[int]			=     logging.get_logger(__name__)


lowerCAmelCase_	:		int			=     r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

"""




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				@add_start_docstrings(lowercase__					)
				def __call__(						self					:						str       ,		lowercase__					:						torch.LongTensor       ,		lowercase__					:						torch.FloatTensor       ,		**lowercase__					:						Tuple					)							->bool:


										'''simple docstring'''




										raise NotImplementedError("StoppingCriteria needs to be subclassed"					)





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						Union[str, Any]       ,		lowercase__					:						int       ,		lowercase__					:						Optional[int] = None					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[Any]   			=						max_length
										_UpperCamelCase			:			str   			=						max_position_embeddings







				@add_start_docstrings(lowercase__					)
				def __call__(						self					:						Optional[int]       ,		lowercase__					:						torch.LongTensor       ,		lowercase__					:						torch.FloatTensor       ,		**lowercase__					:						Optional[Any]					)							->bool:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						input_ids.shape[-1]
										_UpperCamelCase			:			Optional[int]   			=						cur_len >= self.max_length
										if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
																logger.warning_once(
																    "This is a friendly reminder - the current text generation call will exceed the model's predefined "
																    f'''maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '''
																    "exceptions, performance degradation, or nothing at all."					)
										return is_done





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						List[Any]       ,		lowercase__					:						int       ,		lowercase__					:						int					)							->int:


										'''simple docstring'''




										warnings.warn(
										    "The class `MaxNewTokensCriteria` is deprecated. "
										    f'''Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '''
										    "with `max_length = start_length + max_new_tokens` instead."       ,		lowercase__       ,		)
										_UpperCamelCase			:			Dict   			=						start_length
										_UpperCamelCase			:			Optional[Any]   			=						max_new_tokens
										_UpperCamelCase			:			List[Any]   			=						start_length + max_new_tokens







				@add_start_docstrings(lowercase__					)
				def __call__(						self					:						Dict       ,		lowercase__					:						torch.LongTensor       ,		lowercase__					:						torch.FloatTensor       ,		**lowercase__					:						Dict					)							->bool:


										'''simple docstring'''




										return input_ids.shape[-1] >= self.max_length





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						Union[str, Any]       ,		lowercase__					:						float       ,		lowercase__					:						Optional[float] = None					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			Dict   			=						max_time
										_UpperCamelCase			:			int   			=						time.time() if initial_timestamp is None else initial_timestamp







				@add_start_docstrings(lowercase__					)
				def __call__(						self					:						Dict       ,		lowercase__					:						torch.LongTensor       ,		lowercase__					:						torch.FloatTensor       ,		**lowercase__					:						Tuple					)							->bool:


										'''simple docstring'''




										return time.time() - self.initial_timestamp > self.max_time





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				@add_start_docstrings(lowercase__					)
				def __call__(						self					:						Optional[int]       ,		lowercase__					:						torch.LongTensor       ,		lowercase__					:						torch.FloatTensor       ,		**lowercase__					:						List[Any]					)							->bool:


										'''simple docstring'''




										return any(criteria(lowercase__       ,		lowercase__					) for criteria in self					)







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


										'''simple docstring'''




										for stopping_criterium in self:
																if isinstance(lowercase__       ,		lowercase__					):
																						return stopping_criterium.max_length
																elif isinstance(lowercase__       ,		lowercase__					):
																						return stopping_criterium.max_length
										return None





def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			StoppingCriteriaList:




						'''simple docstring'''
						_UpperCamelCase			:			Optional[int]   			=						stopping_criteria.max_length
						_UpperCamelCase			:			Optional[Any]   			=						deepcopy(UpperCAmelCase					)
						if stopping_max_length is not None and stopping_max_length != max_length:
												warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"     ,UpperCAmelCase					)
						elif stopping_max_length is None:
												new_stopping_criteria.append(MaxLengthCriteria(max_length=UpperCAmelCase					)					)
						return new_stopping_criteria


