






"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


lowerCamelCase_					=      get_logger(__name__)







class 			UpperCamelCase_       :
	__magic_name__	      =				'''dummy_data'''
	__magic_name__	      =				'''datasets'''
	__magic_name__	      =				False





	def __init__(      self					: str	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: Union[Version, str]	,	lowerCAmelCase_					: Optional[str] = None	,	lowerCAmelCase_					: bool = False	,	lowerCAmelCase_					: bool = True	,	lowerCAmelCase_					: Optional[List[Callable]] = None	,	)						->			Tuple:
				UpperCAmelCase_     : Optional[int]			     =					0
				UpperCAmelCase_     : int			     =					dataset_name
				UpperCAmelCase_     : Optional[int]			     =					cache_dir
				UpperCAmelCase_     : Tuple			     =					use_local_dummy_data
				UpperCAmelCase_     : int			     =					config
				# download_callbacks take a single url as input
				UpperCAmelCase_     : List[Callable]			     =					download_callbacks or []
				# if False, it doesn't load existing files and it returns the paths of the dummy files relative
				# to the dummy_data zip file root
				UpperCAmelCase_     : Optional[Any]			     =					load_existing_dummy_data

				# TODO(PVP, QL) might need to make this more general
				UpperCAmelCase_     : Dict			     =					str(lowerCAmelCase_						)
				# to be downloaded
				UpperCAmelCase_     : Optional[Any]			     =					None
				UpperCAmelCase_     : int			     =					None





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			str:
				if self._dummy_file is None:
							UpperCAmelCase_     : List[str]			     =					self.download_dummy_data()
				return self._dummy_file





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			int:
				if self.config is not None:
							# structure is dummy / config_name / version_name
							return os.path.join("dummy"	,	self.config.name	,	self.version_name						)
				# structure is dummy / version_name
				return os.path.join("dummy"	,	self.version_name						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Optional[int]:
				return os.path.join(self.dummy_data_folder	,	"dummy_data.zip"						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Tuple:
				UpperCAmelCase_     : int			     =					(
				    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
				)

				UpperCAmelCase_     : Union[str, Any]			     =					cached_path(
				    lowerCAmelCase_	,	cache_dir=self.cache_dir	,	extract_compressed_file=lowerCAmelCase_	,	force_extract=lowerCAmelCase_						)

				return os.path.join(lowerCAmelCase_	,	self.dummy_file_name						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			int:
				return os.path.join(self.datasets_scripts_dir	,	self.dataset_name	,	self.dummy_zip_file						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Optional[Any]:
				if self._bucket_url is None:
							UpperCAmelCase_     : Union[str, Any]			     =					hf_github_url(self.dataset_name	,	self.dummy_zip_file.replace(os.sep	,	"/"						)						)
				return self._bucket_url





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Optional[Any]:
				# return full path if its a dir
				if os.path.isdir(self.dummy_file						):
							return self.dummy_file
				# else cut off path to file -> example `xsum`.
				return "/".join(self.dummy_file.replace(os.sep	,	"/"						).split("/"						)[:-1]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: List[str]	,	*lowerCAmelCase_					: List[Any]						)						->			Optional[int]:
				if self.load_existing_dummy_data:
							# dummy data is downloaded and tested
							UpperCAmelCase_     : Dict			     =					self.dummy_file
				else:
							# dummy data cannot be downloaded and only the path to dummy file is returned
							UpperCAmelCase_     : Optional[int]			     =					self.dummy_file_name

				# special case when data_url is a dict
				if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						):
							return self.create_dummy_data_dict(lowerCAmelCase_	,	lowerCAmelCase_						)
				elif isinstance(lowerCAmelCase_	,	(list, tuple)						):
							return self.create_dummy_data_list(lowerCAmelCase_	,	lowerCAmelCase_						)
				else:
							return self.create_dummy_data_single(lowerCAmelCase_	,	lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]	,	lowerCAmelCase_					: int	,	*lowerCAmelCase_					: Union[str, Any]						)						->			Any:
				return self.download_and_extract(lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Tuple						)						->			Any:
				return self.download_and_extract(lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: Union[str, Any]	,	*lowerCAmelCase_					: Tuple	,	**lowerCAmelCase_					: Tuple						)						->			Union[str, Any]:
				return path





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			List[str]:
				return {}





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: List[Any]						)						->			List[Any]:
				UpperCAmelCase_     : Dict			     =					{}
				for key, single_urls in data_url.items():
							for download_callback in self.download_callbacks:
										if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						):
													for single_url in single_urls:
																download_callback(lowerCAmelCase_						)
										else:
													UpperCAmelCase_     : Tuple			     =					single_urls
													download_callback(lowerCAmelCase_						)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
							if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						):
										UpperCAmelCase_     : List[str]			     =					[os.path.join(lowerCAmelCase_	,	urllib.parse.quote_plus(Path(lowerCAmelCase_						).name						)						) for x in single_urls]
							else:
										UpperCAmelCase_     : Optional[int]			     =					single_urls
										UpperCAmelCase_     : Optional[Any]			     =					os.path.join(lowerCAmelCase_	,	urllib.parse.quote_plus(Path(lowerCAmelCase_						).name						)						)
							UpperCAmelCase_     : int			     =					value

				# make sure that values are unique
				if all(isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) for i in dummy_data_dict.values()						) and len(set(dummy_data_dict.values()						)						) < len(
				    dummy_data_dict.values()						):
							# append key to value to make its name unique
							UpperCAmelCase_     : List[str]			     =					{key: value + key for key, value in dummy_data_dict.items()}

				return dummy_data_dict





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: Optional[int]						)						->			Dict:
				UpperCAmelCase_     : str			     =					[]
				# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
				UpperCAmelCase_     : int			     =					all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"	,	lowerCAmelCase_						)						) for url in data_url						)
				UpperCAmelCase_     : Union[str, Any]			     =					all(
				    url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed"						) for url in data_url						)
				if data_url and (is_tf_records or is_pubmed_records):
							UpperCAmelCase_     : Tuple			     =					[data_url[0]] * len(lowerCAmelCase_						)
				for single_url in data_url:
							for download_callback in self.download_callbacks:
										download_callback(lowerCAmelCase_						)
							# we force the name of each key to be the last file / folder name of the url path
							# if the url has arguments, we need to encode them with urllib.parse.quote_plus
							UpperCAmelCase_     : Dict			     =					os.path.join(lowerCAmelCase_	,	urllib.parse.quote_plus(single_url.split("/"						)[-1]						)						)
							dummy_data_list.append(lowerCAmelCase_						)
				return dummy_data_list





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: Optional[Any]	,	lowerCAmelCase_					: str						)						->			Optional[int]:
				for download_callback in self.download_callbacks:
							download_callback(lowerCAmelCase_						)
				# we force the name of each key to be the last file / folder name of the url path
				# if the url has arguments, we need to encode them with urllib.parse.quote_plus
				UpperCAmelCase_     : Optional[Any]			     =					os.path.join(lowerCAmelCase_	,	urllib.parse.quote_plus(data_url.split("/"						)[-1]						)						)
				if os.path.exists(lowerCAmelCase_						) or not self.load_existing_dummy_data:
							return value
				else:
							# Backward compatibility, maybe deprecate at one point.
							# For many datasets with single url calls to dl_manager.download_and_extract,
							# the dummy_data.zip file is actually the zipped downloaded file
							# while now we expected the dummy_data.zip file to be a directory containing
							# the downloaded file.
							return path_to_dummy_data





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			int:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			Optional[int]:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: Dict						)						->			Optional[Any]:
				def _iter_archive_members(lowerCAmelCase_					: Dict						):
							# this preserves the order of the members inside the ZIP archive
							UpperCAmelCase_     : str			     =					Path(self.dummy_file						).parent
							UpperCAmelCase_     : Optional[Any]			     =					path.relative_to(lowerCAmelCase_						)
							with ZipFile(self.local_path_to_dummy_data						) as zip_file:
										UpperCAmelCase_     : str			     =					zip_file.namelist()
							for member in members:
										if member.startswith(relative_path.as_posix()						):
													yield dummy_parent_path.joinpath(lowerCAmelCase_						)

				UpperCAmelCase_     : Optional[Any]			     =					Path(lowerCAmelCase_						)
				UpperCAmelCase_     : List[Any]			     =					_iter_archive_members(lowerCAmelCase_						) if self.use_local_dummy_data else path.rglob("*"						)
				for file_path in file_paths:
							if file_path.is_file() and not file_path.name.startswith((".", "__")						):
										yield file_path.relative_to(lowerCAmelCase_						).as_posix(), file_path.open("rb"						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]	,	lowerCAmelCase_					: Tuple						)						->			str:
				if not isinstance(lowerCAmelCase_	,	lowerCAmelCase_						):
							UpperCAmelCase_     : str			     =					[paths]
				for path in paths:
							if os.path.isfile(lowerCAmelCase_						):
										if os.path.basename(lowerCAmelCase_						).startswith((".", "__")						):
													return
										yield path
							else:
										for dirpath, dirnames, filenames in os.walk(lowerCAmelCase_						):
													if os.path.basename(lowerCAmelCase_						).startswith((".", "__")						):
																continue
													dirnames.sort()
													for filename in sorted(lowerCAmelCase_						):
																if filename.startswith((".", "__")						):
																			continue
																yield os.path.join(lowerCAmelCase_	,	lowerCAmelCase_						)











"""simple docstring"""
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''t5'''
	__magic_name__	      =				['''past_key_values''']
	__magic_name__	      =				{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





	def __init__(      self					: str	,	lowerCAmelCase_					: List[Any]=32_128	,	lowerCAmelCase_					: Tuple=512	,	lowerCAmelCase_					: Optional[int]=64	,	lowerCAmelCase_					: List[str]=2_048	,	lowerCAmelCase_					: Tuple=6	,	lowerCAmelCase_					: Dict=None	,	lowerCAmelCase_					: str=8	,	lowerCAmelCase_					: Optional[int]=32	,	lowerCAmelCase_					: Dict=128	,	lowerCAmelCase_					: Optional[Any]=0.1	,	lowerCAmelCase_					: str=1e-6	,	lowerCAmelCase_					: Dict=1.0	,	lowerCAmelCase_					: str="relu"	,	lowerCAmelCase_					: List[Any]=True	,	lowerCAmelCase_					: List[str]=True	,	lowerCAmelCase_					: Any=0	,	lowerCAmelCase_					: Tuple=1	,	**lowerCAmelCase_					: Optional[int]	,	)						->			int:
				UpperCAmelCase_     : int			     =					vocab_size
				UpperCAmelCase_     : Optional[Any]			     =					d_model
				UpperCAmelCase_     : str			     =					d_kv
				UpperCAmelCase_     : Any			     =					d_ff
				UpperCAmelCase_     : int			     =					num_layers
				UpperCAmelCase_     : Union[str, Any]			     =					(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				UpperCAmelCase_     : Optional[Any]			     =					num_heads
				UpperCAmelCase_     : Any			     =					relative_attention_num_buckets
				UpperCAmelCase_     : Optional[Any]			     =					relative_attention_max_distance
				UpperCAmelCase_     : Optional[Any]			     =					dropout_rate
				UpperCAmelCase_     : Tuple			     =					layer_norm_epsilon
				UpperCAmelCase_     : int			     =					initializer_factor
				UpperCAmelCase_     : int			     =					feed_forward_proj
				UpperCAmelCase_     : str			     =					use_cache

				UpperCAmelCase_     : Tuple			     =					self.feed_forward_proj.split("-"						)
				UpperCAmelCase_     : List[Any]			     =					act_info[-1]
				UpperCAmelCase_     : Optional[int]			     =					act_info[0] == "gated"

				if len(lowerCAmelCase_						) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_						) > 2:
							raise ValueError(
							    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
							    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
							    "'gated-gelu' or 'relu'"						)

				# for backwards compatibility
				if feed_forward_proj == "gated-gelu":
							UpperCAmelCase_     : int			     =					"gelu_new"

				super().__init__(
				    pad_token_id=lowerCAmelCase_	,	eos_token_id=lowerCAmelCase_	,	is_encoder_decoder=lowerCAmelCase_	,	**lowerCAmelCase_	,	)







class 			UpperCamelCase_       (__A     ):





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Mapping[str, Mapping[int, str]]:
				UpperCAmelCase_     : Any			     =					{
				    "input_ids": {0: "batch", 1: "encoder_sequence"},
				    "attention_mask": {0: "batch", 1: "encoder_sequence"},
				}
				if self.use_past:
							UpperCAmelCase_     : List[Any]			     =					"past_encoder_sequence + sequence"
							UpperCAmelCase_     : Union[str, Any]			     =					{0: "batch"}
							UpperCAmelCase_     : Optional[Any]			     =					{0: "batch", 1: "past_decoder_sequence + sequence"}
				else:
							UpperCAmelCase_     : List[Any]			     =					{0: "batch", 1: "decoder_sequence"}
							UpperCAmelCase_     : Tuple			     =					{0: "batch", 1: "decoder_sequence"}

				if self.use_past:
							self.fill_with_past_key_values_(lowerCAmelCase_	,	direction="inputs"						)

				return common_inputs





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			int:
				return 13



