


import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard







def 							lowerCAmelCase_	(   __lowerCamelCase      ):
							assert "mock" in _fsspec_registry
							assert "bz2" in _fsspec_registry







def 							lowerCAmelCase_	(   ):
							assert "mock" not in _fsspec_registry
							assert "bz2" in _fsspec_registry







def 							lowerCAmelCase_	(   ):
							__snake_case						:     Any									=  "mock-s3-bucket"
							__snake_case						:     str									=  F's3://{mock_bucket}'
							__snake_case						:     Optional[Any]									=  extract_path_from_uri(__lowerCamelCase      )
							assert dataset_path.startswith("s3://"      ) is False

							__snake_case						:     Tuple									=  "./local/path"
							__snake_case						:     Tuple									=  extract_path_from_uri(__lowerCamelCase      )
							assert dataset_path == new_dataset_path







def 							lowerCAmelCase_	(   __lowerCamelCase      ):
							__snake_case						:     Tuple									=  is_remote_filesystem(__lowerCamelCase      )
							assert is_remote is True

							__snake_case						:     Tuple									=  fsspec.filesystem("file"      )

							__snake_case						:     Optional[int]									=  is_remote_filesystem(__lowerCamelCase      )
							assert is_remote is False







@pytest.mark.parametrize("compression_fs_class"				,      __lowerCamelCase      )
def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase      ):
							__snake_case						:     Tuple									=  {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
							__snake_case						:     Union[str, Any]									=  input_paths[compression_fs_class.protocol]
							if input_path is None:
														__snake_case						:     Tuple									=  F'for \'{compression_fs_class.protocol}\' compression protocol, '
														if compression_fs_class.protocol == "lz4":
																					reason += require_lza.kwargs["reason"]
														elif compression_fs_class.protocol == "zstd":
																					reason += require_zstandard.kwargs["reason"]
														pytest.skip(__lowerCamelCase      )
							__snake_case						:     int									=  fsspec.filesystem(compression_fs_class.protocol				,      fo=__lowerCamelCase      )
							assert isinstance(__lowerCamelCase				,      __lowerCamelCase      )
							__snake_case						:     Optional[Any]									=  os.path.basename(__lowerCamelCase      )
							__snake_case						:     Dict									=  expected_filename[: expected_filename.rindex("."      )]
							assert fs.glob("*"      ) == [expected_filename]
							with fs.open(__lowerCamelCase				,      "r"				,      encoding="utf-8"      ) as f, open(__lowerCamelCase				,      encoding="utf-8"      ) as expected_file:
														assert f.read() == expected_file.read()







@pytest.mark.parametrize("protocol"				,      ["zip", "gzip"]      )
def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase      ):
							__snake_case						:     Union[str, Any]									=  {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
							__snake_case						:     int									=  compressed_file_paths[protocol]
							__snake_case						:     str									=  "dataset.jsonl"
							__snake_case						:     List[str]									=  F'{protocol}://{member_file_path}::{compressed_file_path}'
							__snake_case					,     *__snake_case						:     Dict									=  fsspec.get_fs_token_paths(__lowerCamelCase      )
							assert fs.isfile(__lowerCamelCase      )
							assert not fs.isfile("non_existing_" + member_file_path      )







@pytest.mark.integration
def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase      ):
							__snake_case						:     Optional[Any]									=  hf_api.dataset_info(__lowerCamelCase				,      token=__lowerCamelCase      )
							__snake_case						:     List[str]									=  HfFileSystem(repo_info=__lowerCamelCase				,      token=__lowerCamelCase      )
							assert sorted(hffs.glob("*"      )      ) == [".gitattributes", "data"]
							assert hffs.isdir("data"      )
							assert hffs.isfile(".gitattributes"      ) and hffs.isfile("data/text_data.txt"      )
							with open(__lowerCamelCase      ) as f:
														assert hffs.open("data/text_data.txt"				,      "r"      ).read() == f.read()







def 							lowerCAmelCase_	(   ):
							__snake_case						:     Optional[int]									=  "bz2"

							# Import module
							import datasets.filesystems

							# Overwrite protocol and reload
							register_implementation(__lowerCamelCase				,      __lowerCamelCase				,      clobber=__lowerCamelCase      )
							with pytest.warns(__lowerCamelCase      ) as warning_info:
														importlib.reload(datasets.filesystems      )

							assert len(__lowerCamelCase      ) == 1
							assert (
							    str(warning_info[0].message      )
							    == F'A filesystem protocol was already set for {protocol} and will be overwritten.'
							)









"""simple docstring"""
from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase       =				logging.get_logger(__name__)

_lowerCAmelCase       =				{
    "huggingface/informer-tourism-monthly": (
        "https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Optional[Any] 			=					"""informer"""
	_A							:					Dict 			=					{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	    """num_hidden_layers""": """encoder_layers""",
	}





	def __init__(self  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = "student_t"  ,  lowerCAmelCase_ = "nll"  ,  lowerCAmelCase_ = 1  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = "mean"  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = 64  ,  lowerCAmelCase_ = 32  ,  lowerCAmelCase_ = 32  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = True  ,  lowerCAmelCase_ = "gelu"  ,  lowerCAmelCase_ = 0.05  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 100  ,  lowerCAmelCase_ = 0.02  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_ = "prob"  ,  lowerCAmelCase_ = 5  ,  lowerCAmelCase_ = True  ,  **lowerCAmelCase_  ,  ):
		# time series specific configuration
		A_		:    Optional[Any]						     = prediction_length
		A_		:    Dict						     = context_length or prediction_length
		A_		:    Dict						     = distribution_output
		A_		:    Tuple						     = loss
		A_		:    Dict						     = input_size
		A_		:    Union[str, Any]						     = num_time_features
		A_		:    List[str]						     = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
		A_		:    Optional[int]						     = scaling
		A_		:    Optional[Any]						     = num_dynamic_real_features
		A_		:    Tuple						     = num_static_real_features
		A_		:    Tuple						     = num_static_categorical_features

		# set cardinality
		if cardinality and num_static_categorical_features > 0:
			if len(lowerCAmelCase_				) != num_static_categorical_features:
				raise ValueError(
				    """The cardinality should be a list of the same length as `num_static_categorical_features`"""				)
			A_		:    List[str]						     = cardinality
		else:
			A_		:    List[Any]						     = [0]

		# set embedding_dimension
		if embedding_dimension and num_static_categorical_features > 0:
			if len(lowerCAmelCase_				) != num_static_categorical_features:
				raise ValueError(
				    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""				)
			A_		:    int						     = embedding_dimension
		else:
			A_		:    Optional[int]						     = [min(50  ,  (cat + 1) // 2				) for cat in self.cardinality]

		A_		:    Optional[int]						     = num_parallel_samples

		# Transformer architecture configuration
		A_		:    Union[str, Any]						     = input_size * len(self.lags_sequence				) + self._number_of_features
		A_		:    Dict						     = d_model
		A_		:    Dict						     = encoder_attention_heads
		A_		:    Dict						     = decoder_attention_heads
		A_		:    Any						     = encoder_ffn_dim
		A_		:    Tuple						     = decoder_ffn_dim
		A_		:    Tuple						     = encoder_layers
		A_		:    Optional[int]						     = decoder_layers

		A_		:    List[str]						     = dropout
		A_		:    List[str]						     = attention_dropout
		A_		:    Any						     = activation_dropout
		A_		:    Any						     = encoder_layerdrop
		A_		:    List[Any]						     = decoder_layerdrop

		A_		:    str						     = activation_function
		A_		:    Optional[Any]						     = init_std

		A_		:    Optional[int]						     = use_cache

		# Informer
		A_		:    Dict						     = attention_type
		A_		:    List[Any]						     = sampling_factor
		A_		:    List[Any]						     = distil

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





	@property
	def  lowerCamelCase(self				):
		return (
		    sum(self.embedding_dimension				)
		    + self.num_dynamic_real_features
		    + self.num_time_features
		    + self.num_static_real_features
		    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
		)

