
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser






def   lowerCAmelCase_						(					_snake_case      :  int=None      ,   _snake_case      :  Union[str, Any]=None   )				->		Union[str, Any]:







	'''simple docstring'''


	return field(default_factory=lambda: default      ,   metadata=_snake_case   )





@dataclass
class       _snake_case					:
			UpperCamelCase__          =    field(
			    metadata={'help': 'The csv file to plot.'}				,						)
			UpperCamelCase__          =    field(
			    default=snake_case				,						metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}				,						)
			UpperCamelCase__          =    field(
			    default=snake_case				,						metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}				,						)
			UpperCamelCase__          =    field(
			    default=snake_case				,						metadata={'help': 'Disable logarithmic scale when plotting'}				,						)
			UpperCamelCase__          =    field(
			    default=snake_case				,						metadata={
			        'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
			    }				,						)
			UpperCamelCase__          =    field(
			    default=snake_case				,						metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}				,						)
			UpperCamelCase__          =    list_field(
			    default=snake_case				,						metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}				)







def   lowerCAmelCase_						(					_snake_case      :  int   )				->		str:







	'''simple docstring'''


	try:
		int(_snake_case   )
		return True
	except ValueError:
		return False






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]   )				->		Union[str, Any]:







	'''simple docstring'''


	try:
		float(_snake_case   )
		return True
	except ValueError:
		return False





class       _snake_case					:







			def __init__(				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     args
				__magic_name__		:		List[str]	   =     defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

				with open(self.args.csv_file						,   newline=""		) as csv_file:
					__magic_name__		:		Optional[Any]	   =     csv.DictReader(_a		)
					for row in reader:
						__magic_name__		:		Any	   =     row["model"]
						self.result_dict[model_name]["bsz"].append(int(row["batch_size"]		)		)
						self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]		)		)
						if can_convert_to_int(row["result"]		):
							# value is not None
							__magic_name__		:		List[Any]	   =     int(row["result"]		)
						elif can_convert_to_float(row["result"]		):
							# value is not None
							__magic_name__		:		Dict	   =     float(row["result"]		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__   ,    __magic_name__		:		Tuple	   =     plt.subplots()
				__magic_name__		:		Optional[int]	   =     "Time usage" if self.args.is_time else "Memory usage"
				__magic_name__		:		int	   =     title_str + " for training" if self.args.is_train else title_str + " for inference"

				if not self.args.no_log_scale:
					# set logarithm scales
					ax.set_xscale("log"		)
					ax.set_yscale("log"		)

				for axis in [ax.xaxis, ax.yaxis]:
					axis.set_major_formatter(ScalarFormatter()		)

				for model_name_idx, model_name in enumerate(self.result_dict.keys()		):
					__magic_name__		:		int	   =     sorted(set(self.result_dict[model_name]["bsz"]		)		)
					__magic_name__		:		int	   =     sorted(set(self.result_dict[model_name]["seq_len"]		)		)
					__magic_name__		:		List[Any]	   =     self.result_dict[model_name]["result"]

					((__magic_name__)   ,    (__magic_name__))		:		str	   =     (
					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
					)

					__magic_name__		:		str	   =     (
					    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
					)

					for inner_loop_value in inner_loop_array:
						if self.args.plot_along_batch:
							__magic_name__		:		List[Any]	   =     np.asarray(
							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]						,   dtype=_a						,   )
						else:
							__magic_name__		:		Union[str, Any]	   =     np.asarray(
							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]						,   dtype=np.floataa						,   )

						((__magic_name__)   ,    (__magic_name__))		:		int	   =     (
						    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
						)

						__magic_name__		:		Dict	   =     np.asarray(_a						,   _a		)[: len(_a		)]
						plt.scatter(
						    _a						,   _a						,   label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''		)
						plt.plot(_a						,   _a						,   "--"		)

					title_str += f''' {label_model_name} vs.'''

				__magic_name__		:		Tuple	   =     title_str[:-4]
				__magic_name__		:		Dict	   =     "Time in s" if self.args.is_time else "Memory in MB"

				# plot
				plt.title(_a		)
				plt.xlabel(_a		)
				plt.ylabel(_a		)
				plt.legend()

				if self.args.figure_png_file is not None:
					plt.savefig(self.args.figure_png_file		)
				else:
					plt.show()







def   lowerCAmelCase_						(					)				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		Any	   =     HfArgumentParser(_snake_case   )
	__magic_name__		:		List[str]	   =     parser.parse_args_into_dataclasses()[0]
	__magic_name__		:		Dict	   =     Plot(args=_snake_case   )
	plot.plot()


if __name__ == "__main__":
				main()


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_						(					_snake_case      :  List[Any]   )				->		List[Any]:







	'''simple docstring'''


	assert "mock" in _fsspec_registry
	assert "bz2" in _fsspec_registry






def   lowerCAmelCase_						(					)				->		Tuple:







	'''simple docstring'''


	assert "mock" not in _fsspec_registry
	assert "bz2" in _fsspec_registry






def   lowerCAmelCase_						(					)				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		Dict	   =     "mock-s3-bucket"
	__magic_name__		:		Any	   =     F'''s3://{mock_bucket}'''
	__magic_name__		:		str	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path.startswith("s3://"   ) is False

	__magic_name__		:		Tuple	   =     "./local/path"
	__magic_name__		:		Optional[Any]	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path == new_dataset_path






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		Optional[Any]:







	'''simple docstring'''


	__magic_name__		:		str	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is True

	__magic_name__		:		Optional[int]	   =     fsspec.filesystem("file"   )

	__magic_name__		:		int	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is False






@pytest.mark.parametrize("compression_fs_class"      ,   _snake_case   )
def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  int      ,   _snake_case      :  Tuple      ,   _snake_case      :  Any      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  Any   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Any	   =     {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
	__magic_name__		:		str	   =     input_paths[compression_fs_class.protocol]
	if input_path is None:
		__magic_name__		:		Dict	   =     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(_snake_case   )
	__magic_name__		:		str	   =     fsspec.filesystem(compression_fs_class.protocol      ,   fo=_snake_case   )
	assert isinstance(_snake_case      ,   _snake_case   )
	__magic_name__		:		int	   =     os.path.basename(_snake_case   )
	__magic_name__		:		Optional[int]	   =     expected_filename[: expected_filename.rindex("."   )]
	assert fs.glob("*"   ) == [expected_filename]
	with fs.open(_snake_case      ,   "r"      ,   encoding="utf-8"   ) as f, open(_snake_case      ,   encoding="utf-8"   ) as expected_file:
		assert f.read() == expected_file.read()






@pytest.mark.parametrize("protocol"      ,   ["zip", "gzip"]   )
def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  Optional[Any]   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
	__magic_name__		:		int	   =     compressed_file_paths[protocol]
	__magic_name__		:		Tuple	   =     "dataset.jsonl"
	__magic_name__		:		List[str]	   =     F'''{protocol}://{member_file_path}::{compressed_file_path}'''
	__magic_name__   ,    *__magic_name__		:		Optional[Any]	   =     fsspec.get_fs_token_paths(_snake_case   )
	assert fs.isfile(_snake_case   )
	assert not fs.isfile("non_existing_" + member_file_path   )






@pytest.mark.integration
def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Dict      ,   _snake_case      :  List[str]      ,   _snake_case      :  Tuple   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     hf_api.dataset_info(_snake_case      ,   token=_snake_case   )
	__magic_name__		:		Optional[Any]	   =     HfFileSystem(repo_info=_snake_case      ,   token=_snake_case   )
	assert sorted(hffs.glob("*"   )   ) == [".gitattributes", "data"]
	assert hffs.isdir("data"   )
	assert hffs.isfile(".gitattributes"   ) and hffs.isfile("data/text_data.txt"   )
	with open(_snake_case   ) as f:
		assert hffs.open("data/text_data.txt"      ,   "r"   ).read() == f.read()






def   lowerCAmelCase_						(					)				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		Optional[Any]	   =     "bz2"

	# Import module
	import datasets.filesystems

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

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