



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: Tuple							=						get_logger(__name__)

class 							a__:
    lowercase__			=   """dummy_data"""
    lowercase__			=   """datasets"""
    lowercase__			=   False







    def __init__(				self			:  List[Any]						,      __snake_case			:  str						,      __snake_case			:  str						,      __snake_case			:  Union[Version, str]						,      __snake_case			:  Optional[str] = None						,      __snake_case			:  bool = False						,      __snake_case			:  bool = True						,      __snake_case			:  Optional[List[Callable]] = None						,      ):
      a						: Optional[int]					       =					0
      a						: List[str]					       =					dataset_name
      a						: List[Any]					       =					cache_dir
      a						: Optional[int]					       =					use_local_dummy_data
      a						: Any					       =					config
      # download_callbacks take a single url as input
      a						: 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
      a						: Union[str, Any]					       =					load_existing_dummy_data

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







    @property
    def       lowercase_	(				self			:  Any     ):
      if self._dummy_file is None:
        a						: Union[str, Any]					       =					self.download_dummy_data()
      return self._dummy_file







    @property
    def       lowercase_	(				self			:  List[Any]     ):
      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       lowercase_	(				self			:  str     ):
      return os.path.join(self.dummy_data_folder						,      'dummy_data.zip'     )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: Tuple					       =					(
          self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
      )

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

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







    @property
    def       lowercase_	(				self			:  List[str]     ):
      return os.path.join(self.datasets_scripts_dir						,      self.dataset_name						,      self.dummy_zip_file     )







    @property
    def       lowercase_	(				self			:  Optional[int]     ):
      if self._bucket_url is None:
        a						: Optional[Any]					       =					hf_github_url(self.dataset_name						,      self.dummy_zip_file.replace(os.sep						,      '/'     )     )
      return self._bucket_url







    @property
    def       lowercase_	(				self			:  Union[str, 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       lowercase_	(				self			:  Tuple						,      __snake_case			:  str						,      *__snake_case			:  int     ):
      if self.load_existing_dummy_data:
        # dummy data is downloaded and tested
        a						: Tuple					       =					self.dummy_file
      else:
        # dummy data cannot be downloaded and only the path to dummy file is returned
        a						: Optional[Any]					       =					self.dummy_file_name

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







    def       lowercase_	(				self			:  Any						,      __snake_case			:  int						,      *__snake_case			:  int     ):
      return self.download_and_extract(__snake_case     )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  List[Any]						,      __snake_case			:  Union[str, Any]     ):
      return self.download_and_extract(__snake_case     )







    def       lowercase_	(				self			:  int						,      __snake_case			:  int						,      *__snake_case			:  Dict						,      **__snake_case			:  str     ):
      return path







    def       lowercase_	(				self			:  Optional[int]     ):
      return {}







    def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  List[Any]						,      __snake_case			:  Optional[Any]     ):
      a						: str					       =					{}
      for key, single_urls in data_url.items():
        for download_callback in self.download_callbacks:
          if isinstance(__snake_case						,      __snake_case     ):
            for single_url in single_urls:
              download_callback(__snake_case     )
          else:
            a						: str					       =					single_urls
            download_callback(__snake_case     )
            # 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(__snake_case						,      __snake_case     ):
          a						: int					       =					[os.path.join(__snake_case						,      urllib.parse.quote_plus(Path(__snake_case     ).name     )     ) for x in single_urls]
        else:
          a						: Union[str, Any]					       =					single_urls
          a						: Tuple					       =					os.path.join(__snake_case						,      urllib.parse.quote_plus(Path(__snake_case     ).name     )     )
        a						: Optional[int]					       =					value

      # make sure that values are unique
      if all(isinstance(__snake_case						,      __snake_case     ) 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
        a						: Any					       =					{key: value + key for key, value in dummy_data_dict.items()}

      return dummy_data_dict







    def       lowercase_	(				self			:  str						,      __snake_case			:  int						,      __snake_case			:  str     ):
      a						: Union[str, Any]					       =					[]
      # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
      a						: Optional[Any]					       =					all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}'						,      __snake_case     )     ) for url in data_url     )
      a						: 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):
        a						: List[Any]					       =					[data_url[0]] * len(__snake_case     )
      for single_url in data_url:
        for download_callback in self.download_callbacks:
          download_callback(__snake_case     )
        # 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
        a						: List[str]					       =					os.path.join(__snake_case						,      urllib.parse.quote_plus(single_url.split('/'     )[-1]     )     )
        dummy_data_list.append(__snake_case     )
      return dummy_data_list







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  List[str]     ):
      for download_callback in self.download_callbacks:
        download_callback(__snake_case     )
      # 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
      a						: Optional[int]					       =					os.path.join(__snake_case						,      urllib.parse.quote_plus(data_url.split('/'     )[-1]     )     )
      if os.path.exists(__snake_case     ) 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       lowercase_	(				self			:  Optional[int]     ):
      pass







    def       lowercase_	(				self			:  Union[str, Any]     ):
      pass







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

      a						: Union[str, Any]					       =					Path(__snake_case     )
      a						: List[Any]					       =					_iter_archive_members(__snake_case     ) 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(__snake_case     ).as_posix(), file_path.open('rb'     )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  List[Any]     ):
      if not isinstance(__snake_case						,      __snake_case     ):
        a						: Union[str, Any]					       =					[paths]
      for path in paths:
        if os.path.isfile(__snake_case     ):
          if os.path.basename(__snake_case     ).startswith(('.', '__')     ):
            return
          yield path
        else:
          for dirpath, dirnames, filenames in os.walk(__snake_case     ):
            if os.path.basename(__snake_case     ).startswith(('.', '__')     ):
              continue
            dirnames.sort()
            for filename in sorted(__snake_case     ):
              if filename.startswith(('.', '__')     ):
                continue
              yield os.path.join(__snake_case						,      __snake_case     )




'''simple docstring'''






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__	(						_A=None			,      _A=None    ):
  return field(default_factory=lambda: default			,      metadata=_A    )

@dataclass
class 							a__:
    lowercase__			=   field(
        metadata={"""help""": """The csv file to plot."""}		,			)
    lowercase__			=   field(
        default=lowerCamelCase__		,			metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}		,			)
    lowercase__			=   field(
        default=lowerCamelCase__		,			metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}		,			)
    lowercase__			=   field(
        default=lowerCamelCase__		,			metadata={"""help""": """Disable logarithmic scale when plotting"""}		,			)
    lowercase__			=   field(
        default=lowerCamelCase__		,			metadata={
            """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
        }		,			)
    lowercase__			=   field(
        default=lowerCamelCase__		,			metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}		,			)
    lowercase__			=   list_field(
        default=lowerCamelCase__		,			metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}     )
def 				lowerCamelCase__	(						_A    ):
  try:
    int(_A    )
    return True
  except ValueError:
    return False
def 				lowerCamelCase__	(						_A    ):
  try:
    float(_A    )
    return True
  except ValueError:
    return False

class 							a__:







    def __init__(				self			:  Union[str, Any]						,      __snake_case			:  Optional[int]     ):
      a						: int					       =					args
      a						: Dict					       =					defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}     )

      with open(self.args.csv_file						,      newline=''     ) as csv_file:
        a						: List[Any]					       =					csv.DictReader(__snake_case     )
        for row in reader:
          a						: Dict					       =					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
            a						: Dict					       =					int(row['result']     )
          elif can_convert_to_float(row['result']     ):
            # value is not None
            a						: int					       =					float(row['result']     )







    def       lowercase_	(				self			:  int     ):
      a       ,     a						: Dict					       =					plt.subplots()
      a						: int					       =					'Time usage' if self.args.is_time else 'Memory usage'
      a						: 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()     ):
        a						: Dict					       =					sorted(set(self.result_dict[model_name]['bsz']     )     )
        a						: Any					       =					sorted(set(self.result_dict[model_name]['seq_len']     )     )
        a						: Any					       =					self.result_dict[model_name]['result']

        ((a)       ,     (a))						: Optional[Any]					       =					(
            (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
        )

        a						: int					       =					(
            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:
            a						: Dict					       =					np.asarray(
                [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]						,      dtype=__snake_case						,      )
          else:
            a						: int					       =					np.asarray(
                [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]						,      dtype=np.floataa						,      )

          ((a)       ,     (a))						: Optional[int]					       =					(
              ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
          )

          a						: List[str]					       =					np.asarray(__snake_case						,      __snake_case     )[: len(__snake_case     )]
          plt.scatter(
              __snake_case						,      __snake_case						,      label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""     )
          plt.plot(__snake_case						,      __snake_case						,      '--'     )

        title_str += F""" {label_model_name} vs."""

      a						: List[Any]					       =					title_str[:-4]
      a						: Optional[Any]					       =					'Time in s' if self.args.is_time else 'Memory in MB'

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

      if self.args.figure_png_file is not None:
        plt.savefig(self.args.figure_png_file     )
      else:
        plt.show()
def 				lowerCamelCase__	(						):
  a						: Dict					       =					HfArgumentParser(_A    )
  a						: List[str]					       =					parser.parse_args_into_dataclasses()[0]
  a						: Dict					       =					Plot(args=_A    )
  plot.plot()


if __name__ == "__main__":
     main()