


import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
       from .features import FeatureType


__lowerCamelCase  ,       __lowerCamelCase  ,       __lowerCamelCase        =     False, False, False


@dataclass
class   a__	:
       lowerCamelCase__:     Optional[int]							    =     None
       lowerCamelCase__:     bool							    =     True
       lowerCamelCase__:     bool							    =     True
       lowerCamelCase__:     Optional[str]							    =     None
       # Automatically constructed
       lowerCamelCase__:     ClassVar[str]							    =     "dict"
       lowerCamelCase__:     ClassVar[Any]							    =     pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}    )
       lowerCamelCase__:     str							    =     field(default="""Audio"""						,       init=a__						,       repr=a__    )

       def __call__(   self      :     List[str]    ):
            return self.pa_type

       def 						UpperCAmelCase(   self      :     Any      ,							lowerCamelCase_      :     Optional[Any]    ):
            try:
                 import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
            except ImportError as err:
                 raise ImportError("""To support encoding audio data, please install \'soundfile\'."""    ) from err
            if isinstance(lowerCamelCase_      ,							lowerCamelCase_    ):
                 return {"bytes": None, "path": value}
            elif isinstance(lowerCamelCase_      ,							lowerCamelCase_    ):
                 return {"bytes": value, "path": None}
            elif "array" in value:
                 # convert the audio array to wav bytes
                 a_    :     Optional[int]										=    BytesIO()
                 sf.write(lowerCamelCase_      ,							value["""array"""]      ,							value["""sampling_rate"""]      ,							format="""wav"""    )
                 return {"bytes": buffer.getvalue(), "path": None}
            elif value.get("""path"""    ) is not None and os.path.isfile(value["""path"""]    ):
                 # we set "bytes": None to not duplicate the data if they're already available locally
                 if value["path"].endswith("""pcm"""    ):
                      # "PCM" only has raw audio bytes
                      if value.get("""sampling_rate"""    ) is None:
                           # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
                           raise KeyError("""To use PCM files, please specify a \'sampling_rate\' in Audio object"""    )
                      if value.get("""bytes"""    ):
                           # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
                           a_    :     Tuple										=    np.frombuffer(value["""bytes"""]      ,							dtype=np.intaa    ).astype(np.floataa    ) / 3_2_7_6_7
                      else:
                           a_    :     str										=    np.memmap(value["""path"""]      ,							dtype="""h"""      ,							mode="""r"""    ).astype(np.floataa    ) / 3_2_7_6_7

                      a_    :     Optional[int]										=    BytesIO(bytes()    )
                      sf.write(lowerCamelCase_      ,							lowerCamelCase_      ,							value["""sampling_rate"""]      ,							format="""wav"""    )
                      return {"bytes": buffer.getvalue(), "path": None}
                 else:
                      return {"bytes": None, "path": value.get("""path"""    )}
            elif value.get("""bytes"""    ) is not None or value.get("""path"""    ) is not None:
                 # store the audio bytes, and path is used to infer the audio format using the file extension
                 return {"bytes": value.get("""bytes"""    ), "path": value.get("""path"""    )}
            else:
                 raise ValueError(
                     F'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''    )

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Union[str, Any] = None    ):
            if not self.decode:
                 raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead."""    )

            a_			,			a_    :     int										=    (value["""path"""], BytesIO(value["""bytes"""]    )) if value["""bytes"""] is not None else (value["""path"""], None)
            if path is None and file is None:
                 raise ValueError(F'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.'''    )

            try:
                 import librosa
                 import soundfile as sf
            except ImportError as err:
                 raise ImportError("""To support decoding audio files, please install \'librosa\' and \'soundfile\'."""    ) from err

            a_    :     List[Any]										=    xsplitext(lowerCamelCase_    )[1][1:].lower() if path is not None else None
            if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
                 raise RuntimeError(
                     """Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, """
                     """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """    )
            elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
                 raise RuntimeError(
                     """Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, """
                     """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """    )

            if file is None:
                 a_    :     int										=    token_per_repo_id or {}
                 a_    :     Union[str, Any]										=    path.split("""::"""    )[-1]
                 try:
                      a_    :     Optional[int]										=    string_to_dict(lowerCamelCase_      ,							config.HUB_DATASETS_URL    )["""repo_id"""]
                      a_    :     Union[str, Any]										=    token_per_repo_id[repo_id]
                 except (ValueError, KeyError):
                      a_    :     Union[str, Any]										=    None

                 with xopen(lowerCamelCase_      ,							"""rb"""      ,							use_auth_token=lowerCamelCase_    ) as f:
                      a_			,			a_    :     int										=    sf.read(lowerCamelCase_    )

            else:
                 a_			,			a_    :     Dict										=    sf.read(lowerCamelCase_    )

            a_    :     Tuple										=    array.T
            if self.mono:
                 a_    :     List[Any]										=    librosa.to_mono(lowerCamelCase_    )
            if self.sampling_rate and self.sampling_rate != sampling_rate:
                 a_    :     Tuple										=    librosa.resample(lowerCamelCase_      ,							orig_sr=lowerCamelCase_      ,							target_sr=self.sampling_rate    )
                 a_    :     Optional[int]										=    self.sampling_rate

            return {"path": path, "array": array, "sampling_rate": sampling_rate}

       def 						UpperCAmelCase(   self      :     Tuple    ):
            from .features import Value

            if self.decode:
                 raise ValueError("""Cannot flatten a decoded Audio feature."""    )
            return {
                "bytes": Value("""binary"""    ),
                "path": Value("""string"""    ),
            }

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     Optional[Any]    ):
            if pa.types.is_string(storage.type    ):
                 a_    :     Dict										=    pa.array([None] * len(lowerCamelCase_    )      ,							type=pa.binary()    )
                 a_    :     int										=    pa.StructArray.from_arrays([bytes_array, storage]      ,							["""bytes""", """path"""]      ,							mask=storage.is_null()    )
            elif pa.types.is_binary(storage.type    ):
                 a_    :     Union[str, Any]										=    pa.array([None] * len(lowerCamelCase_    )      ,							type=pa.string()    )
                 a_    :     List[Any]										=    pa.StructArray.from_arrays([storage, path_array]      ,							["""bytes""", """path"""]      ,							mask=storage.is_null()    )
            elif pa.types.is_struct(storage.type    ) and storage.type.get_all_field_indices("""array"""    ):
                 a_    :     List[str]										=    pa.array([Audio().encode_example(lowerCamelCase_    ) if x is not None else None for x in storage.to_pylist()]    )
            elif pa.types.is_struct(storage.type    ):
                 if storage.type.get_field_index("""bytes"""    ) >= 0:
                      a_    :     Optional[int]										=    storage.field("""bytes"""    )
                 else:
                      a_    :     Any										=    pa.array([None] * len(lowerCamelCase_    )      ,							type=pa.binary()    )
                 if storage.type.get_field_index("""path"""    ) >= 0:
                      a_    :     Tuple										=    storage.field("""path"""    )
                 else:
                      a_    :     Tuple										=    pa.array([None] * len(lowerCamelCase_    )      ,							type=pa.string()    )
                 a_    :     Dict										=    pa.StructArray.from_arrays([bytes_array, path_array]      ,							["""bytes""", """path"""]      ,							mask=storage.is_null()    )
            return array_cast(lowerCamelCase_      ,							self.pa_type    )

       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     str    ):

            @no_op_if_value_is_null
            def path_to_bytes(lowerCamelCase_      :     Dict    ):
                 with xopen(lowerCamelCase_      ,							"""rb"""    ) as f:
                      a_    :     Dict										=    f.read()
                 return bytes_

            a_    :     str										=    pa.array(
                [
                    (path_to_bytes(x["""path"""]    ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
                    for x in storage.to_pylist()
                ]      ,							type=pa.binary()      ,							)
            a_    :     Union[str, Any]										=    pa.array(
                [os.path.basename(lowerCamelCase_    ) if path is not None else None for path in storage.field("""path"""    ).to_pylist()]      ,							type=pa.string()      ,							)
            a_    :     List[Any]										=    pa.StructArray.from_arrays([bytes_array, path_array]      ,							["""bytes""", """path"""]      ,							mask=bytes_array.is_null()    )
            return array_cast(lowerCamelCase_      ,							self.pa_type    )







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  _a			(						__UpperCamelCase=None  ,  __UpperCamelCase=None ):
     return field(default_factory=lambda: default  ,  metadata=__UpperCamelCase )


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



def  _a			(						__UpperCamelCase ):
     try:
          int(__UpperCamelCase )
          return True
     except ValueError:
          return False



def  _a			(						__UpperCamelCase ):
     try:
          float(__UpperCamelCase )
          return True
     except ValueError:
          return False


class   a__	:

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

            with open(self.args.csv_file      ,							newline=""""""    ) as csv_file:
                 a_    :     List[Any]										=    csv.DictReader(lowerCamelCase_    )
                 for row in reader:
                      a_    :     List[str]										=    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_    :     Optional[Any]										=    int(row["""result"""]    )
                      elif can_convert_to_float(row["""result"""]    ):
                           # value is not None
                           a_    :     Dict										=    float(row["""result"""]    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            a_			,			a_    :     Tuple										=    plt.subplots()
            a_    :     List[Any]										=    """Time usage""" if self.args.is_time else """Memory usage"""
            a_    :     Optional[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_    :     List[Any]										=    sorted(set(self.result_dict[model_name]["""bsz"""]    )    )
                 a_    :     Union[str, Any]										=    sorted(set(self.result_dict[model_name]["""seq_len"""]    )    )
                 a_    :     List[str]										=    self.result_dict[model_name]["""result"""]

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

                 a_    :     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:
                           a_    :     Optional[int]										=    np.asarray(
                               [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,							dtype=lowerCamelCase_      ,							)
                      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_))    :     List[str]										=    (
                          ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
                      )

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

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

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

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

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



def  _a			(						):
     a_    :     Tuple										=    HfArgumentParser(__UpperCamelCase )
     a_    :     Dict										=    parser.parse_args_into_dataclasses()[0]
     a_    :     Dict										=    Plot(args=__UpperCamelCase )
     plot.plot()


if __name__ == "__main__":
       main()



