





"""simple docstring"""






from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


A	     =					logging.get_logger(__name__)




@add_end_docstrings(_UpperCamelCase							)
class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					def __init__(  self		,     **_UpperCAmelCase       ):
									super().__init__(**_UpperCAmelCase       )

									if self.framework != "pt":
													raise ValueError(f"""The {self.__class__} is only available in PyTorch."""       )
									# No specific FOR_XXX available yet







					def __call__(  self		,     _UpperCAmelCase		,     **_UpperCAmelCase       ):
									return super().__call__(_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     **_UpperCAmelCase       ):
									__a	:  List[str]         =					{}
									if "candidate_labels" in kwargs:
													__a	:  int         =					kwargs['''candidate_labels''']
									if "hypothesis_template" in kwargs:
													__a	:  Tuple         =					kwargs['''hypothesis_template''']

									return preprocess_params, {}, {}







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=None		,     _UpperCAmelCase="This is a sound of {}."       ):
									if isinstance(_UpperCAmelCase		,     _UpperCAmelCase       ):
													if audio.startswith('''http://'''       ) or audio.startswith('''https://'''       ):
																	# We need to actually check for a real protocol, otherwise it's impossible to use a local file
																	# like http_huggingface_co.png
																	__a	:  Dict         =					requests.get(_UpperCAmelCase       ).content
													else:
																	with open(_UpperCAmelCase		,     '''rb'''       ) as f:
																					__a	:  Optional[Any]         =					f.read()

									if isinstance(_UpperCAmelCase		,     _UpperCAmelCase       ):
													__a	:  Any         =					ffmpeg_read(_UpperCAmelCase		,     self.feature_extractor.sampling_rate       )

									if not isinstance(_UpperCAmelCase		,     np.ndarray       ):
													raise ValueError('''We expect a numpy ndarray as input'''       )
									if len(audio.shape       ) != 1:
													raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline'''       )

									__a	:  Dict         =					self.feature_extractor(
									    [audio]		,     sampling_rate=self.feature_extractor.sampling_rate		,     return_tensors='''pt'''       )
									__a	:  List[Any]         =					candidate_labels
									__a	:  Tuple         =					[hypothesis_template.format(_UpperCAmelCase       ) for x in candidate_labels]
									__a	:  str         =					self.tokenizer(_UpperCAmelCase		,     return_tensors=self.framework		,     padding=_UpperCAmelCase       )
									__a	:  List[Any]         =					[text_inputs]
									return inputs







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  Any         =					model_inputs.pop('''candidate_labels'''       )
									__a	:  Union[str, Any]         =					model_inputs.pop('''text_inputs'''       )
									if isinstance(text_inputs[0]		,     _UpperCAmelCase       ):
													__a	:  Tuple         =					text_inputs[0]
									else:
													# Batching case.
													__a	:  Optional[int]         =					text_inputs[0][0]

									__a	:  str         =					self.model(**_UpperCAmelCase		,     **_UpperCAmelCase       )

									__a	:  List[str]         =					{
									    '''candidate_labels''': candidate_labels,
									    '''logits''': outputs.logits_per_audio,
									}
									return model_outputs







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  str         =					model_outputs.pop('''candidate_labels'''       )
									__a	:  Optional[int]         =					model_outputs['''logits'''][0]

									if self.framework == "pt":
													__a	:  List[Any]         =					logits.softmax(dim=0       )
													__a	:  List[Any]         =					probs.tolist()
									else:
													raise ValueError('''`tf` framework not supported.'''       )

									__a	:  Optional[int]         =					[
									    {'''score''': score, '''label''': candidate_label}
									    for score, candidate_label in sorted(zip(_UpperCAmelCase		,     _UpperCAmelCase       )		,     key=lambda _UpperCAmelCase       : -x[0]       )
									]
									return result






"""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       __A (     a_  :List[Any]=None				,    a_  :Tuple=None)						->						List[Any]:
				return field(default_factory=lambda: default				,    metadata=a_)




@dataclass
class 			__lowercase     :







					'''simple docstring'''




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






def       __A (     a_  :Optional[Any])						->						Any:
				try:
								int(a_)
								return True
				except ValueError:
								return False

def       __A (     a_  :List[Any])						->						Any:
				try:
								float(a_)
								return True
				except ValueError:
								return False




class 			__lowercase     :







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase       ):
									__a	:  Dict         =					args
									__a	:  Tuple         =					defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

									with open(self.args.csv_file		,     newline=''''''       ) as csv_file:
													__a	:  int         =					csv.DictReader(_UpperCAmelCase       )
													for row in reader:
																	__a	:  Union[str, 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
																					__a	:  Optional[int]         =					int(row['''result''']       )
																	elif can_convert_to_float(row['''result''']       ):
																					# value is not None
																					__a	:  Optional[Any]         =					float(row['''result''']       )







					def 	_lowerCamelCase						(  self       ):
									__a    ,  __a	:  Optional[int]         =					plt.subplots()
									__a	:  str         =					'''Time usage''' if self.args.is_time else '''Memory usage'''
									__a	:  str         =					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	:  str         =					sorted(set(self.result_dict[model_name]['''bsz''']       )       )
													__a	:  Dict         =					sorted(set(self.result_dict[model_name]['''seq_len''']       )       )
													__a	:  Dict         =					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	:  Any         =					(
													    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=_UpperCAmelCase		,     )
																	else:
																					__a	:  Dict         =					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))	:  Union[str, Any]         =					(
																	    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																	)

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

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

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

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

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






def       __A (     )						->						List[str]:
				__a	:  List[str]         =					HfArgumentParser(a_)
				__a	:  Optional[int]         =					parser.parse_args_into_dataclasses()[0]
				__a	:  Tuple         =					Plot(args=a_)
				plot.plot()


if __name__ == "__main__":
		main()