
"""simple docstring"""
import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_			      =					logging.get_logger(__name__)
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						'''semantic'''




							def __init__(				self  ,							snake_case_=10_000  ,							snake_case_=True  ,							snake_case_=768  ,							snake_case_=False  ,							snake_case_=False  ,							snake_case_=False  ,							snake_case_=False  ,							snake_case_=0.7  ,							snake_case_=True  ,							snake_case_=10_048  ,							snake_case_=129_595  ,							snake_case_=129_599  ,							snake_case_=10_000  ,							snake_case_=256  ,							snake_case_=49.9  ,							**snake_case_  ,							)   ->       int:
													super().__init__(
													    temperature=snake_case_  ,							do_sample=snake_case_  ,							eos_token_id=snake_case_  ,							renormalize_logits=snake_case_  ,							max_new_tokens=snake_case_  ,							output_scores=snake_case_  ,							return_dict_in_generate=snake_case_  ,							output_hidden_states=snake_case_  ,							output_attentions=snake_case_  ,							**snake_case_  ,							)

													__lowerCAmelCase        =       text_encoding_offset
													__lowerCAmelCase        =       text_pad_token
													__lowerCAmelCase        =       eos_token_id
													__lowerCAmelCase        =       semantic_infer_token
													__lowerCAmelCase        =       semantic_vocab_size
													__lowerCAmelCase        =       max_input_semantic_length
													__lowerCAmelCase        =       semantic_rate_hz




class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						'''coarse_acoustics'''




							def __init__(				self  ,							snake_case_=True  ,							snake_case_=False  ,							snake_case_=False  ,							snake_case_=False  ,							snake_case_=False  ,							snake_case_=0.7  ,							snake_case_=True  ,							snake_case_=12_048  ,							snake_case_=75  ,							snake_case_=2  ,							snake_case_=12_050  ,							snake_case_=256  ,							snake_case_ = 630  ,							snake_case_ = 60  ,							**snake_case_  ,							)   ->       int:
													super().__init__(
													    temperature=snake_case_  ,							do_sample=snake_case_  ,							renormalize_logits=snake_case_  ,							output_scores=snake_case_  ,							return_dict_in_generate=snake_case_  ,							output_hidden_states=snake_case_  ,							output_attentions=snake_case_  ,							**snake_case_  ,							)

													__lowerCAmelCase        =       coarse_semantic_pad_token
													__lowerCAmelCase        =       coarse_rate_hz
													__lowerCAmelCase        =       n_coarse_codebooks
													__lowerCAmelCase        =       coarse_infer_token
													__lowerCAmelCase        =       max_coarse_input_length
													__lowerCAmelCase        =       max_coarse_history
													__lowerCAmelCase        =       sliding_window_len




class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						'''fine_acoustics'''




							def __init__(				self  ,							snake_case_=0.5  ,							snake_case_=512  ,							snake_case_=1_024  ,							snake_case_=8  ,							**snake_case_  ,							)   ->       Tuple:
													super().__init__(temperature=snake_case_							)

													__lowerCAmelCase        =       max_fine_history_length
													__lowerCAmelCase        =       max_fine_input_length
													__lowerCAmelCase        =       n_fine_codebooks




class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						'''bark'''
							_snake_case		     =						True




							def __init__(				self  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_=24_000  ,							snake_case_=1_024  ,							**snake_case_  ,							)   ->       Any:
													if semantic_config is None:
																			__lowerCAmelCase        =       {}
																			logger.info("""semantic_config is None. initializing the semantic model with default values."""							)

													if coarse_acoustics_config is None:
																			__lowerCAmelCase        =       {}
																			logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""							)

													if fine_acoustics_config is None:
																			__lowerCAmelCase        =       {}
																			logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""							)

													__lowerCAmelCase        =       BarkSemanticGenerationConfig(**snake_case_							)
													__lowerCAmelCase        =       BarkCoarseGenerationConfig(**snake_case_							)
													__lowerCAmelCase        =       BarkFineGenerationConfig(**snake_case_							)

													__lowerCAmelCase        =       sample_rate
													__lowerCAmelCase        =       codebook_size




							@classmethod
							def  A__		(				cls  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							**snake_case_  ,							)   ->       List[Any]:
													return cls(
													    semantic_config=semantic_config.to_dict()  ,							coarse_acoustics_config=coarse_acoustics_config.to_dict()  ,							fine_acoustics_config=fine_acoustics_config.to_dict()  ,							**snake_case_  ,							)




							def  A__		(				self							)   ->       Tuple:
													__lowerCAmelCase        =       copy.deepcopy(self.__dict__							)

													__lowerCAmelCase        =       self.semantic_config.to_dict()
													__lowerCAmelCase        =       self.coarse_acoustics_config.to_dict()
													__lowerCAmelCase        =       self.fine_acoustics_config.to_dict()

													__lowerCAmelCase        =       self.__class__.model_type
													return output


"""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 				lowercase      (_lowerCAmelCase=None				,  _lowerCAmelCase=None		):
						return field(default_factory=lambda: default				,  metadata=_lowerCAmelCase		)
@dataclass
class      lowerCAmelCase_						:



							'''simple docstring'''





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

def 				lowercase      (_lowerCAmelCase		):
						try:
												int(_lowerCAmelCase		)
												return True
						except ValueError:
												return False

def 				lowercase      (_lowerCAmelCase		):
						try:
												float(_lowerCAmelCase		)
												return True
						except ValueError:
												return False
class      lowerCAmelCase_						:



							'''simple docstring'''





							def __init__(				self  ,							snake_case_							)   ->       List[Any]:
													__lowerCAmelCase        =       args
													__lowerCAmelCase        =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}							)

													with open(self.args.csv_file  ,							newline=""""""							) as csv_file:
																			__lowerCAmelCase        =       csv.DictReader(snake_case_							)
																			for row in reader:
																									__lowerCAmelCase        =       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
																															__lowerCAmelCase        =       int(row["""result"""]							)
																									elif can_convert_to_float(row["""result"""]							):
																															# value is not None
																															__lowerCAmelCase        =       float(row["""result"""]							)




							def  A__		(				self							)   ->       List[Any]:
													__lowerCAmelCase ,      __lowerCAmelCase        =       plt.subplots()
													__lowerCAmelCase        =       """Time usage""" if self.args.is_time else """Memory usage"""
													__lowerCAmelCase        =       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()							):
																			__lowerCAmelCase        =       sorted(set(self.result_dict[model_name]["""bsz"""]							)							)
																			__lowerCAmelCase        =       sorted(set(self.result_dict[model_name]["""seq_len"""]							)							)
																			__lowerCAmelCase        =       self.result_dict[model_name]["""result"""]

																			((__lowerCAmelCase) ,      (__lowerCAmelCase))        =       (
																			    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																			)

																			__lowerCAmelCase        =       (
																			    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:
																															__lowerCAmelCase        =       np.asarray(
																															    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]  ,							dtype=snake_case_  ,							)
																									else:
																															__lowerCAmelCase        =       np.asarray(
																															    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]  ,							dtype=np.floataa  ,							)

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

																									__lowerCAmelCase        =       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."""

													__lowerCAmelCase        =       title_str[:-4]
													__lowerCAmelCase        =       """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 				lowercase      ():
						__lowerCAmelCase        =       HfArgumentParser(_lowerCAmelCase		)
						__lowerCAmelCase        =       parser.parse_args_into_dataclasses()[0]
						__lowerCAmelCase        =       Plot(args=_lowerCAmelCase		)
						plot.plot()


if __name__ == "__main__":
			main()
