import copy
from typing import Dict

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


lowercase__      :    Any          =     logging.get_logger(__name__)




class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	UpperCAmelCase_  : Optional[int]       =    """semantic"""


	def __init__(							self		,      __SCREAMING_SNAKE_CASE=10000		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=768		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=0.7		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=10048		,      __SCREAMING_SNAKE_CASE=129595		,      __SCREAMING_SNAKE_CASE=129599		,      __SCREAMING_SNAKE_CASE=10000		,      __SCREAMING_SNAKE_CASE=256		,      __SCREAMING_SNAKE_CASE=4_9.9		,      **__SCREAMING_SNAKE_CASE		,      )						->Optional[int]:
								super().__init__(
								    temperature=__SCREAMING_SNAKE_CASE		,      do_sample=__SCREAMING_SNAKE_CASE		,      eos_token_id=__SCREAMING_SNAKE_CASE		,      renormalize_logits=__SCREAMING_SNAKE_CASE		,      max_new_tokens=__SCREAMING_SNAKE_CASE		,      output_scores=__SCREAMING_SNAKE_CASE		,      return_dict_in_generate=__SCREAMING_SNAKE_CASE		,      output_hidden_states=__SCREAMING_SNAKE_CASE		,      output_attentions=__SCREAMING_SNAKE_CASE		,      **__SCREAMING_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        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	UpperCAmelCase_  : List[Any]       =    """coarse_acoustics"""


	def __init__(							self		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=0.7		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=12048		,      __SCREAMING_SNAKE_CASE=75		,      __SCREAMING_SNAKE_CASE=2		,      __SCREAMING_SNAKE_CASE=12050		,      __SCREAMING_SNAKE_CASE=256		,      __SCREAMING_SNAKE_CASE = 630		,      __SCREAMING_SNAKE_CASE = 60		,      **__SCREAMING_SNAKE_CASE		,      )						->Optional[Any]:
								super().__init__(
								    temperature=__SCREAMING_SNAKE_CASE		,      do_sample=__SCREAMING_SNAKE_CASE		,      renormalize_logits=__SCREAMING_SNAKE_CASE		,      output_scores=__SCREAMING_SNAKE_CASE		,      return_dict_in_generate=__SCREAMING_SNAKE_CASE		,      output_hidden_states=__SCREAMING_SNAKE_CASE		,      output_attentions=__SCREAMING_SNAKE_CASE		,      **__SCREAMING_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        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	UpperCAmelCase_  : List[str]       =    """fine_acoustics"""


	def __init__(							self		,      __SCREAMING_SNAKE_CASE=0.5		,      __SCREAMING_SNAKE_CASE=512		,      __SCREAMING_SNAKE_CASE=1024		,      __SCREAMING_SNAKE_CASE=8		,      **__SCREAMING_SNAKE_CASE		,      )						->Tuple:
								super().__init__(temperature=__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		max_fine_history_length
								lowerCAmelCase     		=		max_fine_input_length
								lowerCAmelCase     		=		n_fine_codebooks




class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	UpperCAmelCase_  : List[Any]       =    """bark"""
	UpperCAmelCase_  : Optional[Any]       =    True


	def __init__(							self		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE=24000		,      __SCREAMING_SNAKE_CASE=1024		,      **__SCREAMING_SNAKE_CASE		,      )						->List[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(**__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		BarkCoarseGenerationConfig(**__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		BarkFineGenerationConfig(**__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		sample_rate
								lowerCAmelCase     		=		codebook_size


	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      **__SCREAMING_SNAKE_CASE		,      )						->Union[str, 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()		,      **__SCREAMING_SNAKE_CASE		,      )




	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:
								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




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        SCREAMING_SNAKE_CASE_					(						snake_case__=None       ,  snake_case__=None							)					->     Optional[Any]:
							return field(default_factory=lambda: default       ,  metadata=snake_case__							)




@dataclass
class        lowercase_  :



	"""simple docstring"""





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

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Dict:
							try:
														int(snake_case__							)
														return True
							except ValueError:
														return False

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Union[str, Any]:
							try:
														float(snake_case__							)
														return True
							except ValueError:
														return False




class        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								lowerCAmelCase     		=		args
								lowerCAmelCase     		=		defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

								with open(self.args.csv_file		,      newline=''''''    ) as csv_file:
															lowerCAmelCase     		=		csv.DictReader(__SCREAMING_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       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								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=__SCREAMING_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(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )[: len(__SCREAMING_SNAKE_CASE    )]
																						plt.scatter(
																						    __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"    )
																						plt.plot(__SCREAMING_SNAKE_CASE		,      __SCREAMING_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(__SCREAMING_SNAKE_CASE    )
								plt.xlabel(__SCREAMING_SNAKE_CASE    )
								plt.ylabel(__SCREAMING_SNAKE_CASE    )
								plt.legend()

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

def        SCREAMING_SNAKE_CASE_					(						)					->     List[str]:
							lowerCAmelCase     		=		HfArgumentParser(snake_case__							)
							lowerCAmelCase     		=		parser.parse_args_into_dataclasses()[0]
							lowerCAmelCase     		=		Plot(args=snake_case__							)
							plot.plot()


if __name__ == "__main__":
	main()



