





"""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__     (					_UpperCAmelCase=None		,       _UpperCAmelCase=None		)      ->					List[str]:

 '''simple docstring'''






 return field(default_factory=lambda: default		,       metadata=_lowerCamelCase		)



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





def 							a__     (					_UpperCAmelCase		)      ->					Any:

 '''simple docstring'''






 try:
  int(_lowerCamelCase		)
  return True
 except ValueError:
  return False





def 							a__     (					_UpperCAmelCase		)      ->					Dict:

 '''simple docstring'''






 try:
  float(_lowerCamelCase		)
  return True
 except ValueError:
  return False



class        lowercase_			:

       def __init__(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			args
        snake_case__      							=			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			plt.subplots()
        snake_case__      							=			"""Time usage""" if self.args.is_time else """Memory usage"""
        snake_case__      							=			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()       ):
         snake_case__      							=			sorted(set(self.result_dict[model_name]["bsz"]       )       )
         snake_case__      							=			sorted(set(self.result_dict[model_name]["seq_len"]       )       )
         snake_case__      							=			self.result_dict[model_name]["""result"""]

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

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

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

          snake_case__      							=			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."

        snake_case__      							=			title_str[:-4]
        snake_case__      							=			"""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 							a__     (					)      ->					str:

 '''simple docstring'''






 snake_case__      							=			HfArgumentParser(_lowerCamelCase		)
 snake_case__      							=			parser.parse_args_into_dataclasses()[0]
 snake_case__      							=			Plot(args=_lowerCamelCase		)
 plot.plot()


if __name__ == "__main__":
   main()








"""simple docstring"""


import copy
from typing import Dict

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


a__   :       str   						=			logging.get_logger(__name__)



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Dict										=       """semantic"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=1_0000				,   __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=1_0048				,   __SCREAMING_SNAKE_CASE=12_9595				,   __SCREAMING_SNAKE_CASE=12_9599				,   __SCREAMING_SNAKE_CASE=1_0000				,   __SCREAMING_SNAKE_CASE=256				,   __SCREAMING_SNAKE_CASE=49.9				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        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				,   )

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



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Union[str, 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=1_2048				,   __SCREAMING_SNAKE_CASE=75				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=1_2050				,   __SCREAMING_SNAKE_CASE=256				,   __SCREAMING_SNAKE_CASE = 630				,   __SCREAMING_SNAKE_CASE = 60				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        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				,   )

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



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Union[str, Any]										=       """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				,   ):






        '''simple docstring'''

        super().__init__(temperature=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			max_fine_history_length
        snake_case__      							=			max_fine_input_length
        snake_case__      							=			n_fine_codebooks



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							List[str]										=       """bark"""
       snake_case__			:							Dict										=       True

       def __init__(     self				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE=2_4000				,   __SCREAMING_SNAKE_CASE=1024				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        if semantic_config is None:
         snake_case__      							=			{}
         logger.info("semantic_config is None. initializing the semantic model with default values."       )

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

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

        snake_case__      							=			BarkSemanticGenerationConfig(**__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			BarkCoarseGenerationConfig(**__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			BarkFineGenerationConfig(**__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			sample_rate
        snake_case__      							=			codebook_size

       @classmethod
       def 						_SCREAMING_SNAKE_CASE(     cls				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        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       ):






        '''simple docstring'''

        snake_case__      							=			copy.deepcopy(self.__dict__       )

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

        snake_case__      							=			self.__class__.model_type
        return output

