
'''simple docstring'''





import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


UpperCAmelCase    					=   {
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 48_000,
        '''sample_size''': 65_536,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 48_000,
        '''sample_size''': 65_536,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 48_000,
        '''sample_size''': 131_072,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 16_000,
        '''sample_size''': 65_536,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 16_000,
        '''sample_size''': 65_536,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 16_000,
        '''sample_size''': 65_536,
    },
}

def      A (       A_							:					Optional[int]    ,							A_							:					Optional[Any]       ):
						return torch.atana(A_    ,							A_       ) / math.pi * 2

def      A (       A_							:					List[str]       ):
						snake_case       :     str =    torch.sin(t * math.pi / 2       ) ** 2
						snake_case       :     Optional[Any] =    (1 - sigma**2) ** 0.5
						return alpha_sigma_to_t(A_    ,							A_       )

class 						a   (				UpperCamelCase__    ):
	pass




class 						a   (				nn.Module    ):




	def __init__(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Any      ):
							super().__init__()

							snake_case       :     int =    DiffusionAttnUnetaD(SCREAMING_SNAKE_CASE_,    n_attn_layers=4      )
							snake_case       :     Optional[int] =    deepcopy(self.diffusion      )
							snake_case       :     List[str] =    torch.quasirandom.SobolEngine(1,    scramble=SCREAMING_SNAKE_CASE_      )




def      A (       A_							:					Any       ):
						snake_case       :     Union[str, Any] =    MODELS_MAP[model_name]['''url''']
						os.system(F"""wget {url} ./"""       )

						return F"""./{model_name}.ckpt"""


UpperCAmelCase    					=   {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
UpperCAmelCase    					=   {
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
UpperCAmelCase    					=   {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
    '''8''': '''resnets.3''',
    '''9''': '''attentions.3''',
    '''10''': '''resnets.4''',
    '''11''': '''attentions.4''',
    '''12''': '''resnets.5''',
    '''13''': '''attentions.5''',
}
UpperCAmelCase    					=   {
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

UpperCAmelCase    					=   {
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

UpperCAmelCase    					=   {
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}

def      A (       A_							:					Optional[Any]       ):




						if name.startswith('''skip'''       ):
												return name.replace('''skip'''    ,							RES_CONV_MAP['''skip''']       )

						# name has to be of format main.{digit}
						if not name.startswith('''main.'''       ):
												raise ValueError(F"""ResConvBlock error with {name}"""       )

						return name.replace(name[:6]    ,							RES_CONV_MAP[name[:6]]       )

def      A (       A_							:					str       ):




						for key, value in ATTN_MAP.items():
												if name.startswith(A_       ) and not isinstance(A_    ,							A_       ):
																		return name.replace(A_    ,							A_       )
												elif name.startswith(A_       ):
																		return [name.replace(A_    ,							A_       ) for v in value]
						raise ValueError(F"""Attn error with {name}"""       )

def      A (       A_							:					List[Any]    ,							A_							:					Optional[int]=13       ):
						snake_case       :     Optional[Any] =    input_string

						if string.split('''.'''       )[0] == "timestep_embed":
												return string.replace('''timestep_embed'''    ,							'''time_proj'''       )

						snake_case       :     Optional[Any] =    0
						if string.startswith('''net.3.'''       ):
												depth += 1
												snake_case       :     List[str] =    string[6:]
						elif string.startswith('''net.'''       ):
												snake_case       :     Union[str, Any] =    string[4:]

						while string.startswith('''main.7.'''       ):
												depth += 1
												snake_case       :     Optional[int] =    string[7:]

						if string.startswith('''main.'''       ):
												snake_case       :     Optional[Any] =    string[5:]

						# mid block
						if string[:2].isdigit():
												snake_case       :     Dict =    string[:2]
												snake_case       :     int =    string[2:]
						else:
												snake_case       :     Optional[int] =    string[0]
												snake_case       :     Union[str, Any] =    string[1:]

						if depth == max_depth:
												snake_case       :     Dict =    MID_NUM_TO_LAYER[layer_num]
												snake_case       :     List[Any] =    '''mid_block'''
						elif depth > 0 and int(A_       ) < 7:
												snake_case       :     List[Any] =    DOWN_NUM_TO_LAYER[layer_num]
												snake_case       :     int =    F"""down_blocks.{depth}"""
						elif depth > 0 and int(A_       ) > 7:
												snake_case       :     List[str] =    UP_NUM_TO_LAYER[layer_num]
												snake_case       :     Any =    F"""up_blocks.{max_depth - depth - 1}"""
						elif depth == 0:
												snake_case       :     Tuple =    DEPTH_0_TO_LAYER[layer_num]
												snake_case       :     str =    F"""up_blocks.{max_depth - 1}""" if int(A_       ) > 3 else '''down_blocks.0'''

						if not string_left.startswith('''.'''       ):
												raise ValueError(F"""Naming error with {input_string} and string_left: {string_left}."""       )

						snake_case       :     Dict =    string_left[1:]

						if "resnets" in new_layer:
												snake_case       :     Dict =    convert_resconv_naming(A_       )
						elif "attentions" in new_layer:
												snake_case       :     List[Any] =    convert_attn_naming(A_       )
												snake_case       :     Dict =    new_string_left

						if not isinstance(A_    ,							A_       ):
												snake_case       :     Optional[Any] =    prefix + '''.''' + new_layer + '''.''' + string_left
						else:
												snake_case       :     Union[str, Any] =    [prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
						return new_string

def      A (       A_							:					List[Any]       ):
						snake_case       :     int =    {}
						for k, v in state_dict.items():
												if k.endswith('''kernel'''       ):
																		# up- and downsample layers, don't have trainable weights
																		continue

												snake_case       :     List[str] =    rename(A_       )

												# check if we need to transform from Conv => Linear for attention
												if isinstance(A_    ,							A_       ):
																		snake_case       :     int =    transform_conv_attns(A_    ,							A_    ,							A_       )
												else:
																		snake_case       :     int =    v

						return new_state_dict

def      A (       A_							:					Dict    ,							A_							:					int    ,							A_							:					int       ):




						if len(A_       ) == 1:
												if len(v.shape       ) == 3:
																		# weight
																		snake_case       :     Tuple =    v[:, :, 0]
												else:
																		# bias
																		snake_case       :     List[Any] =    v
						else:
												# qkv matrices
												snake_case       :     Optional[int] =    v.shape[0]
												snake_case       :     Tuple =    trippled_shape // 3
												for i in range(3       ):
																		if len(v.shape       ) == 3:
																								snake_case       :     str =    v[i * single_shape : (i + 1) * single_shape, :, 0]
																		else:
																								snake_case       :     Any =    v[i * single_shape : (i + 1) * single_shape]
						return new_state_dict

def      A (       A_							:					Dict       ):
						snake_case       :     Optional[Any] =    torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''       )

						snake_case       :     str =    args.model_path.split('''/'''       )[-1].split('''.'''       )[0]
						if not os.path.isfile(args.model_path       ):
												assert (
												    model_name == args.model_path
												), F"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
												snake_case       :     Any =    download(A_       )

						snake_case       :     str =    MODELS_MAP[model_name]['''sample_rate''']
						snake_case       :     Optional[int] =    MODELS_MAP[model_name]['''sample_size''']

						snake_case       :     Union[str, Any] =    Object()
						snake_case       :     str =    sample_size
						snake_case       :     Optional[Any] =    sample_rate
						snake_case       :     List[Any] =    0

						snake_case       :     List[str] =    UNetaDModel(sample_size=A_    ,							sample_rate=A_       )
						snake_case       :     str =    diffusers_model.state_dict()

						snake_case       :     Any =    DiffusionUncond(A_       )
						orig_model.load_state_dict(torch.load(args.model_path    ,							map_location=A_       )['''state_dict''']       )
						snake_case       :     Tuple =    orig_model.diffusion_ema.eval()
						snake_case       :     Optional[int] =    orig_model.state_dict()
						snake_case       :     List[Any] =    rename_orig_weights(A_       )

						snake_case       :     Optional[Any] =    set(renamed_state_dict.keys()       ) - set(diffusers_state_dict.keys()       )
						snake_case       :     Any =    set(diffusers_state_dict.keys()       ) - set(renamed_state_dict.keys()       )

						assert len(A_       ) == 0, F"""Problem with {renamed_minus_diffusers}"""
						assert all(k.endswith('''kernel'''       ) for k in list(A_       )       ), F"""Problem with {diffusers_minus_renamed}"""

						for key, value in renamed_state_dict.items():
												assert (
												    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
												), F"""Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
												if key == "time_proj.weight":
																		snake_case       :     str =    value.squeeze()

												snake_case       :     Dict =    value

						diffusers_model.load_state_dict(A_       )

						snake_case       :     Optional[Any] =    100
						snake_case       :     List[Any] =    33

						snake_case       :     Dict =    IPNDMScheduler(num_train_timesteps=A_       )

						snake_case       :     List[str] =    torch.manual_seed(A_       )
						snake_case       :     Optional[int] =    torch.randn([1, 2, config.sample_size]    ,							generator=A_       ).to(A_       )

						snake_case       :     str =    torch.linspace(1    ,							0    ,							steps + 1    ,							device=A_       )[:-1]
						snake_case       :     str =    get_crash_schedule(A_       )

						snake_case       :     Optional[int] =    DanceDiffusionPipeline(unet=A_    ,							scheduler=A_       )

						snake_case       :     Optional[Any] =    torch.manual_seed(33       )
						snake_case       :     Optional[Any] =    pipe(num_inference_steps=A_    ,							generator=A_       ).audios

						snake_case       :     int =    sampling.iplms_sample(A_    ,							A_    ,							A_    ,							{}       )
						snake_case       :     List[str] =    generated.clamp(-1    ,							1       )

						snake_case       :     Optional[int] =    (generated - audio).abs().sum()
						snake_case       :     Optional[int] =    (generated - audio).abs().max()

						if args.save:
												pipe.save_pretrained(args.checkpoint_path       )

						print('''Diff sum'''    ,							A_       )
						print('''Diff max'''    ,							A_       )

						assert diff_max < 1e-3, F"""Diff max: {diff_max} is too much :-/"""

						print(F"""Conversion for {model_name} successful!"""       )


if __name__ == "__main__":
						UpperCAmelCase    					=   argparse.ArgumentParser()

						parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
						parser.add_argument(
						    "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
						)
						parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
						UpperCAmelCase    					=   parser.parse_args()

						main(args)



'''simple docstring'''





import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length

def      A (       A_							:					str       ):
						snake_case       :     List[str] =    int(A_       )
						snake_case,					snake_case,					snake_case       :     Any =    t // 3600, (t // 60) % 60, t % 60
						return F"""{h}:{m:02d}:{s:02d}""" if h != 0 else F"""{m:02d}:{s:02d}"""

def      A (       A_							:					Optional[int]    ,							A_							:					int    ,							A_							:					List[str]    ,							A_							:					Optional[int]    ,							A_							:					Optional[int]=300       ):
						# docstyle-ignore
						return F"""
    <div>
      {prefix}
      <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
      {label}
    </div>
    """

def      A (       A_							:					Dict       ):
						snake_case       :     Tuple =    '''<table border="1" class="dataframe">\n'''
						html_code += """  <thead>\n <tr style="text-align: left;">\n"""
						for i in items[0]:
												html_code += F"""      <th>{i}</th>\n"""
						html_code += "    </tr>\n  </thead>\n  <tbody>\n"
						for line in items[1:]:
												html_code += "    <tr>\n"
												for elt in line:
																		snake_case       :     List[str] =    F"""{elt:.6f}""" if isinstance(A_    ,							A_       ) else str(A_       )
																		html_code += F"""      <td>{elt}</td>\n"""
												html_code += "    </tr>\n"
						html_code += "  </tbody>\n</table><p>"
						return html_code

class 						a   :
				_snake_case		    = 5
				_snake_case		    = 0.2




				def __init__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None,    SCREAMING_SNAKE_CASE_	:				bool = True,    SCREAMING_SNAKE_CASE_	:				Optional["NotebookTrainingTracker"] = None,    SCREAMING_SNAKE_CASE_	:				int = 3_00,    ):
										snake_case       :     List[Any] =    total
										snake_case       :     Union[str, Any] =    '''''' if prefix is None else prefix
										snake_case       :     List[Any] =    leave
										snake_case       :     int =    parent
										snake_case       :     List[str] =    width
										snake_case       :     Optional[int] =    None
										snake_case       :     Optional[Any] =    None
										snake_case       :     Tuple =    None




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				bool = False,    SCREAMING_SNAKE_CASE_	:				str = None      ):
										snake_case       :     List[Any] =    value
										if comment is not None:
																snake_case       :     Tuple =    comment
										if self.last_value is None:
																snake_case       :     str =    time.time()
																snake_case       :     List[str] =    value
																snake_case       :     str =    None
																snake_case       :     Dict =    self.warmup
																snake_case       :     Tuple =    1
																self.update_bar(SCREAMING_SNAKE_CASE_      )
										elif value <= self.last_value and not force_update:
																return
										elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for,    self.total      ):
																if self.first_calls > 0:
																						self.first_calls -= 1
																snake_case       :     Tuple =    time.time()
																snake_case       :     str =    current_time - self.start_time
																# We could have value = self.start_value if the update is called twixe with the same start value.
																if value > self.start_value:
																						snake_case       :     Any =    self.elapsed_time / (value - self.start_value)
																else:
																						snake_case       :     List[str] =    None
																if value >= self.total:
																						snake_case       :     List[Any] =    self.total
																						snake_case       :     Tuple =    None
																						if not self.leave:
																												self.close()
																elif self.average_time_per_item is not None:
																						snake_case       :     Optional[int] =    self.average_time_per_item * (self.total - value)
																self.update_bar(SCREAMING_SNAKE_CASE_      )
																snake_case       :     List[str] =    value
																snake_case       :     List[Any] =    current_time
																if self.average_time_per_item is None:
																						snake_case       :     List[str] =    1
																else:
																						snake_case       :     Optional[int] =    max(int(self.update_every / self.average_time_per_item      ),    1      )




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Optional[Any]=None      ):
										snake_case       :     Optional[Any] =    ''' ''' * (len(str(self.total      )      ) - len(str(SCREAMING_SNAKE_CASE_      )      )) + str(SCREAMING_SNAKE_CASE_      )
										if self.elapsed_time is None:
																snake_case       :     int =    F"""[{spaced_value}/{self.total} : < :"""
										elif self.predicted_remaining is None:
																snake_case       :     List[str] =    F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time      )}"""
										else:
																snake_case       :     Optional[int] =    (
																    F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time      )} <"""
																    F""" {format_time(self.predicted_remaining      )}"""
																)
																self.label += F""", {1/self.average_time_per_item:.2f} it/s"""
										self.label += "]" if self.comment is None or len(self.comment      ) == 0 else F""", {self.comment}]"""
										self.display()




				def 	__snake_case							(							self	:				Optional[int]      ):
										snake_case       :     str =    html_progress_bar(self.value,    self.total,    self.prefix,    self.label,    self.width      )
										if self.parent is not None:
																# If this is a child bar, the parent will take care of the display.
																self.parent.display()
																return
										if self.output is None:
																snake_case       :     Any =    disp.display(disp.HTML(self.html_code      ),    display_id=SCREAMING_SNAKE_CASE_      )
										else:
																self.output.update(disp.HTML(self.html_code      )      )




				def 	__snake_case							(							self	:				Optional[int]      ):
										if self.parent is None and self.output is not None:
																self.output.update(disp.HTML(''''''      )      )




class 						a   (				__magic_name__    ):




				def __init__(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Optional[int]=None      ):
										super().__init__(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Tuple =    None if column_names is None else [column_names]
										snake_case       :     str =    None




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     Tuple =    html_progress_bar(self.value,    self.total,    self.prefix,    self.label,    self.width      )
										if self.inner_table is not None:
																self.html_code += text_to_html_table(self.inner_table      )
										if self.child_bar is not None:
																self.html_code += self.child_bar.html_code
										if self.output is None:
																snake_case       :     List[Any] =    disp.display(disp.HTML(self.html_code      ),    display_id=SCREAMING_SNAKE_CASE_      )
										else:
																self.output.update(disp.HTML(self.html_code      )      )




				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Any      ):
										if self.inner_table is None:
																snake_case       :     Optional[Any] =    [list(values.keys()      ), list(values.values()      )]
										else:
																snake_case       :     Tuple =    self.inner_table[0]
																if len(self.inner_table      ) == 1:
																						# We give a chance to update the column names at the first iteration
																						for key in values.keys():
																												if key not in columns:
																																		columns.append(SCREAMING_SNAKE_CASE_      )
																						snake_case       :     int =    columns
																self.inner_table.append([values[c] for c in columns]      )




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				int=None,    SCREAMING_SNAKE_CASE_	:				str=3_00      ):
										snake_case       :     int =    NotebookProgressBar(SCREAMING_SNAKE_CASE_,    prefix=SCREAMING_SNAKE_CASE_,    parent=self,    width=SCREAMING_SNAKE_CASE_      )
										return self.child_bar




				def 	__snake_case							(							self	:				Union[str, Any]      ):
										snake_case       :     int =    None
										self.display()




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Optional[Any]      ):
										snake_case       :     Dict =    None
										snake_case       :     Union[str, Any] =    None
										snake_case       :     Dict =    False




				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				str,    **SCREAMING_SNAKE_CASE_	:				List[str]      ):
										snake_case       :     str =    '''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
										snake_case       :     List[str] =    0
										snake_case       :     Dict =    0
										snake_case       :     List[Any] =    [self.first_column] + ['''Training Loss''']
										if args.evaluation_strategy != IntervalStrategy.NO:
																column_names.append('''Validation Loss'''      )
										snake_case       :     Tuple =    NotebookTrainingTracker(state.max_steps,    SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				str,    **SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										snake_case       :     Tuple =    int(state.epoch      ) if int(state.epoch      ) == state.epoch else F"""{state.epoch:.2f}"""
										self.training_tracker.update(
										    state.global_step + 1,    comment=F"""Epoch {epoch}/{state.num_train_epochs}""",    force_update=self._force_next_update,    )
										snake_case       :     str =    False




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Tuple=None,    **SCREAMING_SNAKE_CASE_	:				Tuple      ):
										if not has_length(SCREAMING_SNAKE_CASE_      ):
																return
										if self.prediction_bar is None:
																if self.training_tracker is not None:
																						snake_case       :     Dict =    self.training_tracker.add_child(len(SCREAMING_SNAKE_CASE_      )      )
																else:
																						snake_case       :     Optional[Any] =    NotebookProgressBar(len(SCREAMING_SNAKE_CASE_      )      )
																self.prediction_bar.update(1      )
										else:
																self.prediction_bar.update(self.prediction_bar.value + 1      )




				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    **SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										if self.prediction_bar is not None:
																self.prediction_bar.close()
										snake_case       :     Optional[int] =    None




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Optional[int]=None,    **SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										# Only for when there is no evaluation
										if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
																snake_case       :     List[str] =    {'''Training Loss''': logs['''loss''']}
																# First column is necessarily Step sine we're not in epoch eval strategy
																snake_case       :     List[Any] =    state.global_step
																self.training_tracker.write_line(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Optional[Any]=None,    **SCREAMING_SNAKE_CASE_	:				Any      ):
										if self.training_tracker is not None:
																snake_case       :     List[Any] =    {'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
																for log in reversed(state.log_history      ):
																						if "loss" in log:
																												snake_case       :     Union[str, Any] =    log['''loss''']
																												break

																if self.first_column == "Epoch":
																						snake_case       :     Optional[int] =    int(state.epoch      )
																else:
																						snake_case       :     Optional[int] =    state.global_step
																snake_case       :     Optional[int] =    '''eval'''
																for k in metrics:
																						if k.endswith('''_loss'''      ):
																												snake_case       :     str =    re.sub(R'''\_loss$''',    '''''',    SCREAMING_SNAKE_CASE_      )
																snake_case       :     str =    metrics.pop('''total_flos''',    SCREAMING_SNAKE_CASE_      )
																snake_case       :     int =    metrics.pop('''epoch''',    SCREAMING_SNAKE_CASE_      )
																snake_case       :     List[str] =    metrics.pop(F"""{metric_key_prefix}_runtime""",    SCREAMING_SNAKE_CASE_      )
																snake_case       :     Union[str, Any] =    metrics.pop(F"""{metric_key_prefix}_samples_per_second""",    SCREAMING_SNAKE_CASE_      )
																snake_case       :     Any =    metrics.pop(F"""{metric_key_prefix}_steps_per_second""",    SCREAMING_SNAKE_CASE_      )
																snake_case       :     Dict =    metrics.pop(F"""{metric_key_prefix}_jit_compilation_time""",    SCREAMING_SNAKE_CASE_      )
																for k, v in metrics.items():
																						if k == F"""{metric_key_prefix}_loss""":
																												snake_case       :     Optional[int] =    v
																						else:
																												snake_case       :     int =    k.split('''_'''      )
																												snake_case       :     Any =    ''' '''.join([part.capitalize() for part in splits[1:]]      )
																												snake_case       :     Tuple =    v
																self.training_tracker.write_line(SCREAMING_SNAKE_CASE_      )
																self.training_tracker.remove_child()
																snake_case       :     Any =    None
																# Evaluation takes a long time so we should force the next update.
																snake_case       :     Optional[int] =    True




				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Dict,    **SCREAMING_SNAKE_CASE_	:				str      ):
										self.training_tracker.update(
										    state.global_step,    comment=F"""Epoch {int(state.epoch      )}/{state.num_train_epochs}""",    force_update=SCREAMING_SNAKE_CASE_      )
										snake_case       :     Tuple =    None

