
"""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  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  int(lowerCAmelCase__						)
			lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  t // 3_600, (t // 60) % 60, t % 60
			return f"{h}:{m:02d}:{s:02d}" if h != 0 else f"{m:02d}:{s:02d}"






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=300						):
			# docstyle-ignore
			return f"\n    <div>\n      {prefix}\n      <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>\n      {label}\n    </div>\n    "






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  '''<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:
									lowerCamelCase_					=  f"{elt:.6f}" if isinstance(lowerCAmelCase__	,lowerCAmelCase__						) else str(lowerCAmelCase__						)
									html_code += f"      <td>{elt}</td>\n"
						html_code += "    </tr>\n"
			html_code += "  </tbody>\n</table><p>"
			return html_code




class  __lowerCamelCase  :
				a__:      List[Any]          =    5
				a__:      List[Any]          =    0.2




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase = None							,						UpperCAmelCase = True							,						UpperCAmelCase = None							,						UpperCAmelCase = 300							,						):
							lowerCamelCase_					=  total
							lowerCamelCase_					=  '''''' if prefix is None else prefix
							lowerCamelCase_					=  leave
							lowerCamelCase_					=  parent
							lowerCamelCase_					=  width
							lowerCamelCase_					=  None
							lowerCamelCase_					=  None
							lowerCamelCase_					=  None




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = False							,						UpperCAmelCase = None   ):
							lowerCamelCase_					=  value
							if comment is not None:
										lowerCamelCase_					=  comment
							if self.last_value is None:
										lowerCamelCase_					=  lowerCamelCase_					=  time.time()
										lowerCamelCase_					=  lowerCamelCase_					=  value
										lowerCamelCase_					=  lowerCamelCase_					=  None
										lowerCamelCase_					=  self.warmup
										lowerCamelCase_					=  1
										self.update_bar(UpperCAmelCase   )
							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
										lowerCamelCase_					=  time.time()
										lowerCamelCase_					=  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:
													lowerCamelCase_					=  self.elapsed_time / (value - self.start_value)
										else:
													lowerCamelCase_					=  None
										if value >= self.total:
													lowerCamelCase_					=  self.total
													lowerCamelCase_					=  None
													if not self.leave:
																self.close()
										elif self.average_time_per_item is not None:
													lowerCamelCase_					=  self.average_time_per_item * (self.total - value)
										self.update_bar(UpperCAmelCase   )
										lowerCamelCase_					=  value
										lowerCamelCase_					=  current_time
										if self.average_time_per_item is None:
													lowerCamelCase_					=  1
										else:
													lowerCamelCase_					=  max(int(self.update_every / self.average_time_per_item   )							,						1   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=None   ):
							lowerCamelCase_					=  ''' ''' * (len(str(self.total   )   ) - len(str(UpperCAmelCase   )   )) + str(UpperCAmelCase   )
							if self.elapsed_time is None:
										lowerCamelCase_					=  f"[{spaced_value}/{self.total} : < :"
							elif self.predicted_remaining is None:
										lowerCamelCase_					=  f"[{spaced_value}/{self.total} {format_time(self.elapsed_time   )}"
							else:
										lowerCamelCase_					=  (
										    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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  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:
										lowerCamelCase_					=  disp.display(disp.HTML(self.html_code   )							,						display_id=UpperCAmelCase   )
							else:
										self.output.update(disp.HTML(self.html_code   )   )





				def        UpperCAmelCase__						(				self   ):
							if self.parent is None and self.output is not None:
										self.output.update(disp.HTML(''''''   )   )




class  __lowerCamelCase  ( lowerCAmelCase			):




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase=None   ):
							super().__init__(UpperCAmelCase   )
							lowerCamelCase_					=  None if column_names is None else [column_names]
							lowerCamelCase_					=  None




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  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:
										lowerCamelCase_					=  disp.display(disp.HTML(self.html_code   )							,						display_id=UpperCAmelCase   )
							else:
										self.output.update(disp.HTML(self.html_code   )   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							if self.inner_table is None:
										lowerCamelCase_					=  [list(values.keys()   ), list(values.values()   )]
							else:
										lowerCamelCase_					=  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(UpperCAmelCase   )
													lowerCamelCase_					=  columns
										self.inner_table.append([values[c] for c in columns]   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=None							,						UpperCAmelCase=300   ):
							lowerCamelCase_					=  NotebookProgressBar(UpperCAmelCase							,						prefix=UpperCAmelCase							,						parent=self							,						width=UpperCAmelCase   )
							return self.child_bar





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  None
							self.display()




class  __lowerCamelCase  ( lowerCAmelCase			):




				def __init__(				self   ):
							lowerCamelCase_					=  None
							lowerCamelCase_					=  None
							lowerCamelCase_					=  False




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						**UpperCAmelCase   ):
							lowerCamelCase_					=  '''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
							lowerCamelCase_					=  0
							lowerCamelCase_					=  0
							lowerCamelCase_					=  [self.first_column] + ['''Training Loss''']
							if args.evaluation_strategy != IntervalStrategy.NO:
										column_names.append('''Validation Loss'''   )
							lowerCamelCase_					=  NotebookTrainingTracker(state.max_steps							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						**UpperCAmelCase   ):
							lowerCamelCase_					=  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							,						)
							lowerCamelCase_					=  False




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase=None							,						**UpperCAmelCase   ):
							if not has_length(UpperCAmelCase   ):
										return
							if self.prediction_bar is None:
										if self.training_tracker is not None:
													lowerCamelCase_					=  self.training_tracker.add_child(len(UpperCAmelCase   )   )
										else:
													lowerCamelCase_					=  NotebookProgressBar(len(UpperCAmelCase   )   )
										self.prediction_bar.update(1   )
							else:
										self.prediction_bar.update(self.prediction_bar.value + 1   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						**UpperCAmelCase   ):
							if self.prediction_bar is not None:
										self.prediction_bar.close()
							lowerCamelCase_					=  None




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




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase=None							,						**UpperCAmelCase   ):
							if self.training_tracker is not None:
										lowerCamelCase_					=  {'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
										for log in reversed(state.log_history   ):
													if "loss" in log:
																lowerCamelCase_					=  log['''loss''']
																break

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





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						**UpperCAmelCase   ):
							self.training_tracker.update(
							    state.global_step							,						comment=f"Epoch {int(state.epoch   )}/{state.num_train_epochs}"							,						force_update=UpperCAmelCase   )
							lowerCamelCase_					=  None





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A_    							=					{
    """configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """GPTNeoForCausalLM""",
						    """GPTNeoForQuestionAnswering""",
						    """GPTNeoForSequenceClassification""",
						    """GPTNeoForTokenClassification""",
						    """GPTNeoModel""",
						    """GPTNeoPreTrainedModel""",
						    """load_tf_weights_in_gpt_neo""",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """FlaxGPTNeoForCausalLM""",
						    """FlaxGPTNeoModel""",
						    """FlaxGPTNeoPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpt_neo import (
												    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTNeoForCausalLM,
												    GPTNeoForQuestionAnswering,
												    GPTNeoForSequenceClassification,
												    GPTNeoForTokenClassification,
												    GPTNeoModel,
												    GPTNeoPreTrainedModel,
												    load_tf_weights_in_gpt_neo,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
						import sys

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



