
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     __lowerCAmelCase			(			a__  )       ->  str:
				__a										=    int(a__  )
				__a							,     __a							,     __a										=    t // 3600, (t // 60) % 60, t % 60
				return F"""{h}:{m:02d}:{s:02d}""" if h != 0 else F"""{m:02d}:{s:02d}"""


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__						,    a__=300  )       ->  Any:
				# docstyle-ignore
				return F"""
    <div>
      {prefix}
      <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
      {label}
    </div>
    """


def     __lowerCAmelCase			(			a__  )       ->  Any:
				__a										=    '''<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:
												__a										=    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		,     _snake_case		,     _snake_case = None		,     _snake_case = True		,     _snake_case = None		,     _snake_case = 300		,     )    ->      Any:


								'''simple docstring'''




								__a										=    total
								__a										=    '''''' if prefix is None else prefix
								__a										=    leave
								__a										=    parent
								__a										=    width
								__a										=    None
								__a										=    None
								__a										=    None




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = False		,     _snake_case = None				)    ->      Optional[Any]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=None				)    ->      Any:


								'''simple docstring'''




								__a										=    ''' ''' * (len(str(self.total				)				) - len(str(_snake_case				)				)) + str(_snake_case				)
								if self.elapsed_time is None:
												__a										=    F"""[{spaced_value}/{self.total} : < :"""
								elif self.predicted_remaining is None:
												__a										=    F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time				)}"""
								else:
												__a										=    (
												    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    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:
												__a										=    disp.display(disp.HTML(self.html_code				)		,     display_id=_snake_case				)
								else:
												self.output.update(disp.HTML(self.html_code				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




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







class 			__A( a			):




				def __init__(     self		,     _snake_case		,     _snake_case=None				)    ->      Union[str, Any]:


								'''simple docstring'''




								super().__init__(_snake_case				)
								__a										=    None if column_names is None else [column_names]
								__a										=    None




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    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:
												__a										=    disp.display(disp.HTML(self.html_code				)		,     display_id=_snake_case				)
								else:
												self.output.update(disp.HTML(self.html_code				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Dict:


								'''simple docstring'''




								if self.inner_table is None:
												__a										=    [list(values.keys()				), list(values.values()				)]
								else:
												__a										=    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(_snake_case				)
																__a										=    columns
												self.inner_table.append([values[c] for c in columns]				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=None		,     _snake_case=300				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    NotebookProgressBar(_snake_case		,     prefix=_snake_case		,     parent=self		,     width=_snake_case				)
								return self.child_bar




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    None
								self.display()







class 			__A( a			):




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


								'''simple docstring'''




								__a										=    None
								__a										=    None
								__a										=    False




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     **_snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     **_snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    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		,     )
								__a										=    False




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case=None		,     **_snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								if not has_length(_snake_case				):
												return
								if self.prediction_bar is None:
												if self.training_tracker is not None:
																__a										=    self.training_tracker.add_child(len(_snake_case				)				)
												else:
																__a										=    NotebookProgressBar(len(_snake_case				)				)
												self.prediction_bar.update(1				)
								else:
												self.prediction_bar.update(self.prediction_bar.value + 1				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     **_snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




								if self.prediction_bar is not None:
												self.prediction_bar.close()
								__a										=    None




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case=None		,     **_snake_case				)    ->      int:


								'''simple docstring'''




								if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
												__a										=    {'''Training Loss''': logs['''loss''']}
												# First column is necessarily Step sine we're not in epoch eval strategy
												__a										=    state.global_step
												self.training_tracker.write_line(_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case=None		,     **_snake_case				)    ->      Tuple:


								'''simple docstring'''




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

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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     **_snake_case				)    ->      Tuple:


								'''simple docstring'''




								self.training_tracker.update(
								    state.global_step		,     comment=F"""Epoch {int(state.epoch				)}/{state.num_train_epochs}"""		,     force_update=_snake_case				)
								__a										=    None

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :	str    = {
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Optional[Any]    = ['BlenderbotTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Optional[Any]    = [
					    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'BlenderbotForCausalLM',
					    'BlenderbotForConditionalGeneration',
					    'BlenderbotModel',
					    'BlenderbotPreTrainedModel',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Dict    = [
					    'TFBlenderbotForConditionalGeneration',
					    'TFBlenderbotModel',
					    'TFBlenderbotPreTrainedModel',
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Dict    = [
					    'FlaxBlenderbotForConditionalGeneration',
					    'FlaxBlenderbotModel',
					    'FlaxBlenderbotPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_blenderbot import (
					    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BlenderbotConfig,
					    BlenderbotOnnxConfig,
					)
					from .tokenization_blenderbot import BlenderbotTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blenderbot import (
										    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlenderbotForCausalLM,
										    BlenderbotForConditionalGeneration,
										    BlenderbotModel,
										    BlenderbotPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blenderbot import (
										    TFBlenderbotForConditionalGeneration,
										    TFBlenderbotModel,
										    TFBlenderbotPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_blenderbot import (
										    FlaxBlenderbotForConditionalGeneration,
										    FlaxBlenderbotModel,
										    FlaxBlenderbotPreTrainedModel,
										)

else:
					import sys

					A     :	List[Any]    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)