
"""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   __magic_name__	(      __snake_case				:       str  ) ->				Optional[int]:
       lowercase    :				str       		=							int(__snake_case  )
       lowercase		,    lowercase		,    lowercase    :				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   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Tuple     , __snake_case				:       List[str]     , __snake_case				:       List[Any]     , __snake_case				:       Optional[Any]=300  ) ->				Optional[int]:
       # docstyle-ignore
       return f"""
    <div>
      {prefix}
      <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
      {label}
    </div>
    """


def   __magic_name__	(      __snake_case				:       Tuple  ) ->				int:
       lowercase    :				Optional[Any]       		=							"<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:
                     lowercase    :				int       		=							f"""{elt:.6f}""" if isinstance(__snake_case     , __snake_case  ) else str(__snake_case  )
                     html_code += f"""      <td>{elt}</td>\n"""
              html_code += "    </tr>\n"
       html_code += "  </tbody>\n</table><p>"
       return html_code

class 		a__						:
   __lowerCAmelCase            =				5
   __lowerCAmelCase            =				0.2
   def __init__(			self	,    _a	,    _a = None	,    _a = True	,    _a = None	,    _a = 300	,    ):
          lowercase    :				int       		=							total
          lowercase    :				int       		=							"" if prefix is None else prefix
          lowercase    :				Union[str, Any]       		=							leave
          lowercase    :				Optional[int]       		=							parent
          lowercase    :				Optional[int]       		=							width
          lowercase    :				Union[str, Any]       		=							None
          lowercase    :				str       		=							None
          lowercase    :				List[str]       		=							None
   def 			__magic_name__			(			self	,    _a	,    _a = False	,    _a = None				):
          lowercase    :				str       		=							value
          if comment is not None:
                 lowercase    :				Optional[Any]       		=							comment
          if self.last_value is None:
                 lowercase    :				List[Any]       		=							time.time()
                 lowercase    :				str       		=							value
                 lowercase    :				Tuple       		=							None
                 lowercase    :				Any       		=							self.warmup
                 lowercase    :				Optional[int]       		=							1
                 self.update_bar(_a				)
          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
                 lowercase    :				Union[str, Any]       		=							time.time()
                 lowercase    :				Optional[int]       		=							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:
                        lowercase    :				List[str]       		=							self.elapsed_time / (value - self.start_value)
                 else:
                        lowercase    :				List[str]       		=							None
                 if value >= self.total:
                        lowercase    :				int       		=							self.total
                        lowercase    :				List[str]       		=							None
                        if not self.leave:
                               self.close()
                 elif self.average_time_per_item is not None:
                        lowercase    :				List[str]       		=							self.average_time_per_item * (self.total - value)
                 self.update_bar(_a				)
                 lowercase    :				Any       		=							value
                 lowercase    :				List[str]       		=							current_time
                 if self.average_time_per_item is None:
                        lowercase    :				int       		=							1
                 else:
                        lowercase    :				Union[str, Any]       		=							max(int(self.update_every / self.average_time_per_item				)	,    1				)
   def 			__magic_name__			(			self	,    _a	,    _a=None				):
          lowercase    :				List[str]       		=							" " * (len(str(self.total				)				) - len(str(_a				)				)) + str(_a				)
          if self.elapsed_time is None:
                 lowercase    :				Optional[Any]       		=							f"""[{spaced_value}/{self.total} : < :"""
          elif self.predicted_remaining is None:
                 lowercase    :				Optional[int]       		=							f"""[{spaced_value}/{self.total} {format_time(self.elapsed_time				)}"""
          else:
                 lowercase    :				str       		=							(
                     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 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							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:
                 lowercase    :				Any       		=							disp.display(disp.HTML(self.html_code				)	,    display_id=_a				)
          else:
                 self.output.update(disp.HTML(self.html_code				)				)

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


class 		a__						(      a_						):
   def __init__(			self	,    _a	,    _a=None				):
          super().__init__(_a				)
          lowercase    :				Optional[Any]       		=							None if column_names is None else [column_names]
          lowercase    :				str       		=							None
   def 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							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:
                 lowercase    :				str       		=							disp.display(disp.HTML(self.html_code				)	,    display_id=_a				)
          else:
                 self.output.update(disp.HTML(self.html_code				)				)
   def 			__magic_name__			(			self	,    _a				):
          if self.inner_table is None:
                 lowercase    :				Any       		=							[list(values.keys()				), list(values.values()				)]
          else:
                 lowercase    :				Optional[int]       		=							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(_a				)
                        lowercase    :				Optional[int]       		=							columns
                 self.inner_table.append([values[c] for c in columns]				)
   def 			__magic_name__			(			self	,    _a	,    _a=None	,    _a=300				):
          lowercase    :				Optional[Any]       		=							NotebookProgressBar(_a	,    prefix=_a	,    parent=self	,    width=_a				)
          return self.child_bar

   def 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							None
          self.display()


class 		a__						(      a_						):
   def __init__(			self				):
          lowercase    :				Union[str, Any]       		=							None
          lowercase    :				List[Any]       		=							None
          lowercase    :				Tuple       		=							False
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    **_a				):
          lowercase    :				int       		=							"Epoch" if args.evaluation_strategy == IntervalStrategy.EPOCH else "Step"
          lowercase    :				int       		=							0
          lowercase    :				Union[str, Any]       		=							0
          lowercase    :				str       		=							[self.first_column] + ["Training Loss"]
          if args.evaluation_strategy != IntervalStrategy.NO:
                 column_names.append("Validation Loss"				)
          lowercase    :				List[Any]       		=							NotebookTrainingTracker(state.max_steps	,    _a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    **_a				):
          lowercase    :				int       		=							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	,    )
          lowercase    :				Union[str, Any]       		=							False
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a=None	,    **_a				):
          if not has_length(_a				):
                 return
          if self.prediction_bar is None:
                 if self.training_tracker is not None:
                        lowercase    :				Dict       		=							self.training_tracker.add_child(len(_a				)				)
                 else:
                        lowercase    :				Union[str, Any]       		=							NotebookProgressBar(len(_a				)				)
                 self.prediction_bar.update(1				)
          else:
                 self.prediction_bar.update(self.prediction_bar.value + 1				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    **_a				):
          if self.prediction_bar is not None:
                 self.prediction_bar.close()
          lowercase    :				Any       		=							None
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a=None	,    **_a				):
          # Only for when there is no evaluation
          if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
                 lowercase    :				Any       		=							{"Training Loss": logs["loss"]}
                 # First column is necessarily Step sine we're not in epoch eval strategy
                 lowercase    :				Dict       		=							state.global_step
                 self.training_tracker.write_line(_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a=None	,    **_a				):
          if self.training_tracker is not None:
                 lowercase    :				Dict       		=							{"Training Loss": "No log", "Validation Loss": "No log"}
                 for log in reversed(state.log_history				):
                        if "loss" in log:
                               lowercase    :				Dict       		=							log["loss"]
                               break

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

   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    **_a				):
          self.training_tracker.update(
              state.global_step	,    comment=f"""Epoch {int(state.epoch				)}/{state.num_train_epochs}"""	,    force_update=_a				)
          lowercase    :				Union[str, Any]       		=							None




"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
   import PIL

if is_torch_available():
   import torch


_A			:       List[Any]   		=       logging.get_logger(__name__)

class 		a__						(      a_						):
   __lowerCAmelCase            =				["""pixel_values"""]
   def __init__(			self	,    _a = True	,    _a = None	,    _a = PILImageResampling.BILINEAR	,    _a = True	,    _a = 1 / 255	,    _a = True	,    _a = None	,    _a = True	,    **_a	,    ):
          super().__init__(**_a				)
          lowercase    :				Optional[Any]       		=							size if size is not None else {"shortest_edge": 224}
          lowercase    :				List[Any]       		=							get_size_dict(_a	,    default_to_square=_a				)
          lowercase    :				str       		=							crop_size if crop_size is not None else {"height": 256, "width": 256}
          lowercase    :				List[str]       		=							get_size_dict(_a	,    param_name="crop_size"				)

          lowercase    :				int       		=							do_resize
          lowercase    :				Optional[int]       		=							size
          lowercase    :				str       		=							resample
          lowercase    :				List[Any]       		=							do_rescale
          lowercase    :				Union[str, Any]       		=							rescale_factor
          lowercase    :				Optional[int]       		=							do_center_crop
          lowercase    :				Union[str, Any]       		=							crop_size
          lowercase    :				Optional[Any]       		=							do_flip_channel_order
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = PIL.Image.BILINEAR	,    _a = None	,    **_a	,    ):
          lowercase    :				List[Any]       		=							get_size_dict(_a	,    default_to_square=_a				)
          if "shortest_edge" not in size:
                 raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""				)
          lowercase    :				Union[str, Any]       		=							get_resize_output_image_size(_a	,    size=size["shortest_edge"]	,    default_to_square=_a				)
          return resize(_a	,    size=_a	,    resample=_a	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          lowercase    :				str       		=							get_size_dict(_a				)
          if "height" not in size or "width" not in size:
                 raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""				)
          return center_crop(_a	,    size=(size["height"], size["width"])	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          return rescale(_a	,    scale=_a	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          return flip_channel_order(_a	,    data_format=_a				)
   def 			__magic_name__			(			self	,    _a	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = ChannelDimension.FIRST	,    **_a	,    ):
          lowercase    :				Optional[Any]       		=							do_resize if do_resize is not None else self.do_resize
          lowercase    :				Tuple       		=							resample if resample is not None else self.resample
          lowercase    :				List[str]       		=							do_rescale if do_rescale is not None else self.do_rescale
          lowercase    :				List[Any]       		=							rescale_factor if rescale_factor is not None else self.rescale_factor
          lowercase    :				Union[str, Any]       		=							do_center_crop if do_center_crop is not None else self.do_center_crop
          lowercase    :				Optional[int]       		=							(
              do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
          )

          lowercase    :				str       		=							size if size is not None else self.size
          lowercase    :				Any       		=							get_size_dict(_a	,    default_to_square=_a				)
          lowercase    :				int       		=							crop_size if crop_size is not None else self.crop_size
          lowercase    :				Any       		=							get_size_dict(_a	,    param_name="crop_size"				)

          lowercase    :				int       		=							make_list_of_images(_a				)

          if not valid_images(_a				):
                 raise ValueError(
                     "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                     "torch.Tensor, tf.Tensor or jax.ndarray."				)

          if do_resize and size is None:
                 raise ValueError("Size must be specified if do_resize is True."				)

          if do_rescale and rescale_factor is None:
                 raise ValueError("Rescale factor must be specified if do_rescale is True."				)

          if do_center_crop and crop_size is None:
                 raise ValueError("Crop size must be specified if do_center_crop is True."				)

          # All transformations expect numpy arrays.
          lowercase    :				Any       		=							[to_numpy_array(_a				) for image in images]

          if do_resize:
                 lowercase    :				Optional[int]       		=							[self.resize(image=_a	,    size=_a	,    resample=_a				) for image in images]

          if do_center_crop:
                 lowercase    :				str       		=							[self.center_crop(image=_a	,    size=_a				) for image in images]

          if do_rescale:
                 lowercase    :				Union[str, Any]       		=							[self.rescale(image=_a	,    scale=_a				) for image in images]

          # the pretrained checkpoints assume images are BGR, not RGB
          if do_flip_channel_order:
                 lowercase    :				int       		=							[self.flip_channel_order(image=_a				) for image in images]

          lowercase    :				int       		=							[to_channel_dimension_format(_a	,    _a				) for image in images]

          lowercase    :				Optional[Any]       		=							{"pixel_values": images}
          return BatchFeature(data=_a	,    tensor_type=_a				)

   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Optional[int]       		=							outputs.logits

          # Resize logits and compute semantic segmentation maps
          if target_sizes is not None:
                 if len(_a				) != len(_a				):
                        raise ValueError(
                            "Make sure that you pass in as many target sizes as the batch dimension of the logits"				)

                 if is_torch_tensor(_a				):
                        lowercase    :				Tuple       		=							target_sizes.numpy()

                 lowercase    :				List[Any]       		=							[]

                 for idx in range(len(_a				)				):
                        lowercase    :				Dict       		=							torch.nn.functional.interpolate(
                            logits[idx].unsqueeze(dim=0				)	,    size=target_sizes[idx]	,    mode="bilinear"	,    align_corners=_a				)
                        lowercase    :				Tuple       		=							resized_logits[0].argmax(dim=0				)
                        semantic_segmentation.append(_a				)
          else:
                 lowercase    :				str       		=							logits.argmax(dim=1				)
                 lowercase    :				Dict       		=							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

          return semantic_segmentation


