


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



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Any:
    _lowercase			:				int 						=						'<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			:				Any 						=						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:
       lowercase_					:      str       					=     5
       lowercase_					:      str       					=     0.2
       def __init__(   self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = 3_00, )			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						total
           _lowercase			:				Optional[int] 						=						'' if prefix is None else prefix
           _lowercase			:				Tuple 						=						leave
           _lowercase			:				str 						=						parent
           _lowercase			:				str 						=						width
           _lowercase			:				List[Any] 						=						None
           _lowercase			:				List[str] 						=						None
           _lowercase			:				Tuple 						=						None
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = False, lowerCamelCase = None)			->  Dict:




           """simple docstring"""

           _lowercase			:				Any 						=						value
           if comment is not None:
               _lowercase			:				Union[str, Any] 						=						comment
           if self.last_value is None:
               _lowercase			:				Dict 						=						time.time()
               _lowercase			:				Tuple 						=						value
               _lowercase			:				str 						=						None
               _lowercase			:				Optional[int] 						=						self.warmup
               _lowercase			:				Optional[Any] 						=						1
               self.update_bar(lowerCamelCase)
           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			:				List[str] 						=						time.time()
               _lowercase			:				Tuple 						=						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			:				Dict 						=						self.elapsed_time / (value - self.start_value)
               else:
                   _lowercase			:				int 						=						None
               if value >= self.total:
                   _lowercase			:				Dict 						=						self.total
                   _lowercase			:				List[str] 						=						None
                   if not self.leave:
                       self.close()
               elif self.average_time_per_item is not None:
                   _lowercase			:				Optional[int] 						=						self.average_time_per_item * (self.total - value)
               self.update_bar(lowerCamelCase)
               _lowercase			:				int 						=						value
               _lowercase			:				Tuple 						=						current_time
               if self.average_time_per_item is None:
                   _lowercase			:				str 						=						1
               else:
                   _lowercase			:				int 						=						max(int(self.update_every / self.average_time_per_item), 1)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=None)			->  Optional[Any]:




           """simple docstring"""

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




           """simple docstring"""

           _lowercase			:				Any 						=						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			:				Optional[Any] 						=						disp.display(disp.HTML(self.html_code), display_id=lowerCamelCase)
           else:
               self.output.update(disp.HTML(self.html_code))




       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

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






class _lowerCamelCase(		_a  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase=None)			->  int:




           """simple docstring"""

           super().__init__(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						None if column_names is None else [column_names]
           _lowercase			:				Any 						=						None
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Any 						=						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			:				Dict 						=						disp.display(disp.HTML(self.html_code), display_id=lowerCamelCase)
           else:
               self.output.update(disp.HTML(self.html_code))
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Dict:




           """simple docstring"""

           if self.inner_table is None:
               _lowercase			:				Dict 						=						[list(values.keys()), list(values.values())]
           else:
               _lowercase			:				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(lowerCamelCase)
                   _lowercase			:				str 						=						columns
               self.inner_table.append([values[c] for c in columns])
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=None, lowerCamelCase=3_00)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				List[str] 						=						NotebookProgressBar(lowerCamelCase, prefix=lowerCamelCase, parent=self, width=lowerCamelCase)
           return self.child_bar




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




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						None
           self.display()






class _lowerCamelCase(		_a  ):
       def __init__(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						None
           _lowercase			:				Dict 						=						None
           _lowercase			:				Dict 						=						False
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, **lowerCamelCase)			->  Dict:




           """simple docstring"""

           _lowercase			:				Dict 						=						'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
           _lowercase			:				Dict 						=						0
           _lowercase			:				Tuple 						=						0
           _lowercase			:				int 						=						[self.first_column] + ['Training Loss']
           if args.evaluation_strategy != IntervalStrategy.NO:
               column_names.append('Validation Loss')
           _lowercase			:				Union[str, Any] 						=						NotebookTrainingTracker(state.max_steps, lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, **lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Any 						=						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			:				str 						=						False
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=None, **lowerCamelCase)			->  Any:




           """simple docstring"""

           if not has_length(lowerCamelCase):
               return
           if self.prediction_bar is None:
               if self.training_tracker is not None:
                   _lowercase			:				Optional[int] 						=						self.training_tracker.add_child(len(lowerCamelCase))
               else:
                   _lowercase			:				Optional[int] 						=						NotebookProgressBar(len(lowerCamelCase))
               self.prediction_bar.update(1)
           else:
               self.prediction_bar.update(self.prediction_bar.value + 1)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, **lowerCamelCase)			->  Optional[int]:




           """simple docstring"""

           if self.prediction_bar is not None:
               self.prediction_bar.close()
           _lowercase			:				Any 						=						None
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=None, **lowerCamelCase)			->  List[Any]:




           """simple docstring"""

           if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
               _lowercase			:				Dict 						=						{'Training Loss': logs['loss']}
               # First column is necessarily Step sine we're not in epoch eval strategy
               _lowercase			:				List[Any] 						=						state.global_step
               self.training_tracker.write_line(lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=None, **lowerCamelCase)			->  List[str]:




           """simple docstring"""

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

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




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, **lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           self.training_tracker.update(
               state.global_step, comment=F'''Epoch {int(state.epoch)}/{state.num_train_epochs}''', force_update=lowerCamelCase)
           _lowercase			:				Any 						=						None




"""simple docstring"""




import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


lowerCAmelCase__				=					logging.get_logger(__name__)

# General docstring
lowerCAmelCase__				=					'PoolFormerConfig'

# Base docstring
lowerCAmelCase__				=					'sail/poolformer_s12'
lowerCAmelCase__				=					[1, 512, 7, 7]

# Image classification docstring
lowerCAmelCase__				=					'sail/poolformer_s12'
lowerCAmelCase__				=					'tabby, tabby cat'

lowerCAmelCase__				=					[
    'sail/poolformer_s12',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]



def 							_lowerCamelCase			(			__a,				__a = 0.0,				__a = False     ):
		if drop_prob == 0.0 or not training:
				return input
		SCREAMING_SNAKE_CASE_    			=			1 - drop_prob
		SCREAMING_SNAKE_CASE_    			=			(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
		SCREAMING_SNAKE_CASE_    			=			keep_prob + torch.rand(__a,				dtype=input.dtype,				device=input.device     )
		random_tensor.floor_()  # binarize
		SCREAMING_SNAKE_CASE_    			=			input.div(__a     ) * random_tensor
		return output





class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_ = None     ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_    			=			drop_prob

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									return drop_path(SCREAMING_SNAKE_CASE_							,							self.drop_prob							,							self.training     )







							def _lowercase       (self     ):

									"""simple docstring"""







									return "p={}".format(self.drop_prob     )







class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=None     ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_    			=			patch_size if isinstance(SCREAMING_SNAKE_CASE_							,							collections.abc.Iterable     ) else (patch_size, patch_size)
									SCREAMING_SNAKE_CASE_    			=			stride if isinstance(SCREAMING_SNAKE_CASE_							,							collections.abc.Iterable     ) else (stride, stride)
									SCREAMING_SNAKE_CASE_    			=			padding if isinstance(SCREAMING_SNAKE_CASE_							,							collections.abc.Iterable     ) else (padding, padding)

									SCREAMING_SNAKE_CASE_    			=			nn.Convad(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							kernel_size=SCREAMING_SNAKE_CASE_							,							stride=SCREAMING_SNAKE_CASE_							,							padding=SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			norm_layer(SCREAMING_SNAKE_CASE_     ) if norm_layer else nn.Identity()







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.projection(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			self.norm(SCREAMING_SNAKE_CASE_     )
									return embeddings







class 			snake_case  (				nn.GroupNorm ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__(1							,							SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )







class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_    			=			nn.AvgPoolad(SCREAMING_SNAKE_CASE_							,							stride=1							,							padding=pool_size // 2							,							count_include_pad=SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									return self.pool(SCREAMING_SNAKE_CASE_     ) - hidden_states







class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_    			=			nn.Convad(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							1     )
									SCREAMING_SNAKE_CASE_    			=			nn.Convad(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							1     )
									SCREAMING_SNAKE_CASE_    			=			PoolFormerDropPath(SCREAMING_SNAKE_CASE_     )
									if isinstance(config.hidden_act							,							SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			ACTaFN[config.hidden_act]
									else:
											SCREAMING_SNAKE_CASE_    			=			config.hidden_act







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.conva(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			self.act_fn(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			self.drop(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			self.conva(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			self.drop(SCREAMING_SNAKE_CASE_     )

									return hidden_states







class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_    			=			PoolFormerPooling(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			PoolFormerOutput(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			PoolFormerGroupNorm(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			PoolFormerGroupNorm(SCREAMING_SNAKE_CASE_     )

									# Useful for training neural nets
									SCREAMING_SNAKE_CASE_    			=			PoolFormerDropPath(SCREAMING_SNAKE_CASE_     ) if drop_path > 0.0 else nn.Identity()
									SCREAMING_SNAKE_CASE_    			=			config.use_layer_scale
									if config.use_layer_scale:
											SCREAMING_SNAKE_CASE_    			=			nn.Parameter(
											    config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE_)     )							,							requires_grad=SCREAMING_SNAKE_CASE_     )
											SCREAMING_SNAKE_CASE_    			=			nn.Parameter(
											    config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE_)     )							,							requires_grad=SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									if self.use_layer_scale:
											SCREAMING_SNAKE_CASE_    			=			self.pooling(self.before_norm(SCREAMING_SNAKE_CASE_     )     )
											SCREAMING_SNAKE_CASE_    			=			self.layer_scale_a.unsqueeze(-1     ).unsqueeze(-1     ) * pooling_output
											# First residual connection
											SCREAMING_SNAKE_CASE_    			=			hidden_states + self.drop_path(SCREAMING_SNAKE_CASE_     )
											SCREAMING_SNAKE_CASE_    			=			()

											SCREAMING_SNAKE_CASE_    			=			self.output(self.after_norm(SCREAMING_SNAKE_CASE_     )     )
											SCREAMING_SNAKE_CASE_    			=			self.layer_scale_a.unsqueeze(-1     ).unsqueeze(-1     ) * layer_output
											# Second residual connection
											SCREAMING_SNAKE_CASE_    			=			hidden_states + self.drop_path(SCREAMING_SNAKE_CASE_     )

											SCREAMING_SNAKE_CASE_    			=			(output,) + outputs
											return outputs

									else:
											SCREAMING_SNAKE_CASE_    			=			self.drop_path(self.pooling(self.before_norm(SCREAMING_SNAKE_CASE_     )     )     )
											# First residual connection
											SCREAMING_SNAKE_CASE_    			=			pooling_output + hidden_states
											SCREAMING_SNAKE_CASE_    			=			()

											# Second residual connection inside the PoolFormerOutput block
											SCREAMING_SNAKE_CASE_    			=			self.drop_path(self.output(self.after_norm(SCREAMING_SNAKE_CASE_     )     )     )
											SCREAMING_SNAKE_CASE_    			=			hidden_states + layer_output

											SCREAMING_SNAKE_CASE_    			=			(output,) + outputs
											return outputs







class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_    			=			config
									# stochastic depth decay rule
									SCREAMING_SNAKE_CASE_    			=			[x.item() for x in torch.linspace(0							,							config.drop_path_rate							,							sum(config.depths     )     )]

									# patch embeddings
									SCREAMING_SNAKE_CASE_    			=			[]
									for i in range(config.num_encoder_blocks     ):
											embeddings.append(
											    PoolFormerEmbeddings(
											        patch_size=config.patch_sizes[i]							,							stride=config.strides[i]							,							padding=config.padding[i]							,							num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]							,							hidden_size=config.hidden_sizes[i]							,							)     )
									SCREAMING_SNAKE_CASE_    			=			nn.ModuleList(SCREAMING_SNAKE_CASE_     )

									# Transformer blocks
									SCREAMING_SNAKE_CASE_    			=			[]
									SCREAMING_SNAKE_CASE_    			=			0
									for i in range(config.num_encoder_blocks     ):
											# each block consists of layers
											SCREAMING_SNAKE_CASE_    			=			[]
											if i != 0:
													cur += config.depths[i - 1]
											for j in range(config.depths[i]     ):
													layers.append(
													    PoolFormerLayer(
													        SCREAMING_SNAKE_CASE_							,							num_channels=config.hidden_sizes[i]							,							pool_size=config.pool_size							,							hidden_size=config.hidden_sizes[i]							,							intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio     )							,							drop_path=dpr[cur + j]							,							)     )
											blocks.append(nn.ModuleList(SCREAMING_SNAKE_CASE_     )     )

									SCREAMING_SNAKE_CASE_    			=			nn.ModuleList(SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=True     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			() if output_hidden_states else None

									SCREAMING_SNAKE_CASE_    			=			pixel_values
									for idx, layers in enumerate(zip(self.patch_embeddings							,							self.block     )     ):
											SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			layers
											# Get patch embeddings from hidden_states
											SCREAMING_SNAKE_CASE_    			=			embedding_layer(SCREAMING_SNAKE_CASE_     )
											# Send the embeddings through the blocks
											for _, blk in enumerate(SCREAMING_SNAKE_CASE_     ):
													SCREAMING_SNAKE_CASE_    			=			blk(SCREAMING_SNAKE_CASE_     )
													SCREAMING_SNAKE_CASE_    			=			layer_outputs[0]

											if output_hidden_states:
													SCREAMING_SNAKE_CASE_    			=			all_hidden_states + (hidden_states,)

									if not return_dict:
											return tuple(v for v in [hidden_states, all_hidden_states] if v is not None     )

									return BaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE_							,							hidden_states=SCREAMING_SNAKE_CASE_     )







class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   PoolFormerConfig
							UpperCAmelCase__      =   '''poolformer'''
							UpperCAmelCase__      =   '''pixel_values'''
							UpperCAmelCase__      =   True

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									if isinstance(SCREAMING_SNAKE_CASE_							,							(nn.Linear, nn.Convad)     ):
											module.weight.data.normal_(mean=0.0							,							std=self.config.initializer_range     )
											if module.bias is not None:
													module.bias.data.zero_()
									elif isinstance(SCREAMING_SNAKE_CASE_							,							nn.LayerNorm     ):
											module.bias.data.zero_()
											module.weight.data.fill_(1.0     )







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=False     ):

									"""simple docstring"""







									if isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			value







lowerCAmelCase__				=					r'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

lowerCAmelCase__				=					r'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`PoolFormerImageProcessor.__call__`] for details.\n'





@add_start_docstrings(
    '''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.'''							,      __lowercase							,      )
class 			snake_case  (				__lowercase ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			config

									SCREAMING_SNAKE_CASE_    			=			PoolFormerEncoder(SCREAMING_SNAKE_CASE_     )

									# Initialize weights and apply final processing
									self.post_init()

							def _lowercase       (self     ):

									"""simple docstring"""







									return self.embeddings.patch_embeddings







							@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_     )
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC							,							output_type=SCREAMING_SNAKE_CASE_							,							config_class=_CONFIG_FOR_DOC							,							modality='''vision'''							,							expected_output=_EXPECTED_OUTPUT_SHAPE							,							)
							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			(
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									SCREAMING_SNAKE_CASE_    			=			return_dict if return_dict is not None else self.config.use_return_dict

									if pixel_values is None:
											raise ValueError('''You have to specify pixel_values'''     )

									SCREAMING_SNAKE_CASE_    			=			self.encoder(
									    SCREAMING_SNAKE_CASE_							,							output_hidden_states=SCREAMING_SNAKE_CASE_							,							return_dict=SCREAMING_SNAKE_CASE_							,							)
									SCREAMING_SNAKE_CASE_    			=			encoder_outputs[0]

									if not return_dict:
											return (sequence_output, None) + encoder_outputs[1:]

									return BaseModelOutputWithNoAttention(
									    last_hidden_state=SCREAMING_SNAKE_CASE_							,							hidden_states=encoder_outputs.hidden_states							,							)







class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_    			=			nn.Linear(config.hidden_size							,							config.hidden_size     )







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.dense(SCREAMING_SNAKE_CASE_     )
									return output







@add_start_docstrings(
    '''
    PoolFormer Model transformer with an image classification head on top
    '''							,      __lowercase							,      )
class 			snake_case  (				__lowercase ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			config.num_labels
									SCREAMING_SNAKE_CASE_    			=			PoolFormerModel(SCREAMING_SNAKE_CASE_     )

									# Final norm
									SCREAMING_SNAKE_CASE_    			=			PoolFormerGroupNorm(config.hidden_sizes[-1]     )
									# Classifier head
									SCREAMING_SNAKE_CASE_    			=			(
									    nn.Linear(config.hidden_sizes[-1]							,							config.num_labels     ) if config.num_labels > 0 else nn.Identity()
									)

									# Initialize weights and apply final processing
									self.post_init()







							@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_     )
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT							,							output_type=SCREAMING_SNAKE_CASE_							,							config_class=_CONFIG_FOR_DOC							,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,							)
							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE_    			=			self.poolformer(
									    SCREAMING_SNAKE_CASE_							,							output_hidden_states=SCREAMING_SNAKE_CASE_							,							return_dict=SCREAMING_SNAKE_CASE_							,							)

									SCREAMING_SNAKE_CASE_    			=			outputs[0]

									SCREAMING_SNAKE_CASE_    			=			self.classifier(self.norm(SCREAMING_SNAKE_CASE_     ).mean([-2, -1]     )     )

									SCREAMING_SNAKE_CASE_    			=			None
									if labels is not None:
											if self.config.problem_type is None:
													if self.num_labels == 1:
															SCREAMING_SNAKE_CASE_    			=			'''regression'''
													elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
															SCREAMING_SNAKE_CASE_    			=			'''single_label_classification'''
													else:
															SCREAMING_SNAKE_CASE_    			=			'''multi_label_classification'''

											if self.config.problem_type == "regression":
													SCREAMING_SNAKE_CASE_    			=			MSELoss()
													if self.num_labels == 1:
															SCREAMING_SNAKE_CASE_    			=			loss_fct(logits.squeeze()							,							labels.squeeze()     )
													else:
															SCREAMING_SNAKE_CASE_    			=			loss_fct(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )
											elif self.config.problem_type == "single_label_classification":
													SCREAMING_SNAKE_CASE_    			=			CrossEntropyLoss()
													SCREAMING_SNAKE_CASE_    			=			loss_fct(logits.view(-1							,							self.num_labels     )							,							labels.view(-1     )     )
											elif self.config.problem_type == "multi_label_classification":
													SCREAMING_SNAKE_CASE_    			=			BCEWithLogitsLoss()
													SCREAMING_SNAKE_CASE_    			=			loss_fct(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

									if not return_dict:
											SCREAMING_SNAKE_CASE_    			=			(logits,) + outputs[2:]
											return ((loss,) + output) if loss is not None else output

									return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE_							,							logits=SCREAMING_SNAKE_CASE_							,							hidden_states=outputs.hidden_states     )