
"""simple docstring"""




import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch

import pyarrow as pa
import pytest
import requests
from packaging import version

from datasets import config


if config.PY_VERSION < version.parse('3.8'):
						import importlib_metadata
else:
						import importlib.metadata as importlib_metadata



def 							_lowerCamelCase			(			__a,				__a=False     ):
		try:
				SCREAMING_SNAKE_CASE_    			=			os.environ[key]
		except KeyError:
				# KEY isn't set, default to `default`.
				SCREAMING_SNAKE_CASE_    			=			default
		else:
				# KEY is set, convert it to True or False.
				try:
						SCREAMING_SNAKE_CASE_    			=			strtobool(__a     )
				except ValueError:
						# More values are supported, but let's keep the message simple.
						raise ValueError(F'If set, {key} must be yes or no.'     )
		return _value


lowerCAmelCase__				=					parse_flag_from_env('RUN_SLOW', default=False)
lowerCAmelCase__				=					parse_flag_from_env('RUN_REMOTE', default=False)
lowerCAmelCase__				=					parse_flag_from_env('RUN_LOCAL', default=True)
lowerCAmelCase__				=					parse_flag_from_env('RUN_PACKAGED', default=True)

# Compression
lowerCAmelCase__				=					pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='test requires lz4')
lowerCAmelCase__				=					pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='test requires py7zr')
lowerCAmelCase__				=					pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='test requires zstandard')

# Audio
lowerCAmelCase__				=					pytest.mark.skipif(
    # On Windows and OS X, soundfile installs sndfile
    find_spec('soundfile') is None or version.parse(importlib_metadata.version('soundfile')) < version.parse('0.12.0'),
    reason='test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ',
)

# Beam
lowerCAmelCase__				=					pytest.mark.skipif(
    not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('0.3.2'),
    reason='test requires apache-beam and a compatible dill version',
)

# Dill-cloudpickle compatibility
lowerCAmelCase__				=					pytest.mark.skipif(
    config.DILL_VERSION <= version.parse('0.3.2'),
    reason='test requires dill>0.3.2 for cloudpickle compatibility',
)

# Windows
lowerCAmelCase__				=					pytest.mark.skipif(
    sys.platform == 'win32',
    reason='test should not be run on Windows',
)



def 							_lowerCamelCase			(			__a     ):
		try:
				import faiss  # noqa
		except ImportError:
				SCREAMING_SNAKE_CASE_    			=			unittest.skip('''test requires faiss'''     )(__a     )
		return test_case



def 							_lowerCamelCase			(			__a     ):
		try:
				import regex  # noqa
		except ImportError:
				SCREAMING_SNAKE_CASE_    			=			unittest.skip('''test requires regex'''     )(__a     )
		return test_case



def 							_lowerCamelCase			(			__a     ):
		try:
				import elasticsearch  # noqa
		except ImportError:
				SCREAMING_SNAKE_CASE_    			=			unittest.skip('''test requires elasticsearch'''     )(__a     )
		return test_case



def 							_lowerCamelCase			(			__a     ):
		try:
				import sqlalchemy  # noqa
		except ImportError:
				SCREAMING_SNAKE_CASE_    			=			unittest.skip('''test requires sqlalchemy'''     )(__a     )
		return test_case



def 							_lowerCamelCase			(			__a     ):
		if not config.TORCH_AVAILABLE:
				SCREAMING_SNAKE_CASE_    			=			unittest.skip('''test requires PyTorch'''     )(__a     )
		return test_case



def 							_lowerCamelCase			(			__a     ):
		if not config.TF_AVAILABLE:
				SCREAMING_SNAKE_CASE_    			=			unittest.skip('''test requires TensorFlow'''     )(__a     )
		return test_case



def 							_lowerCamelCase			(			__a     ):
		if not config.JAX_AVAILABLE:
				SCREAMING_SNAKE_CASE_    			=			unittest.skip('''test requires JAX'''     )(__a     )
		return test_case



def 							_lowerCamelCase			(			__a     ):
		if not config.PIL_AVAILABLE:
				SCREAMING_SNAKE_CASE_    			=			unittest.skip('''test requires Pillow'''     )(__a     )
		return test_case



def 							_lowerCamelCase			(			__a     ):
		try:
				import transformers  # noqa F401
		except ImportError:
				return unittest.skip('''test requires transformers'''     )(__a     )
		else:
				return test_case



def 							_lowerCamelCase			(			__a     ):
		try:
				import tiktoken  # noqa F401
		except ImportError:
				return unittest.skip('''test requires tiktoken'''     )(__a     )
		else:
				return test_case



def 							_lowerCamelCase			(			__a     ):
		try:
				import spacy  # noqa F401
		except ImportError:
				return unittest.skip('''test requires spacy'''     )(__a     )
		else:
				return test_case



def 							_lowerCamelCase			(			__a     ):

		def _require_spacy_model(__a     ):
				try:
						import spacy  # noqa F401

						spacy.load(__a     )
				except ImportError:
						return unittest.skip('''test requires spacy'''     )(__a     )
				except OSError:
						return unittest.skip('''test requires spacy model \'{}\''''.format(__a     )     )(__a     )
				else:
						return test_case

		return _require_spacy_model



def 							_lowerCamelCase			(			__a     ):
		try:
				import pyspark  # noqa F401
		except ImportError:
				return unittest.skip('''test requires pyspark'''     )(__a     )
		else:
				return test_case



def 							_lowerCamelCase			(			__a     ):
		try:
				import joblibspark  # noqa F401
		except ImportError:
				return unittest.skip('''test requires joblibspark'''     )(__a     )
		else:
				return test_case



def 							_lowerCamelCase			(			__a     ):
		if not _run_slow_tests or _run_slow_tests == 0:
				SCREAMING_SNAKE_CASE_    			=			unittest.skip('''test is slow'''     )(__a     )
		return test_case



def 							_lowerCamelCase			(			__a     ):
		if not _run_local_tests or _run_local_tests == 0:
				SCREAMING_SNAKE_CASE_    			=			unittest.skip('''test is local'''     )(__a     )
		return test_case



def 							_lowerCamelCase			(			__a     ):
		if not _run_packaged_tests or _run_packaged_tests == 0:
				SCREAMING_SNAKE_CASE_    			=			unittest.skip('''test is packaged'''     )(__a     )
		return test_case



def 							_lowerCamelCase			(			__a     ):
		if not _run_remote_tests or _run_remote_tests == 0:
				SCREAMING_SNAKE_CASE_    			=			unittest.skip('''test requires remote'''     )(__a     )
		return test_case



def 							_lowerCamelCase			(			*__a     ):
		def decorate(cls     ):
				for name, fn in cls.__dict__.items():
						if callable(__a     ) and name.startswith('''test'''     ):
								for decorator in decorators:
										SCREAMING_SNAKE_CASE_    			=			decorator(__a     )
								setattr(cls,				__a,				__a     )
				return cls

		return decorate





class 			snake_case  (				__lowercase ):
							pass







class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   0
							UpperCAmelCase__      =   1
							UpperCAmelCase__      =   2







@contextmanager
def 							_lowerCamelCase			(			__a=OfflineSimulationMode.CONNECTION_FAILS,				__a=1E-16     ):
		SCREAMING_SNAKE_CASE_    			=			requests.Session().request

		def timeout_request(__a,				__a,				__a,				**__a     ):
				# Change the url to an invalid url so that the connection hangs
				SCREAMING_SNAKE_CASE_    			=			'''https://10.255.255.1'''
				if kwargs.get('''timeout'''     ) is None:
						raise RequestWouldHangIndefinitelyError(
						    F'Tried a call to {url} in offline mode with no timeout set. Please set a timeout.'     )
				SCREAMING_SNAKE_CASE_    			=			timeout
				try:
						return online_request(__a,				__a,				**__a     )
				except Exception as e:
						# The following changes in the error are just here to make the offline timeout error prettier
						SCREAMING_SNAKE_CASE_    			=			url
						SCREAMING_SNAKE_CASE_    			=			e.args[0]
						SCREAMING_SNAKE_CASE_    			=			(max_retry_error.args[0].replace('''10.255.255.1''',				F'OfflineMock[{url}]'     ),)
						SCREAMING_SNAKE_CASE_    			=			(max_retry_error,)
						raise

		def raise_connection_error(__a,				__a,				**__a     ):
				raise requests.ConnectionError('''Offline mode is enabled.''',				request=__a     )

		if mode is OfflineSimulationMode.CONNECTION_FAILS:
				with patch('''requests.Session.send''',				__a     ):
						yield
		elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
				# inspired from https://stackoverflow.com/a/904609
				with patch('''requests.Session.request''',				__a     ):
						yield
		elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
				with patch('''datasets.config.HF_DATASETS_OFFLINE''',				__a     ):
						yield
		else:
				raise ValueError('''Please use a value from the OfflineSimulationMode enum.'''     )



@contextmanager
def 							_lowerCamelCase			(			*__a,				**__a     ):
		SCREAMING_SNAKE_CASE_    			=			str(Path().resolve()     )
		with tempfile.TemporaryDirectory(*__a,				**__a     ) as tmp_dir:
				try:
						os.chdir(__a     )
						yield
				finally:
						os.chdir(__a     )



@contextmanager
def 							_lowerCamelCase			(			):
		import gc

		gc.collect()
		SCREAMING_SNAKE_CASE_    			=			pa.total_allocated_bytes()
		yield
		assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."



@contextmanager
def 							_lowerCamelCase			(			):
		import gc

		gc.collect()
		SCREAMING_SNAKE_CASE_    			=			pa.total_allocated_bytes()
		yield
		assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."



def 							_lowerCamelCase			(			__a,				__a     ):
		return deepcopy(__a     ).integers(0,				100,				10     ).tolist() == deepcopy(__a     ).integers(0,				100,				10     ).tolist()



def 							_lowerCamelCase			(			__a     ):
		import decorator
		from requests.exceptions import HTTPError

		def _wrapper(__a,				*__a,				**__a     ):
				try:
						return func(*__a,				**__a     )
				except HTTPError as err:
						if str(__a     ).startswith('''500'''     ) or str(__a     ).startswith('''502'''     ):
								pytest.xfail(str(__a     )     )
						raise err

		return decorator.decorator(_wrapper,				__a     )





class 			snake_case  :

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

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			returncode
									SCREAMING_SNAKE_CASE_    			=			stdout
									SCREAMING_SNAKE_CASE_    			=			stderr







async def 							_lowerCamelCase			(			__a,				__a     ):
		while True:
				SCREAMING_SNAKE_CASE_    			=			await stream.readline()
				if line:
						callback(__a     )
				else:
						break



async def 							_lowerCamelCase			(			__a,				__a=None,				__a=None,				__a=None,				__a=False,				__a=False     ):
		if echo:
				print('''\nRunning: ''',				''' '''.join(__a     )     )

		SCREAMING_SNAKE_CASE_    			=			await asyncio.create_subprocess_exec(
		    cmd[0],				*cmd[1:],				stdin=__a,				stdout=asyncio.subprocess.PIPE,				stderr=asyncio.subprocess.PIPE,				env=__a,				)

		# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
		# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
		#
		# If it starts hanging, will need to switch to the following code. The problem is that no data
		# will be seen until it's done and if it hangs for example there will be no debug info.
		# out, err = await p.communicate()
		# return _RunOutput(p.returncode, out, err)

		SCREAMING_SNAKE_CASE_    			=			[]
		SCREAMING_SNAKE_CASE_    			=			[]

		def tee(__a,				__a,				__a,				__a=""     ):
				SCREAMING_SNAKE_CASE_    			=			line.decode('''utf-8'''     ).rstrip()
				sink.append(__a     )
				if not quiet:
						print(__a,				__a,				file=__a     )

    # XXX: the timeout doesn't seem to make any difference here
		await asyncio.wait(
		    [
		        _read_stream(p.stdout,				lambda __a     : tee(__a,				__a,				sys.stdout,				label='''stdout:'''     )     ),
		        _read_stream(p.stderr,				lambda __a     : tee(__a,				__a,				sys.stderr,				label='''stderr:'''     )     ),
		    ],				timeout=__a,				)
		return _RunOutput(await p.wait(),				__a,				__a     )



def 							_lowerCamelCase			(			__a,				__a=None,				__a=None,				__a=180,				__a=False,				__a=True     ):
		SCREAMING_SNAKE_CASE_    			=			asyncio.get_event_loop()
		SCREAMING_SNAKE_CASE_    			=			loop.run_until_complete(
		    _stream_subprocess(__a,				env=__a,				stdin=__a,				timeout=__a,				quiet=__a,				echo=__a     )     )

		SCREAMING_SNAKE_CASE_    			=			''' '''.join(__a     )
		if result.returncode > 0:
				SCREAMING_SNAKE_CASE_    			=			'''\n'''.join(result.stderr     )
				raise RuntimeError(
				    F'\'{cmd_str}\' failed with returncode {result.returncode}\n\n'
				    F'The combined stderr from workers follows:\n{stderr}'     )

		# check that the subprocess actually did run and produced some output, should the test rely on
		# the remote side to do the testing
		if not result.stdout and not result.stderr:
				raise RuntimeError(F'\'{cmd_str}\' produced no output.'     )

		return result



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			os.environ.get('''PYTEST_XDIST_WORKER''',				'''gw0'''     )
		SCREAMING_SNAKE_CASE_    			=			re.sub(r'''^gw''',				'''''',				__a,				0,				re.M     )
		return int(__a     )



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			29_500
		SCREAMING_SNAKE_CASE_    			=			pytest_xdist_worker_id()
		return port + uniq_delta

"""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     )