




from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


UpperCamelCase								=					'\\n@misc{wu2016googles,\n      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n'

UpperCamelCase								=					'\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'

UpperCamelCase								=					'\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    \'google_bleu\': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results["google_bleu"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results["google_bleu"], 2))\n        0.4\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,    _KWARGS_DESCRIPTION   )
class 			__lowerCamelCase	(   datasets.Metric   ):




				"""simple docstring"""

				def        a				(	self					:					Union[str, Any]       )				->	MetricInfo:
									return datasets.MetricInfo(
									    description=_DESCRIPTION       ,     citation=_CITATION       ,     inputs_description=_KWARGS_DESCRIPTION       ,     features=datasets.Features(
									        {
									            "predictions": datasets.Sequence(datasets.Value("string"       ,     id="token"       )       ,     id="sequence"       ),
									            "references": datasets.Sequence(
									                datasets.Sequence(datasets.Value("string"       ,     id="token"       )       ,     id="sequence"       )       ,     id="references"       ),
									        }       )       ,     )


				def        a				(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any] = 1       ,     SCREAMING_SNAKE_CASE__					:					List[str] = 4       ,     )				->	Dict[str, float]:
									return {
									    "google_bleu": gleu_score.corpus_gleu(
									        list_of_references=SCREAMING_SNAKE_CASE__       ,     hypotheses=SCREAMING_SNAKE_CASE__       ,     min_len=SCREAMING_SNAKE_CASE__       ,     max_len=SCREAMING_SNAKE_CASE__       )
									}






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


UpperCamelCase								=					logging.get_logger(__name__)

# General docstring
UpperCamelCase								=					'PoolFormerConfig'

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

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

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



def 							_A       (						lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: float = 0.0       ,    lowerCAmelCase_					: bool = False      ):
     """simple docstring"""

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





class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[float] = None       )				->	None:
         super().__init__()
         lowerCAmelCase__          =			drop_prob


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					torch.Tensor       )				->	torch.Tensor:
         return drop_path(SCREAMING_SNAKE_CASE__       ,     self.drop_prob       ,     self.training       )


    def        a				(	self					:					Optional[Any]       )				->	str:
         return "p={}".format(self.drop_prob       )






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					str=None       )				->	Optional[Any]:
         super().__init__()
         lowerCAmelCase__          =			patch_size if isinstance(SCREAMING_SNAKE_CASE__       ,     collections.abc.Iterable       ) else (patch_size, patch_size)
         lowerCAmelCase__          =			stride if isinstance(SCREAMING_SNAKE_CASE__       ,     collections.abc.Iterable       ) else (stride, stride)
         lowerCAmelCase__          =			padding if isinstance(SCREAMING_SNAKE_CASE__       ,     collections.abc.Iterable       ) else (padding, padding)

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


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			self.projection(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.norm(SCREAMING_SNAKE_CASE__       )
         return embeddings






class 			__lowerCamelCase	(   nn.GroupNorm   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     **SCREAMING_SNAKE_CASE__					:					Any       )				->	Dict:
         super().__init__(1       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	List[Any]:
         super().__init__()
         lowerCAmelCase__          =			nn.AvgPoolad(SCREAMING_SNAKE_CASE__       ,     stride=1       ,     padding=pool_size // 2       ,     count_include_pad=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Optional[Any]:
         return self.pool(SCREAMING_SNAKE_CASE__       ) - hidden_states






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Dict:
         super().__init__()
         lowerCAmelCase__          =			nn.Convad(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     1       )
         lowerCAmelCase__          =			nn.Convad(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     1       )
         lowerCAmelCase__          =			PoolFormerDropPath(SCREAMING_SNAKE_CASE__       )
         if isinstance(config.hidden_act       ,     SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			ACTaFN[config.hidden_act]
         else:
              lowerCAmelCase__          =			config.hidden_act


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	List[Any]:
         lowerCAmelCase__          =			self.conva(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.act_fn(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.drop(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.conva(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.drop(SCREAMING_SNAKE_CASE__       )

         return hidden_states






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Optional[Any]:
         super().__init__()
         lowerCAmelCase__          =			PoolFormerPooling(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PoolFormerOutput(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__       )

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


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	int:
         if self.use_layer_scale:
              lowerCAmelCase__          =			self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__       )       )
              lowerCAmelCase__          =			self.layer_scale_a.unsqueeze(-1       ).unsqueeze(-1       ) * pooling_output
              # First residual connection
              lowerCAmelCase__          =			hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			()

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

              lowerCAmelCase__          =			(output,) + outputs
              return outputs

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

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

              lowerCAmelCase__          =			(output,) + outputs
              return outputs






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	Any:
         super().__init__()
         lowerCAmelCase__          =			config
         # stochastic depth decay rule
         lowerCAmelCase__          =			[x.item() for x in torch.linspace(0       ,     config.drop_path_rate       ,     sum(config.depths       )       )]

         # patch embeddings
         lowerCAmelCase__          =			[]
         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]       ,     )       )
         lowerCAmelCase__          =			nn.ModuleList(SCREAMING_SNAKE_CASE__       )

         # Transformer blocks
         lowerCAmelCase__          =			[]
         lowerCAmelCase__          =			0
         for i in range(config.num_encoder_blocks       ):
              # each block consists of layers
              lowerCAmelCase__          =			[]
              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__       )       )

         lowerCAmelCase__          =			nn.ModuleList(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int=False       ,     SCREAMING_SNAKE_CASE__					:					List[str]=True       )				->	Dict:
         lowerCAmelCase__          =			() if output_hidden_states else None

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

              if output_hidden_states:
                   lowerCAmelCase__          =			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 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				PoolFormerConfig
    snake_case__ =				"poolformer"
    snake_case__ =				"pixel_values"
    snake_case__ =				True


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	List[Any]:
         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        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=False       )				->	Tuple:
         if isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			value






UpperCamelCase								=					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'

UpperCamelCase								=					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."     ,    UpperCamelCase__     ,    )
class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Optional[Any]:
         super().__init__(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			config

         lowerCAmelCase__          =			PoolFormerEncoder(SCREAMING_SNAKE_CASE__       )

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


    def        a				(	self					:					Optional[int]       )				->	Optional[Any]:
         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        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.FloatTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     )				->	Union[Tuple, BaseModelOutputWithNoAttention]:
         lowerCAmelCase__          =			(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         lowerCAmelCase__          =			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"       )

         lowerCAmelCase__          =			self.encoder(
             SCREAMING_SNAKE_CASE__       ,     output_hidden_states=SCREAMING_SNAKE_CASE__       ,     return_dict=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			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 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	List[str]:
         super().__init__()
         lowerCAmelCase__          =			nn.Linear(config.hidden_size       ,     config.hidden_size       )


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	int:
         lowerCAmelCase__          =			self.dense(SCREAMING_SNAKE_CASE__       )
         return output






@add_start_docstrings(
    "\n    PoolFormer Model transformer with an image classification head on top\n    "     ,    UpperCamelCase__     ,    )
class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Optional[int]:
         super().__init__(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			config.num_labels
         lowerCAmelCase__          =			PoolFormerModel(SCREAMING_SNAKE_CASE__       )

         # Final norm
         lowerCAmelCase__          =			PoolFormerGroupNorm(config.hidden_sizes[-1]       )
         # Classifier head
         lowerCAmelCase__          =			(
             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        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.FloatTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.LongTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     )				->	Union[Tuple, ImageClassifierOutputWithNoAttention]:
         lowerCAmelCase__          =			return_dict if return_dict is not None else self.config.use_return_dict

         lowerCAmelCase__          =			self.poolformer(
             SCREAMING_SNAKE_CASE__       ,     output_hidden_states=SCREAMING_SNAKE_CASE__       ,     return_dict=SCREAMING_SNAKE_CASE__       ,     )

         lowerCAmelCase__          =			outputs[0]

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

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

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

         if not return_dict:
              lowerCAmelCase__          =			(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       )
