




import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


UpperCamelCase								=					logging.get_logger(__name__)

UpperCamelCase								=					{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase								=					{
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
UpperCamelCase								=					{
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
UpperCamelCase								=					{
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

UpperCamelCase								=					{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
UpperCamelCase								=					{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
UpperCamelCase								=					{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


UpperCamelCase								=					{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
UpperCamelCase								=					{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
UpperCamelCase								=					{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
    snake_case__ =				DPRContextEncoderTokenizer






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
    snake_case__ =				DPRQuestionEncoderTokenizer






UpperCamelCase								=					collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

UpperCamelCase								=					collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


UpperCamelCase								=					R'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '





@add_start_docstrings(UpperCamelCase__   )
class 			__lowerCamelCase	:




    """simple docstring"""

    def __call__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[str] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[str] = None       ,     SCREAMING_SNAKE_CASE__					:					Union[bool, str] = False       ,     SCREAMING_SNAKE_CASE__					:					Union[bool, str] = False       ,     SCREAMING_SNAKE_CASE__					:					Optional[int] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, TensorType]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     **SCREAMING_SNAKE_CASE__					:					List[Any]       ,     )				->	BatchEncoding:
         if titles is None and texts is None:
              return super().__call__(
                  SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       ,     max_length=SCREAMING_SNAKE_CASE__       ,     return_tensors=SCREAMING_SNAKE_CASE__       ,     return_attention_mask=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     )
         elif titles is None or texts is None:
              lowerCAmelCase__          =			titles if texts is None else texts
              return super().__call__(
                  SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       ,     max_length=SCREAMING_SNAKE_CASE__       ,     return_tensors=SCREAMING_SNAKE_CASE__       ,     return_attention_mask=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			titles if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) else [titles]
         lowerCAmelCase__          =			texts if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) else [texts]
         lowerCAmelCase__          =			len(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			questions if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) else [questions] * n_passages
         assert len(SCREAMING_SNAKE_CASE__       ) == len(
             SCREAMING_SNAKE_CASE__       ), f'There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE__       )} titles and {len(SCREAMING_SNAKE_CASE__       )} texts.'
         lowerCAmelCase__          =			super().__call__(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       )["input_ids"]
         lowerCAmelCase__          =			super().__call__(SCREAMING_SNAKE_CASE__       ,     add_special_tokens=SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       )["input_ids"]
         lowerCAmelCase__          =			{
             "input_ids": [
                 (encoded_question_and_title + encoded_text)[:max_length]
                 if max_length is not None and truncation
                 else encoded_question_and_title + encoded_text
                 for encoded_question_and_title, encoded_text in zip(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
             ]
         }
         if return_attention_mask is not False:
              lowerCAmelCase__          =			[]
              for input_ids in encoded_inputs["input_ids"]:
                   attention_mask.append([int(input_id != self.pad_token_id       ) for input_id in input_ids]       )
              lowerCAmelCase__          =			attention_mask
         return self.pad(SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     max_length=SCREAMING_SNAKE_CASE__       ,     return_tensors=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					BatchEncoding       ,     SCREAMING_SNAKE_CASE__					:					DPRReaderOutput       ,     SCREAMING_SNAKE_CASE__					:					int = 16       ,     SCREAMING_SNAKE_CASE__					:					int = 64       ,     SCREAMING_SNAKE_CASE__					:					int = 4       ,     )				->	List[DPRSpanPrediction]:
         lowerCAmelCase__          =			reader_input["input_ids"]
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			reader_output[:3]
         lowerCAmelCase__          =			len(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			sorted(range(SCREAMING_SNAKE_CASE__       )       ,     reverse=SCREAMING_SNAKE_CASE__       ,     key=relevance_logits.__getitem__       )
         lowerCAmelCase__          =			[]
         for doc_id in sorted_docs:
              lowerCAmelCase__          =			list(input_ids[doc_id]       )
              # assuming question & title information is at the beginning of the sequence
              lowerCAmelCase__          =			sequence_ids.index(self.sep_token_id       ,     2       ) + 1  # second sep id
              if sequence_ids[-1] == self.pad_token_id:
                   lowerCAmelCase__          =			sequence_ids.index(self.pad_token_id       )
              else:
                   lowerCAmelCase__          =			len(SCREAMING_SNAKE_CASE__       )

              lowerCAmelCase__          =			self._get_best_spans(
                  start_logits=start_logits[doc_id][passage_offset:sequence_len]       ,     end_logits=end_logits[doc_id][passage_offset:sequence_len]       ,     max_answer_length=SCREAMING_SNAKE_CASE__       ,     top_spans=SCREAMING_SNAKE_CASE__       ,     )
              for start_index, end_index in best_spans:
                   start_index += passage_offset
                   end_index += passage_offset
                   nbest_spans_predictions.append(
                       DPRSpanPrediction(
                           span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]       ,     relevance_score=relevance_logits[doc_id]       ,     doc_id=SCREAMING_SNAKE_CASE__       ,     start_index=SCREAMING_SNAKE_CASE__       ,     end_index=SCREAMING_SNAKE_CASE__       ,     text=self.decode(sequence_ids[start_index : end_index + 1]       )       ,     )       )
              if len(SCREAMING_SNAKE_CASE__       ) >= num_spans:
                   break
         return nbest_spans_predictions[:num_spans]


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     )				->	List[DPRSpanPrediction]:
         lowerCAmelCase__          =			[]
         for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE__       ):
              for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]       ):
                   scores.append(((start_index, start_index + answer_length), start_score + end_score)       )
         lowerCAmelCase__          =			sorted(SCREAMING_SNAKE_CASE__       ,     key=lambda SCREAMING_SNAKE_CASE__       : x[1]       ,     reverse=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[]
         for (start_index, end_index), score in scores:
              assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
              lowerCAmelCase__          =			end_index - start_index + 1
              assert length <= max_answer_length, f'Span is too long: {length} > {max_answer_length}'
              if any(
                  start_index <= prev_start_index <= prev_end_index <= end_index
                  or prev_start_index <= start_index <= end_index <= prev_end_index
                  for (prev_start_index, prev_end_index) in chosen_span_intervals       ):
                   continue
              chosen_span_intervals.append((start_index, end_index)       )

              if len(SCREAMING_SNAKE_CASE__       ) == top_spans:
                   break
         return chosen_span_intervals






@add_end_docstrings(UpperCamelCase__   )
class 			__lowerCamelCase	(   UpperCamelCase__     ,    UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				READER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				READER_PRETRAINED_INIT_CONFIGURATION
    snake_case__ =				["input_ids", "attention_mask"]
    snake_case__ =				DPRReaderTokenizer






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       )
