





'''simple docstring'''

import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


lowercase_					= """bert-base-cased"""
lowercase_					= """google/pegasus-xsum"""
lowercase_					= [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
lowercase_					= ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
lowercase_					= """patrickvonplaten/t5-tiny-random"""
lowercase_					= """sshleifer/bart-tiny-random"""
lowercase_					= """sshleifer/tiny-mbart"""
lowercase_					= """sshleifer/tiny-marian-en-de"""





def lowerCamelCase						(     __lowerCamelCase :    Path					,       __lowerCamelCase :    list    )						->Optional[Any]:
    _SCREAMING_SNAKE_CASE									=			"""\n""".join(__lowerCamelCase    )
    Path(__lowerCamelCase    ).open("""w"""    ).writelines(__lowerCamelCase    )





def lowerCamelCase						(     __lowerCamelCase :    Optional[Any]    )						->Optional[int]:
    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(__lowerCamelCase					,       F'{split}.source'    )					,       __lowerCamelCase    )
        _dump_articles(os.path.join(__lowerCamelCase					,       F'{split}.target'    )					,       __lowerCamelCase    )
    return tmp_dir






class 					a_     (				snake_case_       ):

    '''simple docstring'''


    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ] ,		)
    @slow
    def 			snake_case_(		self ,		A       )		->   str:
        _SCREAMING_SNAKE_CASE									=			AutoTokenizer.from_pretrained(A       )
        _SCREAMING_SNAKE_CASE									=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
        _SCREAMING_SNAKE_CASE									=			max(len(tokenizer.encode(A       )       ) for a in ARTICLES       )
        _SCREAMING_SNAKE_CASE									=			max(len(tokenizer.encode(A       )       ) for a in SUMMARIES       )
        _SCREAMING_SNAKE_CASE									=			4
        _SCREAMING_SNAKE_CASE									=			8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
        _SCREAMING_SNAKE_CASE									=			SeqaSeqDataset(
            A ,		data_dir=A ,		type_path="""train""" ,		max_source_length=A ,		max_target_length=A ,		src_lang=A ,		tgt_lang=A ,		)
        _SCREAMING_SNAKE_CASE									=			DataLoader(A ,		batch_size=2 ,		collate_fn=train_dataset.collate_fn       )
        for batch in dataloader:
            assert isinstance(A ,		A       )
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_src_len
            # show that targets are the same len
            assert batch["labels"].shape[1] == max_tgt_len
            if tok_name != MBART_TINY:
                continue
            # check language codes in correct place
            _SCREAMING_SNAKE_CASE									=			shift_tokens_right(batch["""labels"""] ,		tokenizer.pad_token_id       )
            assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
            assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

            break  # No need to test every batch


    @parameterized.expand([BART_TINY, BERT_BASE_CASED]       )
    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			AutoTokenizer.from_pretrained(A       )
        _SCREAMING_SNAKE_CASE									=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
        _SCREAMING_SNAKE_CASE									=			max(len(tokenizer.encode(A       )       ) for a in ARTICLES       )
        _SCREAMING_SNAKE_CASE									=			max(len(tokenizer.encode(A       )       ) for a in SUMMARIES       )
        _SCREAMING_SNAKE_CASE									=			4
        _SCREAMING_SNAKE_CASE									=			LegacySeqaSeqDataset(
            A ,		data_dir=A ,		type_path="""train""" ,		max_source_length=20 ,		max_target_length=A ,		)
        _SCREAMING_SNAKE_CASE									=			DataLoader(A ,		batch_size=2 ,		collate_fn=train_dataset.collate_fn       )
        for batch in dataloader:
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_len_source
            assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
            # show that targets were truncated
            assert batch["labels"].shape[1] == trunc_target  # Truncated
            assert max_len_target > trunc_target  # Truncated
            break  # No need to test every batch


    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""       )

        _SCREAMING_SNAKE_CASE									=			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
        _SCREAMING_SNAKE_CASE									=			tmp_dir.joinpath("""train.source"""       ).open().readlines()
        _SCREAMING_SNAKE_CASE									=			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
        pack_data_dir(A ,		A ,		128 ,		A       )
        _SCREAMING_SNAKE_CASE									=			{x.name for x in tmp_dir.iterdir()}
        _SCREAMING_SNAKE_CASE									=			{x.name for x in save_dir.iterdir()}
        _SCREAMING_SNAKE_CASE									=			save_dir.joinpath("""train.source"""       ).open().readlines()
        # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
        # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
        assert len(A       ) < len(A       )
        assert len(A       ) == 1
        assert len(packed_examples[0]       ) == sum(len(A       ) for x in orig_examples       )
        assert orig_paths == new_paths


    @pytest.mark.skipif(not FAIRSEQ_AVAILABLE ,		reason="""This test requires fairseq"""       )
    def 			snake_case_(		self       )		->   List[str]:
        if not FAIRSEQ_AVAILABLE:
            return
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._get_dataset(max_len=64       )
        _SCREAMING_SNAKE_CASE									=			64
        _SCREAMING_SNAKE_CASE									=			ds.make_dynamic_sampler(A ,		required_batch_size_multiple=A       )
        _SCREAMING_SNAKE_CASE									=			[len(A       ) for x in batch_sampler]
        assert len(set(A       )       ) > 1  # it's not dynamic batch size if every batch is the same length
        assert sum(A       ) == len(A       )  # no dropped or added examples
        _SCREAMING_SNAKE_CASE									=			DataLoader(A ,		batch_sampler=A ,		collate_fn=ds.collate_fn ,		num_workers=2       )
        _SCREAMING_SNAKE_CASE									=			[]
        _SCREAMING_SNAKE_CASE									=			[]
        for batch in data_loader:
            _SCREAMING_SNAKE_CASE									=			batch["""input_ids"""].shape
            _SCREAMING_SNAKE_CASE									=			src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            _SCREAMING_SNAKE_CASE									=			np.product(batch["""input_ids"""].shape       )
            num_src_per_batch.append(A       )
            if num_src_tokens > (max_tokens * 1.1):
                failures.append(A       )
        assert num_src_per_batch[0] == max(A       )
        if failures:
            raise AssertionError(f'too many tokens in {len(A       )} batches'       )


    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._get_dataset(max_len=512       )
        _SCREAMING_SNAKE_CASE									=			2
        _SCREAMING_SNAKE_CASE									=			ds.make_sortish_sampler(A ,		shuffle=A       )

        _SCREAMING_SNAKE_CASE									=			DataLoader(A ,		batch_size=A ,		collate_fn=ds.collate_fn ,		num_workers=2       )
        _SCREAMING_SNAKE_CASE									=			DataLoader(A ,		batch_size=A ,		collate_fn=ds.collate_fn ,		num_workers=2 ,		sampler=A       )

        _SCREAMING_SNAKE_CASE									=			tokenizer.pad_token_id

        def count_pad_tokens(A ,		A="input_ids"       ):
            return [batch[k].eq(A       ).sum().item() for batch in data_loader]

        assert sum(count_pad_tokens(A ,		k="""labels"""       )       ) < sum(count_pad_tokens(A ,		k="""labels"""       )       )
        assert sum(count_pad_tokens(A       )       ) < sum(count_pad_tokens(A       )       )
        assert len(A       ) == len(A       )


    def 			snake_case_(		self ,		A=1000 ,		A=128       )		->   Union[str, Any]:
        if os.getenv("""USE_REAL_DATA""" ,		A       ):
            _SCREAMING_SNAKE_CASE									=			"""examples/seq2seq/wmt_en_ro"""
            _SCREAMING_SNAKE_CASE									=			max_len * 2 * 64
            if not Path(A       ).joinpath("""train.len"""       ).exists():
                save_len_file(A ,		A       )
        else:
            _SCREAMING_SNAKE_CASE									=			"""examples/seq2seq/test_data/wmt_en_ro"""
            _SCREAMING_SNAKE_CASE									=			max_len * 4
            save_len_file(A ,		A       )

        _SCREAMING_SNAKE_CASE									=			AutoTokenizer.from_pretrained(A       )
        _SCREAMING_SNAKE_CASE									=			SeqaSeqDataset(
            A ,		data_dir=A ,		type_path="""train""" ,		max_source_length=A ,		max_target_length=A ,		n_obs=A ,		)
        return ds, max_tokens, tokenizer


    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._get_dataset()
        _SCREAMING_SNAKE_CASE									=			set(DistributedSortishSampler(A ,		256 ,		num_replicas=2 ,		rank=0 ,		add_extra_examples=A       )       )
        _SCREAMING_SNAKE_CASE									=			set(DistributedSortishSampler(A ,		256 ,		num_replicas=2 ,		rank=1 ,		add_extra_examples=A       )       )
        assert idsa.intersection(A       ) == set()





    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ] ,		)
    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			AutoTokenizer.from_pretrained(A ,		use_fast=A       )
        if tok_name == MBART_TINY:
            _SCREAMING_SNAKE_CASE									=			SeqaSeqDataset(
                A ,		data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       ) ,		type_path="""train""" ,		max_source_length=4 ,		max_target_length=8 ,		src_lang="""EN""" ,		tgt_lang="""FR""" ,		)
            _SCREAMING_SNAKE_CASE									=			train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
            _SCREAMING_SNAKE_CASE									=			SeqaSeqDataset(
                A ,		data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       ) ,		type_path="""train""" ,		max_source_length=4 ,		max_target_length=8 ,		)
            _SCREAMING_SNAKE_CASE									=			train_dataset.dataset_kwargs
            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
            assert len(A       ) == 1 if tok_name == BART_TINY else len(A       ) == 0










'''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


lowercase_					= logging.get_logger(__name__)

# General docstring
lowercase_					= """PoolFormerConfig"""

# Base docstring
lowercase_					= """sail/poolformer_s12"""
lowercase_					= [1, 512, 7, 7]

# Image classification docstring
lowercase_					= """sail/poolformer_s12"""
lowercase_					= """tabby, tabby cat"""

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





def lowerCamelCase						(     __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    float = 0.0					,       __lowerCamelCase :    bool = False    )						->int:
    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(__lowerCamelCase					,       dtype=input.dtype					,       device=input.device    )
    random_tensor.floor_()  # binarize
    _SCREAMING_SNAKE_CASE									=			input.div(__lowerCamelCase    ) * random_tensor
    return output


class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A = None       )		->   None:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			drop_prob


    def 			snake_case_(		self ,		A       )		->   torch.Tensor:
        return drop_path(A ,		self.drop_prob ,		self.training       )





    def 			snake_case_(		self       )		->   str:
        return "p={}".format(self.drop_prob       )




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		A ,		A ,		A=None       )		->   Union[str, Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			patch_size if isinstance(A ,		collections.abc.Iterable       ) else (patch_size, patch_size)
        _SCREAMING_SNAKE_CASE									=			stride if isinstance(A ,		collections.abc.Iterable       ) else (stride, stride)
        _SCREAMING_SNAKE_CASE									=			padding if isinstance(A ,		collections.abc.Iterable       ) else (padding, padding)

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





    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			self.projection(A       )
        _SCREAMING_SNAKE_CASE									=			self.norm(A       )
        return embeddings




class 					a_     (				nn.GroupNorm       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		**A       )		->   Union[str, Any]:
        super().__init__(1 ,		A ,		**A       )




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   Union[str, Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			nn.AvgPoolad(A ,		stride=1 ,		padding=pool_size // 2 ,		count_include_pad=A       )





    def 			snake_case_(		self ,		A       )		->   Union[str, Any]:
        return self.pool(A       ) - hidden_states




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		A       )		->   List[Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			nn.Convad(A ,		A ,		1       )
        _SCREAMING_SNAKE_CASE									=			nn.Convad(A ,		A ,		1       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerDropPath(A       )
        if isinstance(config.hidden_act ,		A       ):
            _SCREAMING_SNAKE_CASE									=			ACTaFN[config.hidden_act]
        else:
            _SCREAMING_SNAKE_CASE									=			config.hidden_act





    def 			snake_case_(		self ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			self.conva(A       )
        _SCREAMING_SNAKE_CASE									=			self.act_fn(A       )
        _SCREAMING_SNAKE_CASE									=			self.drop(A       )
        _SCREAMING_SNAKE_CASE									=			self.conva(A       )
        _SCREAMING_SNAKE_CASE									=			self.drop(A       )

        return hidden_states




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Union[str, Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			PoolFormerPooling(A       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerOutput(A ,		A ,		A ,		A       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerGroupNorm(A       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerGroupNorm(A       )

        # Useful for training neural nets
        _SCREAMING_SNAKE_CASE									=			PoolFormerDropPath(A       ) 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((A)       ) ,		requires_grad=A       )
            _SCREAMING_SNAKE_CASE									=			nn.Parameter(
                config.layer_scale_init_value * torch.ones((A)       ) ,		requires_grad=A       )





    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        if self.use_layer_scale:
            _SCREAMING_SNAKE_CASE									=			self.pooling(self.before_norm(A       )       )
            _SCREAMING_SNAKE_CASE									=			self.layer_scale_a.unsqueeze(-1       ).unsqueeze(-1       ) * pooling_output
            # First residual connection
            _SCREAMING_SNAKE_CASE									=			hidden_states + self.drop_path(A       )
            _SCREAMING_SNAKE_CASE									=			()

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

            _SCREAMING_SNAKE_CASE									=			(output,) + outputs
            return outputs

        else:
            _SCREAMING_SNAKE_CASE									=			self.drop_path(self.pooling(self.before_norm(A       )       )       )
            # 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(A       )       )       )
            _SCREAMING_SNAKE_CASE									=			hidden_states + layer_output

            _SCREAMING_SNAKE_CASE									=			(output,) + outputs
            return outputs




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   Any:
        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(A       )

        # 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(
                        A ,		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(A       )       )

        _SCREAMING_SNAKE_CASE									=			nn.ModuleList(A       )





    def 			snake_case_(		self ,		A ,		A=False ,		A=True       )		->   List[Any]:
        _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(A       )
            # Send the embeddings through the blocks
            for _, blk in enumerate(A       ):
                _SCREAMING_SNAKE_CASE									=			blk(A       )
                _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=A ,		hidden_states=A       )




class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = PoolFormerConfig
    UpperCamelCase     = '''poolformer'''
    UpperCamelCase     = '''pixel_values'''
    UpperCamelCase     = True


    def 			snake_case_(		self ,		A       )		->   int:
        if isinstance(A ,		(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(A ,		nn.LayerNorm       ):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0       )





    def 			snake_case_(		self ,		A ,		A=False       )		->   Dict:
        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			value




lowercase_					= R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

lowercase_					= R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`PoolFormerImageProcessor.__call__`] for details.
"""


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

    '''simple docstring'''


    def __init__(		self ,		A       )		->   int:
        super().__init__(A       )
        _SCREAMING_SNAKE_CASE									=			config

        _SCREAMING_SNAKE_CASE									=			PoolFormerEncoder(A       )

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


    def 			snake_case_(		self       )		->   Any:
        return self.embeddings.patch_embeddings





    @add_start_docstrings_to_model_forward(A       )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC ,		output_type=A ,		config_class=_CONFIG_FOR_DOC ,		modality="""vision""" ,		expected_output=_EXPECTED_OUTPUT_SHAPE ,		)
    def 			snake_case_(		self ,		A = None ,		A = None ,		A = None ,		)		->   Union[Tuple, BaseModelOutputWithNoAttention]:
        _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(
            A ,		output_hidden_states=A ,		return_dict=A ,		)
        _SCREAMING_SNAKE_CASE									=			encoder_outputs[0]

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

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




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   Dict:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			nn.Linear(config.hidden_size ,		config.hidden_size       )





    def 			snake_case_(		self ,		A       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.dense(A       )
        return output




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

    '''simple docstring'''


    def __init__(		self ,		A       )		->   Optional[Any]:
        super().__init__(A       )
        _SCREAMING_SNAKE_CASE									=			config.num_labels
        _SCREAMING_SNAKE_CASE									=			PoolFormerModel(A       )

        # 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(A       )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT ,		output_type=A ,		config_class=_CONFIG_FOR_DOC ,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,		)
    def 			snake_case_(		self ,		A = None ,		A = None ,		A = None ,		A = None ,		)		->   Union[Tuple, ImageClassifierOutputWithNoAttention]:
        _SCREAMING_SNAKE_CASE									=			return_dict if return_dict is not None else self.config.use_return_dict

        _SCREAMING_SNAKE_CASE									=			self.poolformer(
            A ,		output_hidden_states=A ,		return_dict=A ,		)

        _SCREAMING_SNAKE_CASE									=			outputs[0]

        _SCREAMING_SNAKE_CASE									=			self.classifier(self.norm(A       ).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(A ,		A       )
            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(A ,		A       )

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

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



