
'''simple docstring'''

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

UpperCamelCase_        =							2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
UpperCamelCase_        =							{
    # fairseq:
    """wmt19-ru-en""": {"""length_penalty""": 1.1},
    """wmt19-en-ru""": {"""length_penalty""": 1.1_5},
    """wmt19-en-de""": {"""length_penalty""": 1.0},
    """wmt19-de-en""": {"""length_penalty""": 1.1},
    # allenai:
    """wmt16-en-de-dist-12-1""": {"""length_penalty""": 0.6},
    """wmt16-en-de-dist-6-1""": {"""length_penalty""": 0.6},
    """wmt16-en-de-12-1""": {"""length_penalty""": 0.8},
    """wmt19-de-en-6-6-base""": {"""length_penalty""": 0.6},
    """wmt19-de-en-6-6-big""": {"""length_penalty""": 0.6},
}

# this remaps the different models to their organization names
UpperCamelCase_        =							{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
     UpperCamelCase_        =							"""facebook"""
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
     UpperCamelCase_        =							"""allenai"""


def 						_UpperCAmelCase					(	_lowerCamelCase    :							List[str]		)     ->    str:
  # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
  # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
  _lowerCAmelCase							:	Optional[Any] 					=       dict((re.sub(R"""@@$"""	,   """"""	,   __a		), v) if k.endswith("""@@"""		) else (re.sub(R"""$"""	,   """</w>"""	,   __a		), v) for k, v in d.items()		)
  _lowerCAmelCase							:	Tuple 					=       """<s> <pad> </s> <unk>""".split()
  # restore the special tokens
  for k in keep_keys:
    del da[f'{k}</w>']
    _lowerCAmelCase							:	int 					=       d[k]  # restore
  return da


def 						_UpperCAmelCase					(	_lowerCamelCase    :							int	,   _lowerCamelCase    :							str		)     ->    List[Any]:
  # prep
  assert os.path.exists(__a		)
  os.makedirs(__a	,   exist_ok=__a		)
  print(f'Writing results to {pytorch_dump_folder_path}'		)

  # handle various types of models

  _lowerCAmelCase							:	Dict 					=       basename(__a		)
  _lowerCAmelCase							:	Tuple 					=       dirname(__a		)

  _lowerCAmelCase							:	int 					=       fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
  _lowerCAmelCase							:	Any 					=       cls.hub_models()
  _lowerCAmelCase							:	List[str] 					=       {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
  _lowerCAmelCase							:	str 					=       """."""
  # note: since the model dump is old, fairseq has upgraded its model some
  # time later, and it does a whole lot of rewrites and splits on the saved
  # weights, therefore we can't use torch.load() directly on the model file.
  # see: upgrade_state_dict(state_dict) in fairseq_model.py
  print(f'using checkpoint {checkpoint_file}'		)
  _lowerCAmelCase							:	Tuple 					=       hub_utils.from_pretrained(
      __a	,   __a	,   __a	,   archive_map=__a	,   **__a		)

  _lowerCAmelCase							:	Any 					=       vars(chkpt["""args"""]["""model"""]		)

  _lowerCAmelCase							:	Union[str, Any] 					=       args["""source_lang"""]
  _lowerCAmelCase							:	Tuple 					=       args["""target_lang"""]

  _lowerCAmelCase							:	List[Any] 					=       dirname(__a		)
  _lowerCAmelCase							:	Optional[int] 					=       basename(__a		)

  # dicts
  _lowerCAmelCase							:	Tuple 					=       os.path.join(__a	,   f'dict.{src_lang}.txt'		)
  _lowerCAmelCase							:	Dict 					=       os.path.join(__a	,   f'dict.{tgt_lang}.txt'		)

  _lowerCAmelCase							:	Union[str, Any] 					=       Dictionary.load(__a		)
  _lowerCAmelCase							:	Any 					=       rewrite_dict_keys(src_dict.indices		)
  _lowerCAmelCase							:	Optional[int] 					=       len(__a		)
  _lowerCAmelCase							:	Tuple 					=       os.path.join(__a	,   """vocab-src.json"""		)
  print(f'Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'		)
  with open(__a	,   """w"""	,   encoding="""utf-8"""		) as f:
    f.write(json.dumps(__a	,   ensure_ascii=__a	,   indent=__a		)		)

  # detect whether this is a do_lower_case situation, which can be derived by checking whether we
  # have at least one uppercase letter in the source vocab
  _lowerCAmelCase							:	List[Any] 					=       True
  for k in src_vocab.keys():
    if not k.islower():
      _lowerCAmelCase							:	int 					=       False
      break

  _lowerCAmelCase							:	List[Any] 					=       Dictionary.load(__a		)
  _lowerCAmelCase							:	Dict 					=       rewrite_dict_keys(tgt_dict.indices		)
  _lowerCAmelCase							:	Union[str, Any] 					=       len(__a		)
  _lowerCAmelCase							:	Optional[Any] 					=       os.path.join(__a	,   """vocab-tgt.json"""		)
  print(f'Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'		)
  with open(__a	,   """w"""	,   encoding="""utf-8"""		) as f:
    f.write(json.dumps(__a	,   ensure_ascii=__a	,   indent=__a		)		)

  # merges_file (bpecodes)
  _lowerCAmelCase							:	str 					=       os.path.join(__a	,   VOCAB_FILES_NAMES["""merges_file"""]		)
  for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
    _lowerCAmelCase							:	List[str] 					=       os.path.join(__a	,   __a		)
    if os.path.exists(__a		):
      break
  with open(__a	,   encoding="""utf-8"""		) as fin:
    _lowerCAmelCase							:	Optional[Any] 					=       fin.read()
  _lowerCAmelCase							:	str 					=       re.sub(R""" \d+$"""	,   """"""	,   __a	,   0	,   re.M		)  # remove frequency number
  print(f'Generating {merges_file}'		)
  with open(__a	,   """w"""	,   encoding="""utf-8"""		) as fout:
    fout.write(__a		)

  # model config
  _lowerCAmelCase							:	List[str] 					=       os.path.join(__a	,   """config.json"""		)

  # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
  # may have to modify the tokenizer if a different type is used by a future model
  assert args["bpe"] == "fastbpe", f'need to extend tokenizer to support bpe={args["bpe"]}'
  assert args["tokenizer"] == "moses", f'need to extend tokenizer to support bpe={args["tokenizer"]}'

  _lowerCAmelCase							:	List[str] 					=       {
      """architectures""": ["""FSMTForConditionalGeneration"""],
      """model_type""": """fsmt""",
      """activation_dropout""": args["""activation_dropout"""],
      """activation_function""": """relu""",
      """attention_dropout""": args["""attention_dropout"""],
      """d_model""": args["""decoder_embed_dim"""],
      """dropout""": args["""dropout"""],
      """init_std""": 0.02,
      """max_position_embeddings""": args["""max_source_positions"""],
      """num_hidden_layers""": args["""encoder_layers"""],
      """src_vocab_size""": src_vocab_size,
      """tgt_vocab_size""": tgt_vocab_size,
      """langs""": [src_lang, tgt_lang],
      """encoder_attention_heads""": args["""encoder_attention_heads"""],
      """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
      """encoder_layerdrop""": args["""encoder_layerdrop"""],
      """encoder_layers""": args["""encoder_layers"""],
      """decoder_attention_heads""": args["""decoder_attention_heads"""],
      """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
      """decoder_layerdrop""": args["""decoder_layerdrop"""],
      """decoder_layers""": args["""decoder_layers"""],
      """bos_token_id""": 0,
      """pad_token_id""": 1,
      """eos_token_id""": 2,
      """is_encoder_decoder""": True,
      """scale_embedding""": not args["""no_scale_embedding"""],
      """tie_word_embeddings""": args["""share_all_embeddings"""],
  }

  # good hparam defaults to start with
  _lowerCAmelCase							:	Optional[int] 					=       5
  _lowerCAmelCase							:	Dict 					=       False
  if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
    _lowerCAmelCase							:	List[Any] 					=       best_score_hparams[model_dir]["""length_penalty"""]
  else:
    _lowerCAmelCase							:	Tuple 					=       1.0

  print(f'Generating {fsmt_model_config_file}'		)
  with open(__a	,   """w"""	,   encoding="""utf-8"""		) as f:
    f.write(json.dumps(__a	,   ensure_ascii=__a	,   indent=__a		)		)

  # tokenizer config
  _lowerCAmelCase							:	Union[str, Any] 					=       os.path.join(__a	,   __a		)

  _lowerCAmelCase							:	Tuple 					=       {
      """langs""": [src_lang, tgt_lang],
      """model_max_length""": 10_24,
      """do_lower_case""": do_lower_case,
  }

  print(f'Generating {fsmt_tokenizer_config_file}'		)
  with open(__a	,   """w"""	,   encoding="""utf-8"""		) as f:
    f.write(json.dumps(__a	,   ensure_ascii=__a	,   indent=__a		)		)

  # model
  _lowerCAmelCase							:	Union[str, Any] 					=       chkpt["""models"""][0]
  _lowerCAmelCase							:	Any 					=       model.state_dict()

  # rename keys to start with 'model.'
  _lowerCAmelCase							:	Optional[int] 					=       OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items()		)

  # remove unneeded keys
  _lowerCAmelCase							:	Optional[Any] 					=       [
      """model.model""",
      """model.encoder.version""",
      """model.decoder.version""",
      """model.encoder_embed_tokens.weight""",
      """model.decoder_embed_tokens.weight""",
      """model.encoder.embed_positions._float_tensor""",
      """model.decoder.embed_positions._float_tensor""",
  ]
  for k in ignore_keys:
    model_state_dict.pop(__a	,   __a		)

  _lowerCAmelCase							:	str 					=       FSMTConfig.from_pretrained(__a		)
  _lowerCAmelCase							:	Optional[Any] 					=       FSMTForConditionalGeneration(__a		)

  # check that it loads ok
  model_new.load_state_dict(__a	,   strict=__a		)

  # save
  _lowerCAmelCase							:	Optional[Any] 					=       os.path.join(__a	,   __a		)
  print(f'Generating {pytorch_weights_dump_path}'		)
  torch.save(__a	,   __a		)

  print("""Conversion is done!"""		)
  print("""\nLast step is to upload the files to s3"""		)
  print(f'cd {data_root}'		)
  print(f'transformers-cli upload {model_dir}'		)


if __name__ == "__main__":
     UpperCamelCase_        =							argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--fsmt_checkpoint_path""",
         default=None,
         type=str,
         required=True,
         help=(
             """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
             """ bpecodes, etc."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     UpperCamelCase_        =							parser.parse_args()
     convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




"""simple docstring"""




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

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

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


lowerCAmelCase__				=					logging.get_logger(__name__)

# General docstring
lowerCAmelCase__				=					'PoolFormerConfig'

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

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

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



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





class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_ = None     ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_    			=			drop_prob

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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







							def _lowercase       (self     ):

									"""simple docstring"""







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







class 			snake_case  (				nn.Module ):

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

									"""simple docstring"""







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

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







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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







class 			snake_case  (				nn.GroupNorm ):

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

									"""simple docstring"""







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







class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									return self.pool(SCREAMING_SNAKE_CASE_     ) - hidden_states







class 			snake_case  (				nn.Module ):

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

									"""simple docstring"""







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







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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

									return hidden_states







class 			snake_case  (				nn.Module ):

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

									"""simple docstring"""







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

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







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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

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

											SCREAMING_SNAKE_CASE_    			=			(output,) + outputs
											return outputs

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

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

											SCREAMING_SNAKE_CASE_    			=			(output,) + outputs
											return outputs







class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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

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

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

									SCREAMING_SNAKE_CASE_    			=			nn.ModuleList(SCREAMING_SNAKE_CASE_     )







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

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			() if output_hidden_states else None

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

											if output_hidden_states:
													SCREAMING_SNAKE_CASE_    			=			all_hidden_states + (hidden_states,)

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

									return BaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE_							,							hidden_states=SCREAMING_SNAKE_CASE_     )







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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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







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

									"""simple docstring"""







									if isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			value







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

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





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

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			config

									SCREAMING_SNAKE_CASE_    			=			PoolFormerEncoder(SCREAMING_SNAKE_CASE_     )

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

							def _lowercase       (self     ):

									"""simple docstring"""







									return self.embeddings.patch_embeddings







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

									"""simple docstring"""







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

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

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

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

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







class 			snake_case  (				nn.Module ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.dense(SCREAMING_SNAKE_CASE_     )
									return output







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

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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

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

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







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

									"""simple docstring"""







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

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

									SCREAMING_SNAKE_CASE_    			=			outputs[0]

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

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

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

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

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