



"""simple docstring"""




import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						int    )  ->       str:





     '''simple docstring'''

     _UpperCAmelCase      :     Union[str, Any]        =   SwinConfig()
     _UpperCAmelCase      :     Dict        =   swin_name.split("_"    )

     _UpperCAmelCase      :     Tuple        =   name_split[1]
     _UpperCAmelCase      :     Any        =   int(name_split[4]    )
     _UpperCAmelCase      :     Optional[int]        =   int(name_split[3][-1]    )

     if model_size == "tiny":
          _UpperCAmelCase      :     Optional[int]        =   96
          _UpperCAmelCase      :     Dict        =   (2, 2, 6, 2)
          _UpperCAmelCase      :     Optional[Any]        =   (3, 6, 12, 24)
     elif model_size == "small":
          _UpperCAmelCase      :     Any        =   96
          _UpperCAmelCase      :     List[str]        =   (2, 2, 18, 2)
          _UpperCAmelCase      :     Any        =   (3, 6, 12, 24)
     elif model_size == "base":
          _UpperCAmelCase      :     Tuple        =   128
          _UpperCAmelCase      :     List[Any]        =   (2, 2, 18, 2)
          _UpperCAmelCase      :     Tuple        =   (4, 8, 16, 32)
     else:
          _UpperCAmelCase      :     Any        =   192
          _UpperCAmelCase      :     Tuple        =   (2, 2, 18, 2)
          _UpperCAmelCase      :     List[Any]        =   (6, 12, 24, 48)

     if "in22k" in swin_name:
          _UpperCAmelCase      :     str        =   21_841
     else:
          _UpperCAmelCase      :     List[str]        =   1_000
          _UpperCAmelCase      :     List[str]        =   "huggingface/label-files"
          _UpperCAmelCase      :     Union[str, Any]        =   "imagenet-1k-id2label.json"
          _UpperCAmelCase      :     str        =   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  repo_type="dataset"    )						,  "r"    )    )
          _UpperCAmelCase      :     List[Any]        =   {int(SCREAMING_SNAKE_CASE__    ): v for k, v in idalabel.items()}
          _UpperCAmelCase      :     Tuple        =   idalabel
          _UpperCAmelCase      :     int        =   {v: k for k, v in idalabel.items()}

     _UpperCAmelCase      :     str        =   img_size
     _UpperCAmelCase      :     str        =   num_classes
     _UpperCAmelCase      :     Optional[int]        =   embed_dim
     _UpperCAmelCase      :     Union[str, Any]        =   depths
     _UpperCAmelCase      :     int        =   num_heads
     _UpperCAmelCase      :     Optional[int]        =   window_size

     return config

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						List[str]    )  ->       Union[str, Any]:





     '''simple docstring'''

     if "patch_embed.proj" in name:
          _UpperCAmelCase      :     Tuple        =   name.replace("patch_embed.proj"						,  "embeddings.patch_embeddings.projection"    )
     if "patch_embed.norm" in name:
          _UpperCAmelCase      :     Optional[Any]        =   name.replace("patch_embed.norm"						,  "embeddings.norm"    )
     if "layers" in name:
          _UpperCAmelCase      :     List[Any]        =   "encoder." + name
     if "attn.proj" in name:
          _UpperCAmelCase      :     Union[str, Any]        =   name.replace("attn.proj"						,  "attention.output.dense"    )
     if "attn" in name:
          _UpperCAmelCase      :     Dict        =   name.replace("attn"						,  "attention.self"    )
     if "norm1" in name:
          _UpperCAmelCase      :     Dict        =   name.replace("norm1"						,  "layernorm_before"    )
     if "norm2" in name:
          _UpperCAmelCase      :     Dict        =   name.replace("norm2"						,  "layernorm_after"    )
     if "mlp.fc1" in name:
          _UpperCAmelCase      :     List[Any]        =   name.replace("mlp.fc1"						,  "intermediate.dense"    )
     if "mlp.fc2" in name:
          _UpperCAmelCase      :     str        =   name.replace("mlp.fc2"						,  "output.dense"    )

     if name == "norm.weight":
          _UpperCAmelCase      :     str        =   "layernorm.weight"
     if name == "norm.bias":
          _UpperCAmelCase      :     Optional[int]        =   "layernorm.bias"

     if "head" in name:
          _UpperCAmelCase      :     Tuple        =   name.replace("head"						,  "classifier"    )
     else:
          _UpperCAmelCase      :     List[Any]        =   "swin." + name

     return name

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						List[Any]						,  SCREAMING_SNAKE_CASE__     :						Any    )  ->       List[str]:





     '''simple docstring'''

     for key in orig_state_dict.copy().keys():
          _UpperCAmelCase      :     Optional[int]        =   orig_state_dict.pop(SCREAMING_SNAKE_CASE__    )

          if "mask" in key:
               continue
          elif "qkv" in key:
               _UpperCAmelCase      :     Optional[Any]        =   key.split("."    )
               _UpperCAmelCase      :     Dict        =   int(key_split[1]    )
               _UpperCAmelCase      :     List[Any]        =   int(key_split[3]    )
               _UpperCAmelCase      :     Union[str, Any]        =   model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    _UpperCAmelCase      :     List[str]        =   val[:dim, :]
                    _UpperCAmelCase      :     Tuple        =   val[
                        dim : dim * 2, :
                    ]
                    _UpperCAmelCase      :     int        =   val[-dim:, :]
               else:
                    _UpperCAmelCase      :     int        =   val[
                        :dim
                    ]
                    _UpperCAmelCase      :     str        =   val[
                        dim : dim * 2
                    ]
                    _UpperCAmelCase      :     Optional[int]        =   val[
                        -dim:
                    ]
          else:
               _UpperCAmelCase      :     List[str]        =   val

     return orig_state_dict

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						Any    )  ->       List[Any]:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[int]        =   timm.create_model(SCREAMING_SNAKE_CASE__						,  pretrained=SCREAMING_SNAKE_CASE__    )
     timm_model.eval()

     _UpperCAmelCase      :     List[str]        =   get_swin_config(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     int        =   SwinForImageClassification(SCREAMING_SNAKE_CASE__    )
     model.eval()

     _UpperCAmelCase      :     Optional[Any]        =   convert_state_dict(timm_model.state_dict()						,  SCREAMING_SNAKE_CASE__    )
     model.load_state_dict(SCREAMING_SNAKE_CASE__    )

     _UpperCAmelCase      :     List[Any]        =   "http://images.cocodataset.org/val2017/000000039769.jpg"

     _UpperCAmelCase      :     Optional[int]        =   AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"						,  "-"    )    )    )
     _UpperCAmelCase      :     Any        =   Image.open(requests.get(SCREAMING_SNAKE_CASE__						,  stream=SCREAMING_SNAKE_CASE__    ).raw    )
     _UpperCAmelCase      :     int        =   image_processor(images=SCREAMING_SNAKE_CASE__						,  return_tensors="pt"    )

     _UpperCAmelCase      :     Tuple        =   timm_model(inputs["pixel_values"]    )
     _UpperCAmelCase      :     int        =   model(**SCREAMING_SNAKE_CASE__    ).logits

     assert torch.allclose(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  atol=1E-3    )

     print(f'Saving model {swin_name} to {pytorch_dump_folder_path}'    )
     model.save_pretrained(SCREAMING_SNAKE_CASE__    )

     print(f'Saving image processor to {pytorch_dump_folder_path}'    )
     image_processor.save_pretrained(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
 _lowerCAmelCase    : List[str]							=							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--swin_name",
     default="swin_tiny_patch4_window7_224",
     type=str,
     help="Name of the Swin timm model you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )

 _lowerCAmelCase    : Dict							=							parser.parse_args()
 convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





"""simple docstring"""




import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_lowerCAmelCase    : Any							=							logging.getLogger(__name__)






@dataclass
class      UpperCAmelCase_  :
      __SCREAMING_SNAKE_CASE						:       str
      __SCREAMING_SNAKE_CASE						:       List[str]
      __SCREAMING_SNAKE_CASE						:       Optional[List[str]]







@dataclass
class      UpperCAmelCase_  :
      __SCREAMING_SNAKE_CASE						:       List[int]
      __SCREAMING_SNAKE_CASE						:       List[int]
      __SCREAMING_SNAKE_CASE						:       Optional[List[int]]								=			None
      __SCREAMING_SNAKE_CASE						:       Optional[List[int]]								=			None







class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Union[str, Any]								=			'train'
      __SCREAMING_SNAKE_CASE						:       Tuple								=			'dev'
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			'test'







class      UpperCAmelCase_  :



      @staticmethod
      def 						snake_case_ (					A					:     Union[str, Any]					,     A					:     Union[Split, str]						):
           raise NotImplementedError



      @staticmethod
      def 						snake_case_ (					A					:     str						):
           raise NotImplementedError



      @staticmethod
      def 						snake_case_ (					A					:     List[InputExample]					,     A					:     List[str]					,     A					:     int					,     A					:     PreTrainedTokenizer					,     A					:     Optional[int]=False					,     A					:     List[str]="[CLS]"					,     A					:     List[Any]=1					,     A					:     str="[SEP]"					,     A					:     int=False					,     A					:     int=False					,     A					:     Any=0					,     A					:     List[str]=0					,     A					:     Dict=-1_0_0					,     A					:     str=0					,     A					:     Optional[Any]=True					,     ):
           _UpperCAmelCase      :     Dict        =   {label: i for i, label in enumerate(A						)}

           _UpperCAmelCase      :     str        =   []
           for ex_index, example in enumerate(A						):
                if ex_index % 1_0_0_0_0 == 0:
                     logger.info("Writing example %d of %d"					,     A					,     len(A						)						)

                _UpperCAmelCase      :     int        =   []
                _UpperCAmelCase      :     List[str]        =   []
                for word, label in zip(example.words					,     example.labels						):
                     _UpperCAmelCase      :     str        =   tokenizer.tokenize(A						)

                     # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                     if len(A						) > 0:
                          tokens.extend(A						)
                          # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                          label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(A						) - 1)						)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                _UpperCAmelCase      :     List[str]        =   tokenizer.num_special_tokens_to_add()
                if len(A						) > max_seq_length - special_tokens_count:
                     _UpperCAmelCase      :     List[Any]        =   tokens[: (max_seq_length - special_tokens_count)]
                     _UpperCAmelCase      :     List[Any]        =   label_ids[: (max_seq_length - special_tokens_count)]

                # The convention in BERT is:
                # (a) For sequence pairs:
                #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
                #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
                # (b) For single sequences:
                #  tokens:   [CLS] the dog is hairy . [SEP]
                #  type_ids:   0   0   0   0  0     0   0
                #
                # Where "type_ids" are used to indicate whether this is the first
                # sequence or the second sequence. The embedding vectors for `type=0` and
                # `type=1` were learned during pre-training and are added to the wordpiece
                # embedding vector (and position vector). This is not *strictly* necessary
                # since the [SEP] token unambiguously separates the sequences, but it makes
                # it easier for the model to learn the concept of sequences.
                #
                # For classification tasks, the first vector (corresponding to [CLS]) is
                # used as the "sentence vector". Note that this only makes sense because
                # the entire model is fine-tuned.
                tokens += [sep_token]
                label_ids += [pad_token_label_id]
                if sep_token_extra:
                     # roberta uses an extra separator b/w pairs of sentences
                     tokens += [sep_token]
                     label_ids += [pad_token_label_id]
                _UpperCAmelCase      :     Dict        =   [sequence_a_segment_id] * len(A						)

                if cls_token_at_end:
                     tokens += [cls_token]
                     label_ids += [pad_token_label_id]
                     segment_ids += [cls_token_segment_id]
                else:
                     _UpperCAmelCase      :     str        =   [cls_token] + tokens
                     _UpperCAmelCase      :     Dict        =   [pad_token_label_id] + label_ids
                     _UpperCAmelCase      :     Any        =   [cls_token_segment_id] + segment_ids

                _UpperCAmelCase      :     int        =   tokenizer.convert_tokens_to_ids(A						)

                # The mask has 1 for real tokens and 0 for padding tokens. Only real
                # tokens are attended to.
                _UpperCAmelCase      :     List[Any]        =   [1 if mask_padding_with_zero else 0] * len(A						)

                # Zero-pad up to the sequence length.
                _UpperCAmelCase      :     List[str]        =   max_seq_length - len(A						)
                if pad_on_left:
                     _UpperCAmelCase      :     str        =   ([pad_token] * padding_length) + input_ids
                     _UpperCAmelCase      :     str        =   ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                     _UpperCAmelCase      :     Any        =   ([pad_token_segment_id] * padding_length) + segment_ids
                     _UpperCAmelCase      :     Dict        =   ([pad_token_label_id] * padding_length) + label_ids
                else:
                     input_ids += [pad_token] * padding_length
                     input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                     segment_ids += [pad_token_segment_id] * padding_length
                     label_ids += [pad_token_label_id] * padding_length

                assert len(A						) == max_seq_length
                assert len(A						) == max_seq_length
                assert len(A						) == max_seq_length
                assert len(A						) == max_seq_length

                if ex_index < 5:
                     logger.info("*** Example ***"						)
                     logger.info("guid: %s"					,     example.guid						)
                     logger.info("tokens: %s"					,     " ".join([str(A						) for x in tokens]						)						)
                     logger.info("input_ids: %s"					,     " ".join([str(A						) for x in input_ids]						)						)
                     logger.info("input_mask: %s"					,     " ".join([str(A						) for x in input_mask]						)						)
                     logger.info("segment_ids: %s"					,     " ".join([str(A						) for x in segment_ids]						)						)
                     logger.info("label_ids: %s"					,     " ".join([str(A						) for x in label_ids]						)						)

                if "token_type_ids" not in tokenizer.model_input_names:
                     _UpperCAmelCase      :     Dict        =   None

                features.append(
                    InputFeatures(
                        input_ids=A					,     attention_mask=A					,     token_type_ids=A					,     label_ids=A						)						)
           return features


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset






 class      UpperCAmelCase_  (    _UpperCamelCase						):
       __SCREAMING_SNAKE_CASE						:       List[InputFeatures]
       __SCREAMING_SNAKE_CASE						:       int								=			nn.CrossEntropyLoss().ignore_index



       def __init__(					self					:     Dict					,     A					:     TokenClassificationTask					,     A					:     str					,     A					:     PreTrainedTokenizer					,     A					:     List[str]					,     A					:     str					,     A					:     Optional[int] = None					,     A					:     List[str]=False					,     A					:     Split = Split.train					,     ):
            # Load data features from cache or dataset file
            _UpperCAmelCase      :     int        =   os.path.join(
                A					,     "cached_{}_{}_{}".format(mode.value					,     tokenizer.__class__.__name__					,     str(A						)						)					,     )

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            _UpperCAmelCase      :     List[str]        =   cached_features_file + ".lock"
            with FileLock(A						):
                 if os.path.exists(A						) and not overwrite_cache:
                      logger.info(f'Loading features from cached file {cached_features_file}'						)
                      _UpperCAmelCase      :     Tuple        =   torch.load(A						)
                 else:
                      logger.info(f'Creating features from dataset file at {data_dir}'						)
                      _UpperCAmelCase      :     List[str]        =   token_classification_task.read_examples_from_file(A					,     A						)
                      # TODO clean up all this to leverage built-in features of tokenizers
                      _UpperCAmelCase      :     List[Any]        =   token_classification_task.convert_examples_to_features(
                          A					,     A					,     A					,     A					,     cls_token_at_end=bool(model_type in ["xlnet"]						)					,     cls_token=tokenizer.cls_token					,     cls_token_segment_id=2 if model_type in ["xlnet"] else 0					,     sep_token=tokenizer.sep_token					,     sep_token_extra=A					,     pad_on_left=bool(tokenizer.padding_side == "left"						)					,     pad_token=tokenizer.pad_token_id					,     pad_token_segment_id=tokenizer.pad_token_type_id					,     pad_token_label_id=self.pad_token_label_id					,     )
                      logger.info(f'Saving features into cached file {cached_features_file}'						)
                      torch.save(self.features					,     A						)



       def __len__(					self					:     Dict						):
            return len(self.features						)



       def __getitem__(					self					:     List[str]					,     A					:     Optional[Any]						):
            return self.features[i]


if is_tf_available():
 import tensorflow as tf






 class      UpperCAmelCase_  :
       __SCREAMING_SNAKE_CASE						:       List[InputFeatures]
       __SCREAMING_SNAKE_CASE						:       int								=			-1_0_0



       def __init__(					self					:     Tuple					,     A					:     TokenClassificationTask					,     A					:     str					,     A					:     PreTrainedTokenizer					,     A					:     List[str]					,     A					:     str					,     A					:     Optional[int] = None					,     A					:     Optional[Any]=False					,     A					:     Split = Split.train					,     ):
            _UpperCAmelCase      :     Union[str, Any]        =   token_classification_task.read_examples_from_file(A					,     A						)
            # TODO clean up all this to leverage built-in features of tokenizers
            _UpperCAmelCase      :     List[str]        =   token_classification_task.convert_examples_to_features(
                A					,     A					,     A					,     A					,     cls_token_at_end=bool(model_type in ["xlnet"]						)					,     cls_token=tokenizer.cls_token					,     cls_token_segment_id=2 if model_type in ["xlnet"] else 0					,     sep_token=tokenizer.sep_token					,     sep_token_extra=A					,     pad_on_left=bool(tokenizer.padding_side == "left"						)					,     pad_token=tokenizer.pad_token_id					,     pad_token_segment_id=tokenizer.pad_token_type_id					,     pad_token_label_id=self.pad_token_label_id					,     )

            def gen():
                 for ex in self.features:
                      if ex.token_type_ids is None:
                           yield (
                               {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                               ex.label_ids,
                           )
                      else:
                           yield (
                               {
                                   "input_ids": ex.input_ids,
                                   "attention_mask": ex.attention_mask,
                                   "token_type_ids": ex.token_type_ids,
                               },
                               ex.label_ids,
                           )

            if "token_type_ids" not in tokenizer.model_input_names:
                 _UpperCAmelCase      :     List[str]        =   tf.data.Dataset.from_generator(
                     A					,     ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)					,     (
                         {"input_ids": tf.TensorShape([None]						), "attention_mask": tf.TensorShape([None]						)},
                         tf.TensorShape([None]						),
                     )					,     )
            else:
                 _UpperCAmelCase      :     List[Any]        =   tf.data.Dataset.from_generator(
                     A					,     ({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa)					,     (
                         {
                             "input_ids": tf.TensorShape([None]						),
                             "attention_mask": tf.TensorShape([None]						),
                             "token_type_ids": tf.TensorShape([None]						),
                         },
                         tf.TensorShape([None]						),
                     )					,     )



       def 						snake_case_ (					self					:     str						):
            _UpperCAmelCase      :     Dict        =   self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features						)						)						)

            return self.dataset



       def __len__(					self					:     List[Any]						):
            return len(self.features						)



       def __getitem__(					self					:     int					,     A					:     int						):
            return self.features[i]
