



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


__magic_name__            =  logging.getLogger(__name__)




@dataclass
class 					_SCREAMING_SNAKE_CASE  :
      _A						:		str
      _A						:		List[str]
      _A						:		Optional[List[str]]





@dataclass
class 					_SCREAMING_SNAKE_CASE  :
      _A						:		List[int]
      _A						:		List[int]
      _A						:		Optional[List[int]]    	=     None
      _A						:		Optional[List[int]]    	=     None





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		List[str]    	=     'train'
      _A						:		Optional[Any]    	=     'dev'
      _A						:		Dict    	=     'test'





class 					_SCREAMING_SNAKE_CASE  :
      @staticmethod
      def A_			(			lowerCamelCase		,	lowerCamelCase ):
          raise NotImplementedError
      @staticmethod
      def A_			(			lowerCamelCase ):
          raise NotImplementedError



      @staticmethod
      def A_			(			lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=False		,	lowerCamelCase="[CLS]"		,	lowerCamelCase=1		,	lowerCamelCase="[SEP]"		,	lowerCamelCase=False		,	lowerCamelCase=False		,	lowerCamelCase=0		,	lowerCamelCase=0		,	lowerCamelCase=-1_00		,	lowerCamelCase=0		,	lowerCamelCase=True		,	):
          snake_case__						=				{label: i for i, label in enumerate(lowerCamelCase )}

          snake_case__						=				[]
          for ex_index, example in enumerate(lowerCamelCase ):
              if ex_index % 1_00_00 == 0:
                  logger.info("Writing example %d of %d"		,	lowerCamelCase		,	len(lowerCamelCase ) )

              snake_case__						=				[]
              snake_case__						=				[]
              for word, label in zip(example.words		,	example.labels ):
                  snake_case__						=				tokenizer.tokenize(lowerCamelCase )

                  # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                  if len(lowerCamelCase ) > 0:
                      tokens.extend(lowerCamelCase )
                      # 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(lowerCamelCase ) - 1) )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
              snake_case__						=				tokenizer.num_special_tokens_to_add()
              if len(lowerCamelCase ) > max_seq_length - special_tokens_count:
                  snake_case__						=				tokens[: (max_seq_length - special_tokens_count)]
                  snake_case__						=				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]
              snake_case__						=				[sequence_a_segment_id] * len(lowerCamelCase )

              if cls_token_at_end:
                  tokens += [cls_token]
                  label_ids += [pad_token_label_id]
                  segment_ids += [cls_token_segment_id]
              else:
                  snake_case__						=				[cls_token] + tokens
                  snake_case__						=				[pad_token_label_id] + label_ids
                  snake_case__						=				[cls_token_segment_id] + segment_ids

              snake_case__						=				tokenizer.convert_tokens_to_ids(lowerCamelCase )

              # The mask has 1 for real tokens and 0 for padding tokens. Only real
              # tokens are attended to.
              snake_case__						=				[1 if mask_padding_with_zero else 0] * len(lowerCamelCase )

              # Zero-pad up to the sequence length.
              snake_case__						=				max_seq_length - len(lowerCamelCase )
              if pad_on_left:
                  snake_case__						=				([pad_token] * padding_length) + input_ids
                  snake_case__						=				([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                  snake_case__						=				([pad_token_segment_id] * padding_length) + segment_ids
                  snake_case__						=				([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(lowerCamelCase ) == max_seq_length
              assert len(lowerCamelCase ) == max_seq_length
              assert len(lowerCamelCase ) == max_seq_length
              assert len(lowerCamelCase ) == max_seq_length

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

              if "token_type_ids" not in tokenizer.model_input_names:
                  snake_case__						=				None

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


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




     class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
           _A						:		List[InputFeatures]
           _A						:		int    	=     nn.CrossEntropyLoss().ignore_index
           def __init__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase=False		,	lowerCamelCase = Split.train		,	):
               # Load data features from cache or dataset file
               snake_case__						=				os.path.join(
                   lowerCamelCase		,	"cached_{}_{}_{}".format(mode.value		,	tokenizer.__class__.__name__		,	str(lowerCamelCase ) )		,	)

               # Make sure only the first process in distributed training processes the dataset,
               # and the others will use the cache.
               snake_case__						=				cached_features_file + ".lock"
               with FileLock(lowerCamelCase ):
                   if os.path.exists(lowerCamelCase ) and not overwrite_cache:
                       logger.info(F"""Loading features from cached file {cached_features_file}""" )
                       snake_case__						=				torch.load(lowerCamelCase )
                   else:
                       logger.info(F"""Creating features from dataset file at {data_dir}""" )
                       snake_case__						=				token_classification_task.read_examples_from_file(lowerCamelCase		,	lowerCamelCase )
                       # TODO clean up all this to leverage built-in features of tokenizers
                       snake_case__						=				token_classification_task.convert_examples_to_features(
                           lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	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=lowerCamelCase		,	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		,	lowerCamelCase )
           def __len__(			self ):
               return len(self.features )



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


if is_tf_available():
     import tensorflow as tf




     class 					_SCREAMING_SNAKE_CASE  :
           _A						:		List[InputFeatures]
           _A						:		int    	=     -1_00
           def __init__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase=False		,	lowerCamelCase = Split.train		,	):
               snake_case__						=				token_classification_task.read_examples_from_file(lowerCamelCase		,	lowerCamelCase )
               # TODO clean up all this to leverage built-in features of tokenizers
               snake_case__						=				token_classification_task.convert_examples_to_features(
                   lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	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=lowerCamelCase		,	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:
                   snake_case__						=				tf.data.Dataset.from_generator(
                       lowerCamelCase		,	({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)		,	(
                           {"input_ids": tf.TensorShape([None] ), "attention_mask": tf.TensorShape([None] )},
                           tf.TensorShape([None] ),
                       )		,	)
               else:
                   snake_case__						=				tf.data.Dataset.from_generator(
                       lowerCamelCase		,	({"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 A_			(			self ):
               snake_case__						=				self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )

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



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





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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				SwinConfig()
    snake_case__						=				swin_name.split("_"							)

    snake_case__						=				name_split[1]
    snake_case__						=				int(name_split[4]							)
    snake_case__						=				int(name_split[3][-1]							)

    if model_size == "tiny":
        snake_case__						=				96
        snake_case__						=				(2, 2, 6, 2)
        snake_case__						=				(3, 6, 12, 24)
    elif model_size == "small":
        snake_case__						=				96
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(3, 6, 12, 24)
    elif model_size == "base":
        snake_case__						=				128
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(4, 8, 16, 32)
    else:
        snake_case__						=				192
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(6, 12, 24, 48)

    if "in22k" in swin_name:
        snake_case__						=				21_841
    else:
        snake_case__						=				1_000
        snake_case__						=				"huggingface/label-files"
        snake_case__						=				"imagenet-1k-id2label.json"
        snake_case__						=				json.load(open(hf_hub_download(__lowerCAmelCase    ,		__lowerCAmelCase    ,		repo_type="dataset"							)    ,		"r"							)							)
        snake_case__						=				{int(__lowerCAmelCase							): v for k, v in idalabel.items()}
        snake_case__						=				idalabel
        snake_case__						=				{v: k for k, v in idalabel.items()}

    snake_case__						=				img_size
    snake_case__						=				num_classes
    snake_case__						=				embed_dim
    snake_case__						=				depths
    snake_case__						=				num_heads
    snake_case__						=				window_size

    return config





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    if "patch_embed.proj" in name:
        snake_case__						=				name.replace("patch_embed.proj"    ,		"embeddings.patch_embeddings.projection"							)
    if "patch_embed.norm" in name:
        snake_case__						=				name.replace("patch_embed.norm"    ,		"embeddings.norm"							)
    if "layers" in name:
        snake_case__						=				"encoder." + name
    if "attn.proj" in name:
        snake_case__						=				name.replace("attn.proj"    ,		"attention.output.dense"							)
    if "attn" in name:
        snake_case__						=				name.replace("attn"    ,		"attention.self"							)
    if "norm1" in name:
        snake_case__						=				name.replace("norm1"    ,		"layernorm_before"							)
    if "norm2" in name:
        snake_case__						=				name.replace("norm2"    ,		"layernorm_after"							)
    if "mlp.fc1" in name:
        snake_case__						=				name.replace("mlp.fc1"    ,		"intermediate.dense"							)
    if "mlp.fc2" in name:
        snake_case__						=				name.replace("mlp.fc2"    ,		"output.dense"							)

    if name == "norm.weight":
        snake_case__						=				"layernorm.weight"
    if name == "norm.bias":
        snake_case__						=				"layernorm.bias"

    if "head" in name:
        snake_case__						=				name.replace("head"    ,		"classifier"							)
    else:
        snake_case__						=				"swin." + name

    return name





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    for key in orig_state_dict.copy().keys():
        snake_case__						=				orig_state_dict.pop(__lowerCAmelCase							)

        if "mask" in key:
            continue
        elif "qkv" in key:
            snake_case__						=				key.split("."							)
            snake_case__						=				int(key_split[1]							)
            snake_case__						=				int(key_split[3]							)
            snake_case__						=				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                snake_case__						=				val[:dim, :]
                snake_case__						=				val[
                    dim : dim * 2, :
                ]
                snake_case__						=				val[-dim:, :]
            else:
                snake_case__						=				val[
                    :dim
                ]
                snake_case__						=				val[
                    dim : dim * 2
                ]
                snake_case__						=				val[
                    -dim:
                ]
        else:
            snake_case__						=				val

    return orig_state_dict





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				timm.create_model(__lowerCAmelCase    ,		pretrained=__lowerCAmelCase							)
    timm_model.eval()

    snake_case__						=				get_swin_config(__lowerCAmelCase							)
    snake_case__						=				SwinForImageClassification(__lowerCAmelCase							)
    model.eval()

    snake_case__						=				convert_state_dict(timm_model.state_dict()    ,		__lowerCAmelCase							)
    model.load_state_dict(__lowerCAmelCase							)

    snake_case__						=				"http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case__						=				AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"    ,		"-"							)							)							)
    snake_case__						=				Image.open(requests.get(__lowerCAmelCase    ,		stream=__lowerCAmelCase							).raw							)
    snake_case__						=				image_processor(images=__lowerCAmelCase    ,		return_tensors="pt"							)

    snake_case__						=				timm_model(inputs["pixel_values"]							)
    snake_case__						=				model(**__lowerCAmelCase							).logits

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

    print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""							)
    model.save_pretrained(__lowerCAmelCase							)

    print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
    image_processor.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
     __magic_name__            =  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.'''
     )

     __magic_name__            =  parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



