

import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_									=   logging.get_logger(__name__)
def      a      (			A__	:   Optional[Any]      ,   A__	:   int=False     )    ->      str:





      """simple docstring"""



      _lowercase	       =[]
      for i in range(config.num_hidden_layers     ):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''deit.encoder.layer.{i}.layernorm_before.weight''')     )
            rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')     )
            rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')     )
            rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')     )
            rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')     )
            rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')     )
            rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')     )
            rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')     )
            rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')     )
            rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')     )

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ('cls_token', 'deit.embeddings.cls_token'),
              ('dist_token', 'deit.embeddings.distillation_token'),
              ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
              ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
              ('pos_embed', 'deit.embeddings.position_embeddings'),
          ]     )

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ('norm.weight', 'layernorm.weight'),
                    ('norm.bias', 'layernorm.bias'),
                    ('pre_logits.fc.weight', 'pooler.dense.weight'),
                    ('pre_logits.fc.bias', 'pooler.dense.bias'),
                ]     )

            # if just the base model, we should remove "deit" from all keys that start with "deit"
            _lowercase	       =[(pair[0], pair[1][4:]) if pair[1].startswith('deit'     ) else pair for pair in rename_keys]
      else:
            # layernorm + classification heads
            rename_keys.extend(
                [
                    ('norm.weight', 'deit.layernorm.weight'),
                    ('norm.bias', 'deit.layernorm.bias'),
                    ('head.weight', 'cls_classifier.weight'),
                    ('head.bias', 'cls_classifier.bias'),
                    ('head_dist.weight', 'distillation_classifier.weight'),
                    ('head_dist.bias', 'distillation_classifier.bias'),
                ]     )

      return rename_keys
def      a      (			A__	:   Optional[Any]      ,   A__	:   Any      ,   A__	:   Union[str, Any]=False     )    ->      Union[str, Any]:





      """simple docstring"""



      for i in range(config.num_hidden_layers     ):
            if base_model:
                  _lowercase	       =""""""
            else:
                  _lowercase	       ="""deit."""
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            _lowercase	       =state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''     )
            _lowercase	       =state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''     )
            # next, add query, keys and values (in that order) to the state dict
            _lowercase	       =in_proj_weight[
                : config.hidden_size, :
            ]
            _lowercase	       =in_proj_bias[: config.hidden_size]
            _lowercase	       =in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _lowercase	       =in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _lowercase	       =in_proj_weight[
                -config.hidden_size :, :
            ]
            _lowercase	       =in_proj_bias[-config.hidden_size :]
def      a      (			A__	:   Optional[int]      ,   A__	:   Any      ,   A__	:   Any     )    ->      int:





      """simple docstring"""



      _lowercase	       =dct.pop(_lowerCAmelCase     )
      _lowercase	       =val
def      a      (			)    ->      Tuple:





      """simple docstring"""



      _lowercase	       ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _lowercase	       =Image.open(requests.get(_lowerCAmelCase      ,   stream=_lowerCAmelCase     ).raw     )
      return im
@torch.no_grad()
def      a      (			A__	:   Any      ,   A__	:   Optional[int]     )    ->      str:





      """simple docstring"""



      _lowercase	       =DeiTConfig()
      # all deit models have fine-tuned heads
      _lowercase	       =False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      _lowercase	       =1000
      _lowercase	       ="""huggingface/label-files"""
      _lowercase	       ="""imagenet-1k-id2label.json"""
      _lowercase	       =json.load(open(hf_hub_download(_lowerCAmelCase      ,   _lowerCAmelCase      ,   repo_type='dataset'     )      ,   'r'     )     )
      _lowercase	       ={int(_lowerCAmelCase     ): v for k, v in idalabel.items()}
      _lowercase	       =idalabel
      _lowercase	       ={v: k for k, v in idalabel.items()}
      _lowercase	       =int(deit_name[-6:-4]     )
      _lowercase	       =int(deit_name[-3:]     )
      # size of the architecture
      if deit_name[9:].startswith('tiny'     ):
            _lowercase	       =192
            _lowercase	       =768
            _lowercase	       =12
            _lowercase	       =3
      elif deit_name[9:].startswith('small'     ):
            _lowercase	       =384
            _lowercase	       =1536
            _lowercase	       =12
            _lowercase	       =6
      if deit_name[9:].startswith('base'     ):
            pass
      elif deit_name[4:].startswith('large'     ):
            _lowercase	       =1024
            _lowercase	       =4096
            _lowercase	       =24
            _lowercase	       =16

      # load original model from timm
      _lowercase	       =timm.create_model(_lowerCAmelCase      ,   pretrained=_lowerCAmelCase     )
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      _lowercase	       =timm_model.state_dict()
      _lowercase	       =create_rename_keys(_lowerCAmelCase      ,   _lowerCAmelCase     )
      for src, dest in rename_keys:
            rename_key(_lowerCAmelCase      ,   _lowerCAmelCase      ,   _lowerCAmelCase     )
      read_in_q_k_v(_lowerCAmelCase      ,   _lowerCAmelCase      ,   _lowerCAmelCase     )

      # load HuggingFace model
      _lowercase	       =DeiTForImageClassificationWithTeacher(_lowerCAmelCase     ).eval()
      model.load_state_dict(_lowerCAmelCase     )

      # Check outputs on an image, prepared by DeiTImageProcessor
      _lowercase	       =int(
          (256 / 224) * config.image_size     )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      _lowercase	       =DeiTImageProcessor(size=_lowerCAmelCase      ,   crop_size=config.image_size     )
      _lowercase	       =image_processor(images=prepare_img()      ,   return_tensors='pt'     )
      _lowercase	       =encoding["""pixel_values"""]
      _lowercase	       =model(_lowerCAmelCase     )

      _lowercase	       =timm_model(_lowerCAmelCase     )
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(_lowerCAmelCase      ,   outputs.logits      ,   atol=1e-3     )

      Path(_lowerCAmelCase     ).mkdir(exist_ok=_lowerCAmelCase     )
      print(F'''Saving model {deit_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__":
  lowercase_									=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--deit_name',
      default='vit_deit_base_distilled_patch16_224',
      type=str,
      help='Name of the DeiT 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.'
  )

  lowercase_									=   parser.parse_args()
  convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)







'''simple docstring'''





import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class     UpperCAmelCase_       (       _a		,		unittest.TestCase	):
    """simple docstring"""







    lowercase					=		GPTSanJapaneseTokenizer
    lowercase					=		False
    lowercase					=		{"do_clean_text": False, "add_prefix_space": False}




    def lowerCamelCase       (    self					:     str						):
          super().setUp()

          # fmt: off
          snake_case__  :    Optional[Any]    						=      ["""こん""", """こんに""", """にちは""", """ばんは""", """世界,㔺界""", """、""", """。""", """<BR>""", """<SP>""", """<TAB>""", """<URL>""", """<EMAIL>""", """<TEL>""", """<DATE>""", """<PRICE>""", """<BLOCK>""", """<KIGOU>""", """<U2000U2BFF>""", """<|emoji1|>""", """<unk>""", """<|bagoftoken|>""", """<|endoftext|>"""]
          # fmt: on
          snake_case__  :    int    						=      {"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
          snake_case__  :    List[Any]    						=      {"""unk_token""": """<unk>"""}

          snake_case__  :    Optional[Any]    						=      os.path.join(self.tmpdirname ,     VOCAB_FILES_NAMES["""vocab_file"""]						)
          snake_case__  :    Dict    						=      os.path.join(self.tmpdirname ,     VOCAB_FILES_NAMES["""emoji_file"""]						)
          with open(self.vocab_file ,     """w""" ,     encoding="""utf-8"""						) as vocab_writer:
                vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)
          with open(self.emoji_file ,     """w"""						) as emoji_writer:
                emoji_writer.write(json.dumps(snake_case_						)						)




    def lowerCamelCase       (    self					:     Any ,     **snake_case_					:     Union[str, Any]						):
          kwargs.update(self.special_tokens_map						)
          return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname ,     **snake_case_						)




    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     str						):
          snake_case__  :    Union[str, Any]    						=      """こんにちは、世界。 \nこんばんは、㔺界。😀"""
          snake_case__  :    List[str]    						=      """こんにちは、世界。 \nこんばんは、世界。😀"""
          return input_text, output_text




    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     Dict						):
          snake_case__						,				snake_case__  :    int    						=      self.get_input_output_texts(snake_case_						)
          snake_case__  :    int    						=      tokenizer.encode(snake_case_ ,     add_special_tokens=snake_case_						)
          snake_case__  :    List[str]    						=      tokenizer.decode(snake_case_ ,     clean_up_tokenization_spaces=snake_case_						)
          return text, ids




    def lowerCamelCase       (    self					:     Optional[Any]						):
          pass  # TODO add if relevant




    def lowerCamelCase       (    self					:     Union[str, Any]						):
          pass  # TODO add if relevant




    def lowerCamelCase       (    self					:     List[str]						):
          pass  # TODO add if relevant




    def lowerCamelCase       (    self					:     Dict						):
          snake_case__  :    Optional[Any]    						=      self.get_tokenizer()

          # Testing tokenization
          snake_case__  :    int    						=      """こんにちは、世界。　こんばんは、㔺界。"""
          snake_case__  :    Optional[int]    						=      ["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
          snake_case__  :    Dict    						=      tokenizer.tokenize(snake_case_						)
          self.assertListEqual(snake_case_ ,     snake_case_						)

          # Testing conversion to ids without special tokens
          snake_case__  :    Union[str, Any]    						=      [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
          snake_case__  :    List[Any]    						=      tokenizer.convert_tokens_to_ids(snake_case_						)
          self.assertListEqual(snake_case_ ,     snake_case_						)

          # Testing conversion to ids with special tokens
          snake_case__  :    Union[str, Any]    						=      tokens + [tokenizer.unk_token]
          snake_case__  :    Dict    						=      [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
          snake_case__  :    Any    						=      tokenizer.convert_tokens_to_ids(snake_case_						)
          self.assertListEqual(snake_case_ ,     snake_case_						)




    def lowerCamelCase       (    self					:     Optional[Any]						):
          snake_case__  :    Union[str, Any]    						=      self.get_tokenizer()

          # Testing tokenization
          snake_case__  :    Union[str, Any]    						=      """こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"""
          snake_case__  :    Optional[int]    						=      """こんにちは、、、、世界。こんばんは、、、、世界。"""
          snake_case__  :    Any    						=      tokenizer.encode(snake_case_						)
          snake_case__  :    int    						=      tokenizer.decode(snake_case_						)
          self.assertEqual(snake_case_ ,     snake_case_						)




    @slow
    def lowerCamelCase       (    self					:     Union[str, Any]						):
          snake_case__  :    Optional[Any]    						=      self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese"""						)

          # Testing tokenization
          snake_case__  :    Tuple    						=      """こんにちは、世界。"""
          snake_case__  :    Optional[Any]    						=      """こんばんは、㔺界。😀"""
          snake_case__  :    List[str]    						=      """こんにちは、世界。こんばんは、世界。😀"""
          snake_case__  :    Dict    						=      tokenizer.encode(prefix_text + input_text						)
          snake_case__  :    Dict    						=      tokenizer.encode("""""" ,     prefix_text=prefix_text + input_text						)
          snake_case__  :    int    						=      tokenizer.encode(snake_case_ ,     prefix_text=snake_case_						)
          snake_case__  :    Optional[Any]    						=      tokenizer.decode(snake_case_						)
          snake_case__  :    Union[str, Any]    						=      tokenizer.decode(snake_case_						)
          snake_case__  :    str    						=      tokenizer.decode(snake_case_						)
          self.assertEqual(snake_case_ ,     snake_case_						)
          self.assertEqual(snake_case_ ,     snake_case_						)
          self.assertEqual(snake_case_ ,     snake_case_						)




    @slow
    def lowerCamelCase       (    self					:     Union[str, Any]						):
          snake_case__  :    Optional[Any]    						=      self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese"""						)

          # Testing tokenization
          snake_case__  :    Dict    						=      """こんにちは、世界。"""
          snake_case__  :    Optional[int]    						=      """こんばんは、㔺界。😀"""

          snake_case__  :    Any    						=      len(tokenizer.encode(snake_case_						)						) - 2
          snake_case__  :    Optional[int]    						=      len(tokenizer.encode(snake_case_						)						) - 2

          snake_case__  :    List[str]    						=      [1] + [0] * (len_prefix + len_text + 1)
          snake_case__  :    Optional[int]    						=      [1] * (len_prefix + len_text + 1) + [0]
          snake_case__  :    int    						=      [1] + [1] * (len_prefix) + [0] * (len_text + 1)

          snake_case__  :    Any    						=      tokenizer(prefix_text + input_text						).token_type_ids
          snake_case__  :    str    						=      tokenizer("""""" ,     prefix_text=prefix_text + input_text						).token_type_ids
          snake_case__  :    Optional[Any]    						=      tokenizer(snake_case_ ,     prefix_text=snake_case_						).token_type_ids
          self.assertListEqual(snake_case_ ,     snake_case_						)
          self.assertListEqual(snake_case_ ,     snake_case_						)
          self.assertListEqual(snake_case_ ,     snake_case_						)




    @slow
    def lowerCamelCase       (    self					:     Optional[int]						):
          snake_case__  :    Optional[Any]    						=      self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese"""						)

          snake_case__  :    Union[str, Any]    						=      tokenizer.encode("""あンいワ"""						)
          snake_case__  :    int    						=      tokenizer.encode("""""" ,     prefix_text="""あンいワ"""						)
          snake_case__  :    Dict    						=      tokenizer.encode("""いワ""" ,     prefix_text="""あン"""						)

          self.assertEqual(tokenizer.decode(snake_case_						) ,     tokenizer.decode(snake_case_						)						)
          self.assertEqual(tokenizer.decode(snake_case_						) ,     tokenizer.decode(snake_case_						)						)
          self.assertNotEqual(snake_case_ ,     snake_case_						)
          self.assertNotEqual(snake_case_ ,     snake_case_						)
          self.assertEqual(x_token_a[1] ,     x_token_a[-1]						)  # SEG token
          self.assertEqual(x_token_a[1] ,     x_token_a[3]						)  # SEG token




    @slow
    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    Optional[int]    						=      self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese"""						)

          snake_case__  :    int    						=      [["""武田信玄""", """は、"""], ["""織田信長""", """の配下の、"""]]
          snake_case__  :    Optional[Any]    						=      tokenizer(snake_case_ ,     padding=snake_case_						)
          snake_case__  :    Tuple    						=      tokenizer.batch_encode_plus(snake_case_ ,     padding=snake_case_						)

          # fmt: off
          snake_case__  :    Optional[Any]    						=      [[35_993, 8_640, 25_948, 35_998, 30_647, 35_675, 35_999, 35_999], [35_993, 10_382, 9_868, 35_998, 30_646, 9_459, 30_646, 35_675]]
          snake_case__  :    Optional[Any]    						=      [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
          snake_case__  :    Optional[Any]    						=      [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
          # fmt: on
          self.assertListEqual(x_token.input_ids ,     snake_case_						)
          self.assertListEqual(x_token.token_type_ids ,     snake_case_						)
          self.assertListEqual(x_token.attention_mask ,     snake_case_						)
          self.assertListEqual(x_token_a.input_ids ,     snake_case_						)
          self.assertListEqual(x_token_a.token_type_ids ,     snake_case_						)
          self.assertListEqual(x_token_a.attention_mask ,     snake_case_						)




    def lowerCamelCase       (    self					:     Any						):
          # Intentionally convert some words to accommodate character fluctuations unique to Japanese
          pass




    def lowerCamelCase       (    self					:     List[str]						):
          # tokenizer has no padding token
          pass
