
"""simple docstring"""






import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->List[str]:

   """simple docstring"""



   a_   ,			a_   ,			a_						   =     pt_weights[F'''{prefix}.in_proj_weight'''].chunk(3 ,   dim=0     )
   a_   ,			a_   ,			a_						   =     pt_weights[F'''{prefix}.in_proj_bias'''].chunk(3 ,   dim=0     )

   a_						   =     pt_weights[F'''{prefix}.out_proj.weight''']
   a_						   =     pt_weights[F'''{prefix}.out_proj.bias''']

   a_						   =     q_proj
   a_						   =     q_proj_bias

   a_						   =     k_proj
   a_						   =     k_proj_bias

   a_						   =     v_proj
   a_						   =     v_proj_bias

   a_						   =     out_proj_weights
   a_						   =     out_proj_bias
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->Dict:

   """simple docstring"""



   copy_linear(hf_mlp.fca ,   UpperCAmelCase ,   F'''{prefix}.c_fc'''     )
   copy_linear(hf_mlp.fca ,   UpperCAmelCase ,   F'''{prefix}.c_proj'''     )
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->str:

   """simple docstring"""



   a_						   =     pt_weights[F'''{prefix}.weight'''].data
   a_						   =     pt_weights[F'''{prefix}.bias'''].data
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->Union[str, Any]:

   """simple docstring"""



   copy_linear(hf_layer.layer_norma ,   UpperCAmelCase ,   F'''{prefix}.ln_1'''     )
   copy_linear(hf_layer.layer_norma ,   UpperCAmelCase ,   F'''{prefix}.ln_2'''     )

   # copy MLP
   copy_mlp(hf_layer.mlp ,   UpperCAmelCase ,   F'''{prefix}.mlp'''     )

   # copy attn
   copy_attn_layer(hf_layer.self_attn ,   UpperCAmelCase ,   F'''{prefix}.attn'''     )
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->Optional[Any]:

   """simple docstring"""



   for layer_id, hf_layer in enumerate(UpperCAmelCase     ):
      copy_layer(UpperCAmelCase ,   UpperCAmelCase ,   F'''{prefix}.{layer_id}'''     )
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase     )						->Optional[Any]:

   """simple docstring"""



   a_						   =     pt_weights["text_projection"].data.T

   # copy text encoder
   for name, param in hf_model.text_model.named_parameters():
      a_						   =     pt_weights[F'''bert.{name}'''].data
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase     )						->Dict:

   """simple docstring"""



   a_						   =     pt_weights["visual.proj"].data.T

   # copy layer norms
   copy_linear(hf_model.vision_model.pre_layrnorm ,   UpperCAmelCase ,   "visual.ln_pre"     )
   copy_linear(hf_model.vision_model.post_layernorm ,   UpperCAmelCase ,   "visual.ln_post"     )

   # copy embeddings
   a_						   =     pt_weights["visual.conv1.weight"].data
   a_						   =     pt_weights["visual.class_embedding"].data
   a_						   =     pt_weights["visual.positional_embedding"].data

   # copy encoder
   copy_layers(hf_model.vision_model.encoder.layers ,   UpperCAmelCase ,   "visual.transformer.resblocks"     )
@torch.no_grad()
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase=None     )						->List[Any]:

   """simple docstring"""



   assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
   a_						   =     ChineseCLIPConfig.from_pretrained(UpperCAmelCase     )

   a_						   =     ChineseCLIPModel(UpperCAmelCase     ).eval()

   a_						   =     torch.load(UpperCAmelCase ,   map_location="cpu"     )["state_dict"]
   a_						   =     {(name[7:] if name.startswith("module."     ) else name): value for name, value in pt_weights.items()}

   copy_text_model_and_projection(UpperCAmelCase ,   UpperCAmelCase     )
   copy_vision_model_and_projection(UpperCAmelCase ,   UpperCAmelCase     )
   a_						   =     pt_weights["logit_scale"].data

   hf_model.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
      UpperCamelCase_						   =   argparse.ArgumentParser()
      parser.add_argument(
          '--pytorch_dump_folder_path',
          default=None,
          type=str,
          help='Path to the output folder storing converted hf PyTorch model.',
      )
      parser.add_argument(
          '--checkpoint_path', default=None, type=str, help='Path to original github format ChineseCLIP checkpoint.'
      )
      parser.add_argument(
          '--config_path', default=None, required=True, type=str, help='Path to hf config.json of model to convert.'
      )
      UpperCamelCase_						   =   parser.parse_args()

      convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
      print('The conversion is finished!')

"""simple docstring"""






import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 							snake_case       (     SCREAMING_SNAKE_CASE_							,       unittest.TestCase		):
   a_					:		Optional[int]     					=     GPTNeoXJapaneseTokenizer
   a_					:		Dict     					=     False
   a_					:		Union[str, Any]     					=     {"""do_clean_text""": False, """add_prefix_space""": False}







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      super().setUp()

      a_						   =     [
          "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>",
          "<|startoftext|>",
          "<|endoftext|>",
      ]
      a_						   =     {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
      a_						   =     {"unk_token": "<unk>"}

      a_						   =     os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["vocab_file"])
      a_						   =     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(__UpperCAmelCase))







   def    UpperCAmelCase__	( self       ,							**__UpperCAmelCase)   ->int:
      kwargs.update(self.special_tokens_map)
      return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname       ,							**__UpperCAmelCase)







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase)   ->Dict:
      a_						   =     "こんにちは、世界。 \nこんばんは、㔺界。😀"
      a_						   =     "こんにちは、世界。 \nこんばんは、世界。😀"
      return input_text, output_text







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase)   ->Optional[Any]:
      a_   ,			a_						   =     self.get_input_output_texts(__UpperCAmelCase)
      a_						   =     tokenizer.encode(__UpperCAmelCase       ,							add_special_tokens=__UpperCAmelCase)
      a_						   =     tokenizer.decode(__UpperCAmelCase       ,							clean_up_tokenization_spaces=__UpperCAmelCase)
      return text, ids







   def    UpperCAmelCase__	( self)   ->Any:
      pass  # TODO add if relevant







   def    UpperCAmelCase__	( self)   ->int:
      pass  # TODO add if relevant







   def    UpperCAmelCase__	( self)   ->Optional[Any]:
      pass  # TODO add if relevant







   def    UpperCAmelCase__	( self)   ->List[str]:
      a_						   =     self.get_tokenizer()

      # Testing tokenization
      a_						   =     "こんにちは、世界。　こんばんは、㔺界。"
      a_						   =     ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
      a_						   =     tokenizer.tokenize(__UpperCAmelCase)
      self.assertListEqual(__UpperCAmelCase       ,							__UpperCAmelCase)

      # Testing conversion to ids without special tokens
      a_						   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
      a_						   =     tokenizer.convert_tokens_to_ids(__UpperCAmelCase)
      self.assertListEqual(__UpperCAmelCase       ,							__UpperCAmelCase)

      # Testing conversion to ids with special tokens
      a_						   =     tokens + [tokenizer.unk_token]
      a_						   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
      a_						   =     tokenizer.convert_tokens_to_ids(__UpperCAmelCase)
      self.assertListEqual(__UpperCAmelCase       ,							__UpperCAmelCase)







   @slow
   def    UpperCAmelCase__	( self)   ->Tuple:
      a_						   =     self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b")

      a_						   =     tokenizer.encode("ありがとう。"       ,							add_special_tokens=__UpperCAmelCase)
      a_						   =     tokenizer.encode("どういたしまして。"       ,							add_special_tokens=__UpperCAmelCase)

      a_						   =     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase)
      a_						   =     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase       ,							__UpperCAmelCase)

      assert encoded_sentence == ids_a
      assert encoded_pair == ids_a + ids_a







   def    UpperCAmelCase__	( self)   ->Tuple:
      # Intentionally convert some words to accommodate character fluctuations unique to Japanese
      pass







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      # tokenizer has no padding token
      pass