





import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin



class 				__snake_case  (     __SCREAMING_SNAKE_CASE ,    unittest.TestCase						):


    '''simple docstring'''







    lowerCamelCase__						:		Tuple							=  SpeechaTextaTokenizer
    lowerCamelCase__						:		Any							=  False



    def    lowercase_				(					self    ):





        '''simple docstring'''

        super().setUp()

        SCREAMING_SNAKE_CASE__       							= '''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''    )
        SCREAMING_SNAKE_CASE__       							= ['''he re</w> 123''', '''here a 1456''']
        SCREAMING_SNAKE_CASE__       							= dict(zip(A_      ,							range(len(A_    )    )    )    )

        SCREAMING_SNAKE_CASE__       							= {'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

        SCREAMING_SNAKE_CASE__       							= tempfile.mkdtemp()
        SCREAMING_SNAKE_CASE__       							= os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES['''vocab_file''']    )
        SCREAMING_SNAKE_CASE__       							= os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES['''merges_file''']    )

        with open(self.vocab_file      ,							'''w'''      ,							encoding='''utf-8'''    ) as fp:
            fp.write(json.dumps(A_    ) + '''\n'''    )

        with open(self.merges_file      ,							'''w'''    ) as fp:
            fp.write('''\n'''.join(A_    )    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= list(self.get_tokenizer().get_vocab().keys()    )

        self.assertEqual(vocab_keys[0]      ,							'''<s>'''    )
        self.assertEqual(vocab_keys[1]      ,							'''<pad>'''    )
        self.assertEqual(vocab_keys[-1]      ,							'''vocab'''    )
        self.assertEqual(len(A_    )      ,							14    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        self.assertEqual(self.get_tokenizer().vocab_size      ,							14    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= SpeechaTextaTokenizer.from_pretrained(self.tmpdirname    )

        # make sure @@ is correctly concatenated
        SCREAMING_SNAKE_CASE__       							= [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
        SCREAMING_SNAKE_CASE__       							= tokenizer.decode(A_    )

        self.assertTrue(output_string == '''herecouple words ofthe'''    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= SpeechaTextaTokenizer.from_pretrained(self.tmpdirname    )

        with tempfile.TemporaryDirectory() as tmp_dirname:
            tokenizer.save_pretrained(A_    )
            os.remove(os.path.join(A_      ,							'''merges.txt'''    )    )

            # load tokenizer without merges file should not throw an error
            SCREAMING_SNAKE_CASE__       							= SpeechaTextaTokenizer.from_pretrained(A_    )

        with tempfile.TemporaryDirectory() as tmp_dirname:
            # save tokenizer and load again
            tokenizer.save_pretrained(A_    )
            SCREAMING_SNAKE_CASE__       							= SpeechaTextaTokenizer.from_pretrained(A_    )

        self.assertIsNotNone(A_    )




    def    lowercase_				(					self    ):





        '''simple docstring'''

        if not self.test_slow_tokenizer:
            return

        SCREAMING_SNAKE_CASE__       							= inspect.signature(self.tokenizer_class.__init__    )
        SCREAMING_SNAKE_CASE__       							= self.get_tokenizer()

        for parameter_name, parameter in signature.parameters.items():
            if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                self.assertIn(A_      ,							tokenizer.init_kwargs    )







import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.in_proj_weight'''].chunk(3   ,       dim=0  )
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.in_proj_bias'''].chunk(3   ,       dim=0  )

    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.out_proj.weight''']
    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.out_proj.bias''']

    SCREAMING_SNAKE_CASE__       							= q_proj
    SCREAMING_SNAKE_CASE__       							= q_proj_bias

    SCREAMING_SNAKE_CASE__       							= k_proj
    SCREAMING_SNAKE_CASE__       							= k_proj_bias

    SCREAMING_SNAKE_CASE__       							= v_proj
    SCREAMING_SNAKE_CASE__       							= v_proj_bias

    SCREAMING_SNAKE_CASE__       							= out_proj_weights
    SCREAMING_SNAKE_CASE__       							= out_proj_bias


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Dict:
    copy_linear(hf_mlp.fca   ,       lowerCAmelCase_   ,       f'''{prefix}.c_fc'''  )
    copy_linear(hf_mlp.fca   ,       lowerCAmelCase_   ,       f'''{prefix}.c_proj'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.weight'''].data
    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.bias'''].data


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Dict:
    # copy layer norms
    copy_linear(hf_layer.layer_norma   ,       lowerCAmelCase_   ,       f'''{prefix}.ln_1'''  )
    copy_linear(hf_layer.layer_norma   ,       lowerCAmelCase_   ,       f'''{prefix}.ln_2'''  )

    # copy MLP
    copy_mlp(hf_layer.mlp   ,       lowerCAmelCase_   ,       f'''{prefix}.mlp'''  )

    # copy attn
    copy_attn_layer(hf_layer.self_attn   ,       lowerCAmelCase_   ,       f'''{prefix}.attn'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Optional[int]:
    for layer_id, hf_layer in enumerate(lowerCAmelCase_  ):
        copy_layer(lowerCAmelCase_   ,       lowerCAmelCase_   ,       f'''{prefix}.{layer_id}'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Any:
    # copy projection
    SCREAMING_SNAKE_CASE__       							= pt_weights['''text_projection'''].data.T

    # copy text encoder
    for name, param in hf_model.text_model.named_parameters():
        SCREAMING_SNAKE_CASE__       							= pt_weights[f'''bert.{name}'''].data


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	List[str]:
    # copy projection
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.proj'''].data.T

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

    # copy embeddings
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.conv1.weight'''].data
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.class_embedding'''].data
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.positional_embedding'''].data

    # copy encoder
    copy_layers(hf_model.vision_model.encoder.layers   ,       lowerCAmelCase_   ,       '''visual.transformer.resblocks'''  )


@torch.no_grad()
def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_=None  )							->	str:
    assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
    SCREAMING_SNAKE_CASE__       							= ChineseCLIPConfig.from_pretrained(lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__       							= ChineseCLIPModel(lowerCAmelCase_  ).eval()

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

    copy_text_model_and_projection(lowerCAmelCase_   ,       lowerCAmelCase_  )
    copy_vision_model_and_projection(lowerCAmelCase_   ,       lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= pt_weights['''logit_scale'''].data

    hf_model.save_pretrained(lowerCAmelCase_  )


if __name__ == "__main__":
  _A				:							Union[str, Any]							= 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."""
  )
  _A				:							Union[str, Any]							= parser.parse_args()

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