


"""simple docstring"""







import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel
def _A		(		lowercase ,					lowercase ,					lowercase	):







    """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 _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    copy_linear(hf_mlp.fca ,					lowercase ,					f'''{prefix}.c_fc'''	)
    copy_linear(hf_mlp.fca ,					lowercase ,					f'''{prefix}.c_proj'''	)
def _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    a					=pt_weights[f'''{prefix}.weight'''].data
    a					=pt_weights[f'''{prefix}.bias'''].data
def _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    # copy layer norms
    copy_linear(hf_layer.layer_norma ,					lowercase ,					f'''{prefix}.ln_1'''	)
    copy_linear(hf_layer.layer_norma ,					lowercase ,					f'''{prefix}.ln_2'''	)

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

    # copy attn
    copy_attn_layer(hf_layer.self_attn ,					lowercase ,					f'''{prefix}.attn'''	)
def _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    for layer_id, hf_layer in enumerate(lowercase	):
        copy_layer(lowercase ,					lowercase ,					f'''{prefix}.{layer_id}'''	)
def _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    # copy projection
    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 _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    # copy projection
    a					=pt_weights['''visual.proj'''].data.T

    # copy layer norms
    copy_linear(hf_model.vision_model.pre_layrnorm ,					lowercase ,					'''visual.ln_pre'''	)
    copy_linear(hf_model.vision_model.post_layernorm ,					lowercase ,					'''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 ,					lowercase ,					'''visual.transformer.resblocks'''	)
@torch.no_grad()
def _A		(		lowercase ,					lowercase ,					lowercase=None	):







    """simple docstring"""
    assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
    a					=ChineseCLIPConfig.from_pretrained(lowercase	)

    a					=ChineseCLIPModel(lowercase	).eval()

    a					=torch.load(lowercase ,					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(lowercase ,					lowercase	)
    copy_vision_model_and_projection(lowercase ,					lowercase	)
    a					=pt_weights['''logit_scale'''].data

    hf_model.save_pretrained(lowercase	)


if __name__ == "__main__":
       lowerCamelCase_     :     Tuple    		=						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."""
       )
       lowerCamelCase_     :     Optional[Any]    		=						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 argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    a					=TaConfig.from_pretrained(lowercase	)
    a					=FlaxTaForConditionalGeneration(config=lowercase	)
    a					=checkpoints.load_tax_checkpoint(lowercase	)

    a					='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    # Encoder
    for layer_index in range(config.num_layers	):
        a					=f'''layers_{str(lowercase	)}'''

        # Self-Attention
        a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

        # Layer Normalization
        a					=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        a					=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        a					=tax_attention_key
        a					=tax_attention_out
        a					=tax_attention_query
        a					=tax_attention_value

        a					=tax_attention_layer_norm

        if split_mlp_wi:
            a					=tax_mlp_wi_a
            a					=tax_mlp_wi_a
        else:
            a					=tax_mlp_wi

        a					=tax_mlp_wo
        a					=tax_mlp_layer_norm

    # Only for layer 0:
    a					=tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    a					=tax_encoder_rel_embedding

    # Assigning
    a					=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    a					=tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_decoder_layers	):
        a					=f'''layers_{str(lowercase	)}'''

        # Self-Attention
        a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
            '''kernel'''
        ]
        a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
            '''kernel'''
        ]
        a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
            '''kernel'''
        ]
        a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
            '''kernel'''
        ]

        # Layer Normalization
        a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        a					=tax_attention_key
        a					=tax_attention_out
        a					=tax_attention_query
        a					=tax_attention_value

        a					=tax_pre_attention_layer_norm

        a					=tax_enc_dec_attention_key
        a					=tax_enc_dec_attention_out
        a					=tax_enc_dec_attention_query
        a					=tax_enc_dec_attention_value

        a					=tax_cross_layer_norm

        if split_mlp_wi:
            a					=tax_mlp_wi_a
            a					=tax_mlp_wi_a
        else:
            a					=tax_mlp_wi

        a					=tax_mlp_wo

        a					=txa_mlp_layer_norm

    # Decoder Normalization
    a					=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    a					=txa_decoder_norm

    # Only for layer 0:
    a					=tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    a					=tax_decoder_rel_embedding

    # Token Embeddings
    a					=tax_model['''target''']['''token_embedder''']['''embedding''']
    a					=txa_token_embeddings

    # LM Head (only in v1.1 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        a					=tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

    flax_model.save_pretrained(lowercase	)
    print('''T5X Model was sucessfully converted!'''	)


if __name__ == "__main__":
       lowerCamelCase_     :     Dict    		=						argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
       )
       parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
       parser.add_argument(
           """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
       )
       lowerCamelCase_     :     Optional[int]    		=						parser.parse_args()
       convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)