






"""simple docstring"""

import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)




def __lowercase    (							_a	,    _a=False					):
    snake_case_				:  Dict       =			[]
    for i in range(config.num_hidden_layers					):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((f"module.blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")					)
        rename_keys.append((f"module.blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")					)
        rename_keys.append(
            (f"module.blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")					)
        rename_keys.append((f"module.blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")					)
        rename_keys.append((f"module.blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")					)
        rename_keys.append((f"module.blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")					)
        rename_keys.append((f"module.blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")					)
        rename_keys.append((f"module.blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")					)
        rename_keys.append((f"module.blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")					)
        rename_keys.append((f"module.blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")					)

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

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

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

    return rename_keys




def __lowercase    (							_a	,    _a	,    _a=False					):







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




def __lowercase    (							_a					):
    snake_case_				:  str       =			['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(__UpperCAmelCase	,    __UpperCAmelCase					)




def __lowercase    (							_a					):
    snake_case_				:  Any       =			[
        '''module.fc.fc1.weight''',
        '''module.fc.fc1.bias''',
        '''module.fc.bn1.weight''',
        '''module.fc.bn1.bias''',
        '''module.fc.bn1.running_mean''',
        '''module.fc.bn1.running_var''',
        '''module.fc.bn1.num_batches_tracked''',
        '''module.fc.fc2.weight''',
        '''module.fc.fc2.bias''',
        '''module.fc.bn2.weight''',
        '''module.fc.bn2.bias''',
        '''module.fc.bn2.running_mean''',
        '''module.fc.bn2.running_var''',
        '''module.fc.bn2.num_batches_tracked''',
        '''module.fc.fc3.weight''',
        '''module.fc.fc3.bias''',
    ]
    for k in ignore_keys:
        state_dict.pop(__UpperCAmelCase	,    __UpperCAmelCase					)




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Dict       =			dct.pop(__UpperCAmelCase					)
    snake_case_				:  List[str]       =			val




def __lowercase    (							_a	,    _a					):
    snake_case_				:  Tuple       =			ViTMSNConfig()
    snake_case_				:  int       =			1_000

    snake_case_				:  Tuple       =			'''datasets/huggingface/label-files'''
    snake_case_				:  Optional[int]       =			'''imagenet-1k-id2label.json'''
    snake_case_				:  Dict       =			json.load(open(hf_hub_download(__UpperCAmelCase	,    __UpperCAmelCase					)	,    '''r'''					)					)
    snake_case_				:  Any       =			{int(__UpperCAmelCase					): v for k, v in idalabel.items()}
    snake_case_				:  Dict       =			idalabel
    snake_case_				:  List[Any]       =			{v: k for k, v in idalabel.items()}

    if "s16" in checkpoint_url:
        snake_case_				:  Dict       =			384
        snake_case_				:  List[Any]       =			1_536
        snake_case_				:  Tuple       =			6
    elif "l16" in checkpoint_url:
        snake_case_				:  Optional[Any]       =			1_024
        snake_case_				:  int       =			4_096
        snake_case_				:  List[str]       =			24
        snake_case_				:  Dict       =			16
        snake_case_				:  Any       =			0.1
    elif "b4" in checkpoint_url:
        snake_case_				:  Union[str, Any]       =			4
    elif "l7" in checkpoint_url:
        snake_case_				:  Any       =			7
        snake_case_				:  Dict       =			1_024
        snake_case_				:  Any       =			4_096
        snake_case_				:  List[Any]       =			24
        snake_case_				:  str       =			16
        snake_case_				:  List[Any]       =			0.1

    snake_case_				:  Optional[int]       =			ViTMSNModel(__UpperCAmelCase					)

    snake_case_				:  Optional[Any]       =			torch.hub.load_state_dict_from_url(__UpperCAmelCase	,    map_location='''cpu'''					)['''target_encoder''']

    snake_case_				:  List[Any]       =			ViTImageProcessor(size=config.image_size					)

    remove_projection_head(__UpperCAmelCase					)
    snake_case_				:  List[Any]       =			create_rename_keys(__UpperCAmelCase	,    base_model=__UpperCAmelCase					)

    for src, dest in rename_keys:
        rename_key(__UpperCAmelCase	,    __UpperCAmelCase	,    __UpperCAmelCase					)
    read_in_q_k_v(__UpperCAmelCase	,    __UpperCAmelCase	,    base_model=__UpperCAmelCase					)

    model.load_state_dict(__UpperCAmelCase					)
    model.eval()

    snake_case_				:  Tuple       =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''

    snake_case_				:  int       =			Image.open(requests.get(__UpperCAmelCase	,    stream=__UpperCAmelCase					).raw					)
    snake_case_				:  Optional[Any]       =			ViTImageProcessor(
        size=config.image_size	,    image_mean=__UpperCAmelCase	,    image_std=__UpperCAmelCase					)
    snake_case_				:  str       =			image_processor(images=__UpperCAmelCase	,    return_tensors='''pt'''					)

    # forward pass
    torch.manual_seed(2					)
    snake_case_				:  int       =			model(**__UpperCAmelCase					)
    snake_case_				:  Union[str, Any]       =			outputs.last_hidden_state

    # The following Colab Notebook was used to generate these outputs:
    # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
    if "s16" in checkpoint_url:
        snake_case_				:  Tuple       =			torch.tensor([[-1.0915, -1.4876, -1.1809]]					)
    elif "b16" in checkpoint_url:
        snake_case_				:  Union[str, Any]       =			torch.tensor([[14.2_889, -18.9_045, 11.7_281]]					)
    elif "l16" in checkpoint_url:
        snake_case_				:  List[Any]       =			torch.tensor([[41.5_028, -22.8_681, 45.6_475]]					)
    elif "b4" in checkpoint_url:
        snake_case_				:  List[Any]       =			torch.tensor([[-4.3868, 5.2932, -0.4137]]					)
    else:
        snake_case_				:  Optional[Any]       =			torch.tensor([[-0.1792, -0.6465, 2.4263]]					)

    # verify logits
    assert torch.allclose(last_hidden_state[:, 0, :3]	,    __UpperCAmelCase	,    atol=1E-4					)

    print(f"Saving model to {pytorch_dump_folder_path}"					)
    model.save_pretrained(__UpperCAmelCase					)

    print(f"Saving image processor to {pytorch_dump_folder_path}"					)
    image_processor.save_pretrained(__UpperCAmelCase					)


if __name__ == "__main__":
     lowercase__					:						List[str]												=			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--checkpoint_url''',
         default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
         type=str,
         help='''URL of the checkpoint 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__					:						int												=			parser.parse_args()
     convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			AutoConfig.from_pretrained(_a					)
    snake_case_				:  Tuple       =			FlaxAutoModelForSeqaSeqLM.from_config(config=_a					)
    snake_case_				:  Union[str, Any]       =			checkpoints.load_tax_checkpoint(_a					)

    snake_case_				:  Optional[int]       =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        snake_case_				:  str       =			'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        snake_case_				:  List[str]       =			'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  Dict       =			'''TransientGlobalSelfAttention'''
    else:
        raise ValueError(
            '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
            ''' attribute with a value from [\'local\', \'transient-global].'''					)

    # Encoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Any       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  Dict       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  str       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[Any]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Optional[Any]       =			flax_model.params['''encoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  List[str]       =			tax_attention_key
        snake_case_				:  Optional[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  str       =			tax_attention_value

        snake_case_				:  Dict       =			tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Union[str, Any]       =			tax_global_layer_norm

        if split_mlp_wi:
            snake_case_				:  Any       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  Union[str, Any]       =			tax_mlp_wi

        snake_case_				:  List[Any]       =			tax_mlp_wo
        snake_case_				:  int       =			tax_mlp_layer_norm

        snake_case_				:  Any       =			flax_model_encoder_layer_block

    # Only for layer 0:
    snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Any       =			tax_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        snake_case_				:  Tuple       =			tax_encoder_global_rel_embedding

    # Assigning
    snake_case_				:  Dict       =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    snake_case_				:  Any       =			tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Tuple       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  str       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        snake_case_				:  Any       =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        snake_case_				:  Optional[Any]       =			tax_enc_dec_attention_module['''key''']['''kernel''']
        snake_case_				:  str       =			tax_enc_dec_attention_module['''out''']['''kernel''']
        snake_case_				:  Union[str, Any]       =			tax_enc_dec_attention_module['''query''']['''kernel''']
        snake_case_				:  List[str]       =			tax_enc_dec_attention_module['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  Dict       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Dict       =			flax_model.params['''decoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  int       =			tax_attention_key
        snake_case_				:  List[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  Dict       =			tax_attention_value

        snake_case_				:  str       =			tax_pre_attention_layer_norm

        snake_case_				:  Any       =			tax_enc_dec_attention_key
        snake_case_				:  str       =			tax_enc_dec_attention_out
        snake_case_				:  int       =			tax_enc_dec_attention_query
        snake_case_				:  Any       =			tax_enc_dec_attention_value

        snake_case_				:  Optional[Any]       =			tax_cross_layer_norm

        if split_mlp_wi:
            snake_case_				:  Tuple       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  List[Any]       =			tax_mlp_wi

        snake_case_				:  Dict       =			tax_mlp_wo

        snake_case_				:  List[Any]       =			txa_mlp_layer_norm

        snake_case_				:  Optional[int]       =			flax_model_decoder_layer_block

    # Decoder Normalization
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    snake_case_				:  Tuple       =			txa_decoder_norm

    # Only for layer 0:
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Optional[Any]       =			tax_decoder_rel_embedding

    # Token Embeddings
    snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''token_embedder''']['''embedding''']
    snake_case_				:  Optional[int]       =			txa_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

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


if __name__ == "__main__":
     lowercase__					:						Tuple												=			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
     )
     parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
     parser.add_argument(
         '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
     )
     lowercase__					:						Dict												=			parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





