





import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Dict:
    def get_masked_lm_array(_UpperCAmelCase					):
        lowerCamelCase__       : Union[str, Any]   					=						F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        lowerCamelCase__       : Tuple   					=						tf.train.load_variable(_UpperCAmelCase		,      _UpperCAmelCase					)

        if "kernel" in name:
            lowerCamelCase__       : int   					=						array.transpose()

        return torch.from_numpy(_UpperCAmelCase					)

    def get_encoder_array(_UpperCAmelCase					):
        lowerCamelCase__       : List[Any]   					=						F"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        lowerCamelCase__       : Union[str, Any]   					=						tf.train.load_variable(_UpperCAmelCase		,      _UpperCAmelCase					)

        if "kernel" in name:
            lowerCamelCase__       : int   					=						array.transpose()

        return torch.from_numpy(_UpperCAmelCase					)

    def get_encoder_layer_array(_UpperCAmelCase		,      _UpperCAmelCase					):
        lowerCamelCase__       : Dict   					=						F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        lowerCamelCase__       : Dict   					=						tf.train.load_variable(_UpperCAmelCase		,      _UpperCAmelCase					)

        if "kernel" in name:
            lowerCamelCase__       : Optional[Any]   					=						array.transpose()

        return torch.from_numpy(_UpperCAmelCase					)

    def get_encoder_attention_layer_array(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					):
        lowerCamelCase__       : Optional[Any]   					=						F"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        lowerCamelCase__       : List[Any]   					=						tf.train.load_variable(_UpperCAmelCase		,      _UpperCAmelCase					)
        lowerCamelCase__       : Optional[Any]   					=						array.reshape(_UpperCAmelCase					)

        if "kernel" in name:
            lowerCamelCase__       : str   					=						array.transpose()

        return torch.from_numpy(_UpperCAmelCase					)

    print(F"""Loading model based on config from {config_path}..."""					)
    lowerCamelCase__       : List[Any]   					=						BertConfig.from_json_file(_UpperCAmelCase					)
    lowerCamelCase__       : Union[str, Any]   					=						BertForMaskedLM(_UpperCAmelCase					)

    # Layers
    for layer_index in range(0		,      config.num_hidden_layers					):
        lowerCamelCase__       : BertLayer   					=						model.bert.encoder.layer[layer_index]

        # Self-attention
        lowerCamelCase__       : BertSelfAttention   					=						layer.attention.self

        lowerCamelCase__       : List[Any]   					=						get_encoder_attention_layer_array(
            _UpperCAmelCase		,      '_query_dense/kernel'		,      self_attn.query.weight.data.shape					)
        lowerCamelCase__       : List[Any]   					=						get_encoder_attention_layer_array(
            _UpperCAmelCase		,      '_query_dense/bias'		,      self_attn.query.bias.data.shape					)
        lowerCamelCase__       : Dict   					=						get_encoder_attention_layer_array(
            _UpperCAmelCase		,      '_key_dense/kernel'		,      self_attn.key.weight.data.shape					)
        lowerCamelCase__       : List[str]   					=						get_encoder_attention_layer_array(
            _UpperCAmelCase		,      '_key_dense/bias'		,      self_attn.key.bias.data.shape					)
        lowerCamelCase__       : List[Any]   					=						get_encoder_attention_layer_array(
            _UpperCAmelCase		,      '_value_dense/kernel'		,      self_attn.value.weight.data.shape					)
        lowerCamelCase__       : Any   					=						get_encoder_attention_layer_array(
            _UpperCAmelCase		,      '_value_dense/bias'		,      self_attn.value.bias.data.shape					)

        # Self-attention Output
        lowerCamelCase__       : BertSelfOutput   					=						layer.attention.output

        lowerCamelCase__       : Any   					=						get_encoder_attention_layer_array(
            _UpperCAmelCase		,      '_output_dense/kernel'		,      self_output.dense.weight.data.shape					)
        lowerCamelCase__       : int   					=						get_encoder_attention_layer_array(
            _UpperCAmelCase		,      '_output_dense/bias'		,      self_output.dense.bias.data.shape					)

        lowerCamelCase__       : Optional[Any]   					=						get_encoder_layer_array(_UpperCAmelCase		,      '_attention_layer_norm/gamma'					)
        lowerCamelCase__       : Union[str, Any]   					=						get_encoder_layer_array(_UpperCAmelCase		,      '_attention_layer_norm/beta'					)

        # Intermediate
        lowerCamelCase__       : BertIntermediate   					=						layer.intermediate

        lowerCamelCase__       : Union[str, Any]   					=						get_encoder_layer_array(_UpperCAmelCase		,      '_intermediate_dense/kernel'					)
        lowerCamelCase__       : Any   					=						get_encoder_layer_array(_UpperCAmelCase		,      '_intermediate_dense/bias'					)

        # Output
        lowerCamelCase__       : BertOutput   					=						layer.output

        lowerCamelCase__       : List[Any]   					=						get_encoder_layer_array(_UpperCAmelCase		,      '_output_dense/kernel'					)
        lowerCamelCase__       : str   					=						get_encoder_layer_array(_UpperCAmelCase		,      '_output_dense/bias'					)

        lowerCamelCase__       : Optional[Any]   					=						get_encoder_layer_array(_UpperCAmelCase		,      '_output_layer_norm/gamma'					)
        lowerCamelCase__       : Dict   					=						get_encoder_layer_array(_UpperCAmelCase		,      '_output_layer_norm/beta'					)

    # Embeddings
    lowerCamelCase__       : Optional[Any]   					=						get_encoder_array('_position_embedding_layer/embeddings'					)
    lowerCamelCase__       : List[str]   					=						get_encoder_array('_type_embedding_layer/embeddings'					)
    lowerCamelCase__       : int   					=						get_encoder_array('_embedding_norm_layer/gamma'					)
    lowerCamelCase__       : List[Any]   					=						get_encoder_array('_embedding_norm_layer/beta'					)

    # LM Head
    lowerCamelCase__       : Dict   					=						model.cls.predictions.transform

    lowerCamelCase__       : Optional[Any]   					=						get_masked_lm_array('dense/kernel'					)
    lowerCamelCase__       : int   					=						get_masked_lm_array('dense/bias'					)

    lowerCamelCase__       : Any   					=						get_masked_lm_array('layer_norm/gamma'					)
    lowerCamelCase__       : Optional[Any]   					=						get_masked_lm_array('layer_norm/beta'					)

    lowerCamelCase__       : List[str]   					=						get_masked_lm_array('embedding_table'					)

    # Pooling
    lowerCamelCase__       : Any   					=						BertPooler(config=_UpperCAmelCase					)
    lowerCamelCase__       : BertPooler   					=						get_encoder_array('_pooler_layer/kernel'					)
    lowerCamelCase__       : BertPooler   					=						get_encoder_array('_pooler_layer/bias'					)

    # Export final model
    model.save_pretrained(_UpperCAmelCase					)

    # Integration test - should load without any errors ;)
    lowerCamelCase__       : Dict   					=						BertForMaskedLM.from_pretrained(_UpperCAmelCase					)
    print(new_model.eval()					)

    print('Model conversion was done sucessfully!'					)


if __name__ == "__main__":
    _UpperCAmelCase							:			Optional[int]        =							argparse.ArgumentParser()
    parser.add_argument(
        """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow Token Dropping checkpoint path."""
    )
    parser.add_argument(
        """--bert_config_file""",
        type=str,
        required=True,
        help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
    )
    parser.add_argument(
        """--pytorch_dump_path""",
        type=str,
        required=True,
        help="""Path to the output PyTorch model.""",
    )
    _UpperCAmelCase							:			Dict        =							parser.parse_args()
    convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)












import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Dict:
    lowerCamelCase__       : Optional[int]   					=						[]
    embed.append(
        (
            F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
            F"""stage{idx}.patch_embed.proj.weight""",
        )					)
    embed.append(
        (
            F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
            F"""stage{idx}.patch_embed.proj.bias""",
        )					)
    embed.append(
        (
            F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
            F"""stage{idx}.patch_embed.norm.weight""",
        )					)
    embed.append(
        (
            F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
            F"""stage{idx}.patch_embed.norm.bias""",
        )					)
    return embed


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	List[str]:
    lowerCamelCase__       : Optional[int]   					=						[]
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
            F"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
            F"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
            F"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
            F"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
        )					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", F"""stage{idx}.blocks.{cnt}.norm1.weight""")					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", F"""stage{idx}.blocks.{cnt}.norm1.bias""")					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", F"""stage{idx}.blocks.{cnt}.norm2.weight""")					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", F"""stage{idx}.blocks.{cnt}.norm2.bias""")					)
    return attention_weights


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : Union[str, Any]   					=						[]
    token.append((F"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')					)
    return token


def 			SCREAMING_SNAKE_CASE (			)     ->	Dict:
    lowerCamelCase__       : Tuple   					=						[]
    head.append(('layernorm.weight', 'norm.weight')					)
    head.append(('layernorm.bias', 'norm.bias')					)
    head.append(('classifier.weight', 'head.weight')					)
    head.append(('classifier.bias', 'head.bias')					)
    return head


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Optional[int]:
    lowerCamelCase__       : Union[str, Any]   					=						'imagenet-1k-id2label.json'
    lowerCamelCase__       : Tuple   					=						1000

    lowerCamelCase__       : Tuple   					=						'huggingface/label-files'
    lowerCamelCase__       : Optional[int]   					=						num_labels
    lowerCamelCase__       : Optional[Any]   					=						json.load(open(cached_download(hf_hub_url(_UpperCAmelCase		,      _UpperCAmelCase		,      repo_type='dataset'					)					)		,      'r'					)					)
    lowerCamelCase__       : Optional[int]   					=						{int(_UpperCAmelCase					): v for k, v in idalabel.items()}

    lowerCamelCase__       : List[Any]   					=						idalabel
    lowerCamelCase__       : Optional[Any]   					=						{v: k for k, v in idalabel.items()}

    lowerCamelCase__       : int   					=						CvtConfig(num_labels=_UpperCAmelCase		,      idalabel=_UpperCAmelCase		,      labelaid=_UpperCAmelCase					)

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit('/'		,      1					)[-1][4:6] == "13":
        lowerCamelCase__       : Dict   					=						[1, 2, 10]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit('/'		,      1					)[-1][4:6] == "21":
        lowerCamelCase__       : List[str]   					=						[1, 4, 16]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        lowerCamelCase__       : Tuple   					=						[2, 2, 20]
        lowerCamelCase__       : Optional[int]   					=						[3, 12, 16]
        lowerCamelCase__       : int   					=						[192, 768, 1024]

    lowerCamelCase__       : Tuple   					=						CvtForImageClassification(_UpperCAmelCase					)
    lowerCamelCase__       : str   					=						AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'					)
    lowerCamelCase__       : Optional[Any]   					=						image_size
    lowerCamelCase__       : int   					=						torch.load(_UpperCAmelCase		,      map_location=torch.device('cpu'					)					)

    lowerCamelCase__       : Dict   					=						OrderedDict()
    lowerCamelCase__       : Union[str, Any]   					=						[]

    for idx in range(len(config.depth					)					):
        if config.cls_token[idx]:
            lowerCamelCase__       : str   					=						list_of_state_dict + cls_token(_UpperCAmelCase					)
        lowerCamelCase__       : Tuple   					=						list_of_state_dict + embeddings(_UpperCAmelCase					)
        for cnt in range(config.depth[idx]					):
            lowerCamelCase__       : List[str]   					=						list_of_state_dict + attention(_UpperCAmelCase		,      _UpperCAmelCase					)

    lowerCamelCase__       : Optional[Any]   					=						list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_UpperCAmelCase					)
    for i in range(len(_UpperCAmelCase					)					):
        lowerCamelCase__       : List[str]   					=						original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_UpperCAmelCase					)
    model.save_pretrained(_UpperCAmelCase					)
    image_processor.save_pretrained(_UpperCAmelCase					)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
    _UpperCAmelCase							:			int        =							argparse.ArgumentParser()
    parser.add_argument(
        """--cvt_model""",
        default="""cvt-w24""",
        type=str,
        help="""Name of the cvt model you'd like to convert.""",
    )
    parser.add_argument(
        """--image_size""",
        default=3_84,
        type=int,
        help="""Input Image Size""",
    )
    parser.add_argument(
        """--cvt_file_name""",
        default=R"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
        type=str,
        help="""Input Image Size""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
    )

    _UpperCAmelCase							:			Optional[int]        =							parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





