import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase									=   logging.get_logger(__name__)



def lowerCamelCase    (  SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







    __UpperCamelCase	:Optional[int]         =						MobileViTConfig()

    # size of the architecture
    if "mobilevit_s" in mobilevit_name:
        __UpperCamelCase	:int         =						[144, 192, 240]
        __UpperCamelCase	:Any         =						[16, 32, 64, 96, 128, 160, 640]
    elif "mobilevit_xs" in mobilevit_name:
        __UpperCamelCase	:Dict         =						[96, 120, 144]
        __UpperCamelCase	:int         =						[16, 32, 48, 64, 80, 96, 384]
    elif "mobilevit_xxs" in mobilevit_name:
        __UpperCamelCase	:Optional[Any]         =						[64, 80, 96]
        __UpperCamelCase	:Optional[int]         =						[16, 16, 24, 48, 64, 80, 320]
        __UpperCamelCase	:int         =						0.05
        __UpperCamelCase	:Optional[int]         =						2.0

    if mobilevit_name.startswith('''deeplabv3_'''      ):
        __UpperCamelCase	:List[str]         =						512
        __UpperCamelCase	:int         =						16
        __UpperCamelCase	:Optional[Any]         =						21
        __UpperCamelCase	:Any         =						'''pascal-voc-id2label.json'''
    else:
        __UpperCamelCase	:List[Any]         =						1_000
        __UpperCamelCase	:Union[str, Any]         =						'''imagenet-1k-id2label.json'''

    __UpperCamelCase	:str         =						'''huggingface/label-files'''
    __UpperCamelCase	:List[str]         =						json.load(open(hf_hub_download(UpperCamelCase__    ,     UpperCamelCase__    ,     repo_type='''dataset'''      )    ,     '''r'''      )      )
    __UpperCamelCase	:Any         =						{int(UpperCamelCase__      ): v for k, v in idalabel.items()}
    __UpperCamelCase	:Dict         =						idalabel
    __UpperCamelCase	:Dict         =						{v: k for k, v in idalabel.items()}

    return config



def lowerCamelCase    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE=False      ):
    '''simple docstring'''







    for i in range(1    ,     6      ):
        if f"""layer_{i}.""" in name:
            __UpperCamelCase	:Tuple         =						name.replace(f"""layer_{i}."""    ,     f"""encoder.layer.{i - 1}."""      )

    if "conv_1." in name:
        __UpperCamelCase	:Tuple         =						name.replace('''conv_1.'''    ,     '''conv_stem.'''      )
    if ".block." in name:
        __UpperCamelCase	:Any         =						name.replace('''.block.'''    ,     '''.'''      )

    if "exp_1x1" in name:
        __UpperCamelCase	:int         =						name.replace('''exp_1x1'''    ,     '''expand_1x1'''      )
    if "red_1x1" in name:
        __UpperCamelCase	:List[Any]         =						name.replace('''red_1x1'''    ,     '''reduce_1x1'''      )
    if ".local_rep.conv_3x3." in name:
        __UpperCamelCase	:int         =						name.replace('''.local_rep.conv_3x3.'''    ,     '''.conv_kxk.'''      )
    if ".local_rep.conv_1x1." in name:
        __UpperCamelCase	:Optional[int]         =						name.replace('''.local_rep.conv_1x1.'''    ,     '''.conv_1x1.'''      )
    if ".norm." in name:
        __UpperCamelCase	:Tuple         =						name.replace('''.norm.'''    ,     '''.normalization.'''      )
    if ".conv." in name:
        __UpperCamelCase	:List[Any]         =						name.replace('''.conv.'''    ,     '''.convolution.'''      )
    if ".conv_proj." in name:
        __UpperCamelCase	:str         =						name.replace('''.conv_proj.'''    ,     '''.conv_projection.'''      )

    for i in range(0    ,     2      ):
        for j in range(0    ,     4      ):
            if f""".{i}.{j}.""" in name:
                __UpperCamelCase	:Optional[Any]         =						name.replace(f""".{i}.{j}."""    ,     f""".{i}.layer.{j}."""      )

    for i in range(2    ,     6      ):
        for j in range(0    ,     4      ):
            if f""".{i}.{j}.""" in name:
                __UpperCamelCase	:int         =						name.replace(f""".{i}.{j}."""    ,     f""".{i}."""      )
                if "expand_1x1" in name:
                    __UpperCamelCase	:Union[str, Any]         =						name.replace('''expand_1x1'''    ,     '''downsampling_layer.expand_1x1'''      )
                if "conv_3x3" in name:
                    __UpperCamelCase	:Optional[Any]         =						name.replace('''conv_3x3'''    ,     '''downsampling_layer.conv_3x3'''      )
                if "reduce_1x1" in name:
                    __UpperCamelCase	:Optional[Any]         =						name.replace('''reduce_1x1'''    ,     '''downsampling_layer.reduce_1x1'''      )

    for i in range(2    ,     5      ):
        if f""".global_rep.{i}.weight""" in name:
            __UpperCamelCase	:Tuple         =						name.replace(f""".global_rep.{i}.weight"""    ,     '''.layernorm.weight'''      )
        if f""".global_rep.{i}.bias""" in name:
            __UpperCamelCase	:Tuple         =						name.replace(f""".global_rep.{i}.bias"""    ,     '''.layernorm.bias'''      )

    if ".global_rep." in name:
        __UpperCamelCase	:Union[str, Any]         =						name.replace('''.global_rep.'''    ,     '''.transformer.'''      )
    if ".pre_norm_mha.0." in name:
        __UpperCamelCase	:Union[str, Any]         =						name.replace('''.pre_norm_mha.0.'''    ,     '''.layernorm_before.'''      )
    if ".pre_norm_mha.1.out_proj." in name:
        __UpperCamelCase	:Union[str, Any]         =						name.replace('''.pre_norm_mha.1.out_proj.'''    ,     '''.attention.output.dense.'''      )
    if ".pre_norm_ffn.0." in name:
        __UpperCamelCase	:Tuple         =						name.replace('''.pre_norm_ffn.0.'''    ,     '''.layernorm_after.'''      )
    if ".pre_norm_ffn.1." in name:
        __UpperCamelCase	:List[str]         =						name.replace('''.pre_norm_ffn.1.'''    ,     '''.intermediate.dense.'''      )
    if ".pre_norm_ffn.4." in name:
        __UpperCamelCase	:List[str]         =						name.replace('''.pre_norm_ffn.4.'''    ,     '''.output.dense.'''      )
    if ".transformer." in name:
        __UpperCamelCase	:Dict         =						name.replace('''.transformer.'''    ,     '''.transformer.layer.'''      )

    if ".aspp_layer." in name:
        __UpperCamelCase	:int         =						name.replace('''.aspp_layer.'''    ,     '''.'''      )
    if ".aspp_pool." in name:
        __UpperCamelCase	:Optional[Any]         =						name.replace('''.aspp_pool.'''    ,     '''.'''      )
    if "seg_head." in name:
        __UpperCamelCase	:str         =						name.replace('''seg_head.'''    ,     '''segmentation_head.'''      )
    if "segmentation_head.classifier.classifier." in name:
        __UpperCamelCase	:List[Any]         =						name.replace('''segmentation_head.classifier.classifier.'''    ,     '''segmentation_head.classifier.'''      )

    if "classifier.fc." in name:
        __UpperCamelCase	:Optional[int]         =						name.replace('''classifier.fc.'''    ,     '''classifier.'''      )
    elif (not base_model) and ("segmentation_head." not in name):
        __UpperCamelCase	:Dict         =						'''mobilevit.''' + name

    return name



def lowerCamelCase    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE=False      ):
    '''simple docstring'''







    if base_model:
        __UpperCamelCase	:Tuple         =						''''''
    else:
        __UpperCamelCase	:List[Any]         =						'''mobilevit.'''

    for key in orig_state_dict.copy().keys():
        __UpperCamelCase	:int         =						orig_state_dict.pop(UpperCamelCase__      )

        if key[:8] == "encoder.":
            __UpperCamelCase	:Optional[int]         =						key[8:]

        if "qkv" in key:
            __UpperCamelCase	:Dict         =						key.split('''.'''      )
            __UpperCamelCase	:List[Any]         =						int(key_split[0][6:]      ) - 1
            __UpperCamelCase	:Optional[int]         =						int(key_split[3]      )
            __UpperCamelCase	:Dict         =						model.get_submodule(f"""{model_prefix}encoder.layer.{layer_num}"""      )
            __UpperCamelCase	:str         =						layer.transformer.layer[transformer_num].attention.attention.all_head_size
            __UpperCamelCase	:Optional[int]         =						(
                f"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
            )
            if "weight" in key:
                __UpperCamelCase	:int         =						val[:dim, :]
                __UpperCamelCase	:Union[str, Any]         =						val[dim : dim * 2, :]
                __UpperCamelCase	:List[str]         =						val[-dim:, :]
            else:
                __UpperCamelCase	:List[Any]         =						val[:dim]
                __UpperCamelCase	:Optional[Any]         =						val[dim : dim * 2]
                __UpperCamelCase	:int         =						val[-dim:]
        else:
            __UpperCamelCase	:int         =						val

    return orig_state_dict



def lowerCamelCase    (  ):
    '''simple docstring'''







    __UpperCamelCase	:Union[str, Any]         =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __UpperCamelCase	:Optional[int]         =						Image.open(requests.get(UpperCamelCase__    ,     stream=UpperCamelCase__      ).raw      )
    return im



@torch.no_grad()
def lowerCamelCase    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE=False      ):
    '''simple docstring'''







    __UpperCamelCase	:Optional[Any]         =						get_mobilevit_config(UpperCamelCase__      )

    # load original state_dict
    __UpperCamelCase	:Optional[Any]         =						torch.load(UpperCamelCase__    ,     map_location='''cpu'''      )

    # load 🤗 model
    if mobilevit_name.startswith('''deeplabv3_'''      ):
        __UpperCamelCase	:int         =						MobileViTForSemanticSegmentation(UpperCamelCase__      ).eval()
    else:
        __UpperCamelCase	:Tuple         =						MobileViTForImageClassification(UpperCamelCase__      ).eval()

    __UpperCamelCase	:Tuple         =						convert_state_dict(UpperCamelCase__    ,     UpperCamelCase__      )
    model.load_state_dict(UpperCamelCase__      )

    # Check outputs on an image, prepared by MobileViTImageProcessor
    __UpperCamelCase	:Union[str, Any]         =						MobileViTImageProcessor(crop_size=config.image_size    ,     size=config.image_size + 32      )
    __UpperCamelCase	:Optional[Any]         =						image_processor(images=prepare_img()    ,     return_tensors='''pt'''      )
    __UpperCamelCase	:Any         =						model(**UpperCamelCase__      )
    __UpperCamelCase	:str         =						outputs.logits

    if mobilevit_name.startswith('''deeplabv3_'''      ):
        assert logits.shape == (1, 21, 32, 32)

        if mobilevit_name == "deeplabv3_mobilevit_s":
            __UpperCamelCase	:List[Any]         =						torch.tensor(
                [
                    [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
                    [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
                    [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
                ]      )
        elif mobilevit_name == "deeplabv3_mobilevit_xs":
            __UpperCamelCase	:Optional[Any]         =						torch.tensor(
                [
                    [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
                    [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
                    [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
                ]      )
        elif mobilevit_name == "deeplabv3_mobilevit_xxs":
            __UpperCamelCase	:List[str]         =						torch.tensor(
                [
                    [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
                    [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
                    [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
                ]      )
        else:
            raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}"""      )

        assert torch.allclose(logits[0, :3, :3, :3]    ,     UpperCamelCase__    ,     atol=1e-4      )
    else:
        assert logits.shape == (1, 1_000)

        if mobilevit_name == "mobilevit_s":
            __UpperCamelCase	:str         =						torch.tensor([-0.9_866, 0.2_392, -1.1_241]      )
        elif mobilevit_name == "mobilevit_xs":
            __UpperCamelCase	:Optional[int]         =						torch.tensor([-2.4_761, -0.9_399, -1.9_587]      )
        elif mobilevit_name == "mobilevit_xxs":
            __UpperCamelCase	:Union[str, Any]         =						torch.tensor([-1.9_364, -1.2_327, -0.4_653]      )
        else:
            raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}"""      )

        assert torch.allclose(logits[0, :3]    ,     UpperCamelCase__    ,     atol=1e-4      )

    Path(UpperCamelCase__      ).mkdir(exist_ok=UpperCamelCase__      )
    print(f"""Saving model {mobilevit_name} 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 push_to_hub:
        __UpperCamelCase	:Any         =						{
            '''mobilevit_s''': '''mobilevit-small''',
            '''mobilevit_xs''': '''mobilevit-x-small''',
            '''mobilevit_xxs''': '''mobilevit-xx-small''',
            '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
            '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
            '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
        }

        print('''Pushing to the hub...'''      )
        __UpperCamelCase	:Dict         =						model_mapping[mobilevit_name]
        image_processor.push_to_hub(UpperCamelCase__    ,     organization='''apple'''      )
        model.push_to_hub(UpperCamelCase__    ,     organization='''apple'''      )


if __name__ == "__main__":
    __lowercase									=   argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--mobilevit_name''',
        default='''mobilevit_s''',
        type=str,
        help=(
            '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
            ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
        ),
    )
    parser.add_argument(
        '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
    )
    parser.add_argument(
        '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
    )

    __lowercase									=   parser.parse_args()
    convert_movilevit_checkpoint(
        args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )












import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[Any]           =   [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(UpperCamelCase__       ,			UpperCamelCase__					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   str					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				str           =   emb.weight.shape
						__SCREAMING_SNAKE_CASE:				List[str]           =   nn.Linear(UpperCamelCase__       ,			UpperCamelCase__       ,			bias=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Tuple           =   emb.weight.data
						return lin_layer






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   torch.load(UpperCamelCase__       ,			map_location='''cpu'''					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   Namespace(**checkpoint['''cfg''']['''model''']					)
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   checkpoint['''model''']
						remove_ignore_keys_(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				int           =   state_dict['''decoder.embed_tokens.weight'''].shape[0]

						__SCREAMING_SNAKE_CASE:				List[Any]           =   {key.replace('''decoder'''       ,			'''model'''					): val for key, val in state_dict.items()}

						__SCREAMING_SNAKE_CASE:				Dict           =   XGLMConfig(
						    vocab_size=UpperCamelCase__       ,			max_position_embeddings=args.max_target_positions       ,			num_layers=args.decoder_layers       ,			attention_heads=args.decoder_attention_heads       ,			ffn_dim=args.decoder_ffn_embed_dim       ,			d_model=args.decoder_embed_dim       ,			layerdrop=args.decoder_layerdrop       ,			dropout=args.dropout       ,			attention_dropout=args.attention_dropout       ,			activation_dropout=args.activation_dropout       ,			activation_function='''gelu'''       ,			scale_embedding=not args.no_scale_embedding       ,			tie_word_embeddings=args.share_decoder_input_output_embed       ,			)

						__SCREAMING_SNAKE_CASE:				int           =   XGLMForCausalLM(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   model.load_state_dict(UpperCamelCase__       ,			strict=UpperCamelCase__					)
						print(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				List[str]           =   make_linear_from_emb(model.model.embed_tokens					)

						return model


if __name__ == "__main__":
							lowerCAmelCase							:       Union[str, Any]					 =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
							parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							lowerCAmelCase							:       List[str]					 =	parser.parse_args()
							lowerCAmelCase							:       Optional[int]					 =	convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
							model.save_pretrained(args.pytorch_dump_folder_path)



