





"""simple docstring"""



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()
a_					=     logging.get_logger(__name__)







def 						__lowercase	(  snake_case_    :    Union[str, Any]		)  ->Any:



    '''simple docstring'''







    __A       :		Tuple							=	MobileViTConfig()

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

    if mobilevit_name.startswith('''deeplabv3_'''		):
        __A       :		int							=	512
        __A       :		List[str]							=	16
        __A       :		Dict							=	21
        __A       :		Dict							=	'''pascal-voc-id2label.json'''
    else:
        __A       :		Any							=	1000
        __A       :		List[str]							=	'''imagenet-1k-id2label.json'''

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

    return config







def 						__lowercase	(  snake_case_    :    List[str]							,snake_case_    :    Tuple=False		)  ->Dict:



    '''simple docstring'''







    for i in range(1							,6		):
        if F"""layer_{i}.""" in name:
            __A       :		List[Any]							=	name.replace(F"""layer_{i}."""							,F"""encoder.layer.{i - 1}."""		)

    if "conv_1." in name:
        __A       :		Union[str, Any]							=	name.replace('''conv_1.'''							,'''conv_stem.'''		)
    if ".block." in name:
        __A       :		Union[str, Any]							=	name.replace('''.block.'''							,'''.'''		)

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

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

    for i in range(2							,5		):
        if F""".global_rep.{i}.weight""" in name:
            __A       :		Optional[int]							=	name.replace(F""".global_rep.{i}.weight"""							,'''.layernorm.weight'''		)
        if F""".global_rep.{i}.bias""" in name:
            __A       :		str							=	name.replace(F""".global_rep.{i}.bias"""							,'''.layernorm.bias'''		)

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

    if ".aspp_layer." in name:
        __A       :		Dict							=	name.replace('''.aspp_layer.'''							,'''.'''		)
    if ".aspp_pool." in name:
        __A       :		Tuple							=	name.replace('''.aspp_pool.'''							,'''.'''		)
    if "seg_head." in name:
        __A       :		Optional[Any]							=	name.replace('''seg_head.'''							,'''segmentation_head.'''		)
    if "segmentation_head.classifier.classifier." in name:
        __A       :		int							=	name.replace('''segmentation_head.classifier.classifier.'''							,'''segmentation_head.classifier.'''		)

    if "classifier.fc." in name:
        __A       :		int							=	name.replace('''classifier.fc.'''							,'''classifier.'''		)
    elif (not base_model) and ("segmentation_head." not in name):
        __A       :		Union[str, Any]							=	'''mobilevit.''' + name

    return name







def 						__lowercase	(  snake_case_    :    Optional[Any]							,snake_case_    :    Tuple							,snake_case_    :    Optional[int]=False		)  ->List[Any]:



    '''simple docstring'''







    if base_model:
        __A       :		List[str]							=	''''''
    else:
        __A       :		Tuple							=	'''mobilevit.'''

    for key in orig_state_dict.copy().keys():
        __A       :		Any							=	orig_state_dict.pop(snake_case_		)

        if key[:8] == "encoder.":
            __A       :		List[Any]							=	key[8:]

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

    return orig_state_dict







def 						__lowercase	(  )  ->Optional[Any]:



    '''simple docstring'''







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







@torch.no_grad()
def 						__lowercase	(  snake_case_    :    int							,snake_case_    :    Dict							,snake_case_    :    Union[str, Any]							,snake_case_    :    Optional[Any]=False		)  ->List[Any]:



    '''simple docstring'''







    __A       :		Tuple							=	get_mobilevit_config(snake_case_		)

    # load original state_dict
    __A       :		Union[str, Any]							=	torch.load(snake_case_							,map_location='''cpu'''		)

    # load 🤗 model
    if mobilevit_name.startswith('''deeplabv3_'''		):
        __A       :		Tuple							=	MobileViTForSemanticSegmentation(snake_case_		).eval()
    else:
        __A       :		Optional[int]							=	MobileViTForImageClassification(snake_case_		).eval()

    __A       :		Dict							=	convert_state_dict(snake_case_							,snake_case_		)
    model.load_state_dict(snake_case_		)

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

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

        if mobilevit_name == "deeplabv3_mobilevit_s":
            __A       :		List[str]							=	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":
            __A       :		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":
            __A       :		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]							,snake_case_							,atol=1e-4		)
    else:
        assert logits.shape == (1, 1000)

        if mobilevit_name == "mobilevit_s":
            __A       :		Tuple							=	torch.tensor([-0.9_866, 0.2_392, -1.1_241]		)
        elif mobilevit_name == "mobilevit_xs":
            __A       :		Optional[Any]							=	torch.tensor([-2.4_761, -0.9_399, -1.9_587]		)
        elif mobilevit_name == "mobilevit_xxs":
            __A       :		Tuple							=	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]							,snake_case_							,atol=1e-4		)

    Path(snake_case_		).mkdir(exist_ok=snake_case_		)
    print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""		)
    model.save_pretrained(snake_case_		)
    print(F"""Saving image processor to {pytorch_dump_folder_path}"""		)
    image_processor.save_pretrained(snake_case_		)

    if push_to_hub:
        __A       :		Dict							=	{
            '''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...'''		)
        __A       :		str							=	model_mapping[mobilevit_name]
        image_processor.push_to_hub(snake_case_							,organization='''apple'''		)
        model.push_to_hub(snake_case_							,organization='''apple'''		)


if __name__ == "__main__":
       a_					=     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."""
       )

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











"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Any							=	AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''   ,		return_dict=__lowerCamelCase    ).to(__lowerCamelCase    )
     __A       :		Optional[Any]							=	AutoTokenizer.from_pretrained('''google/mt5-small'''    )

     __A       :		Optional[int]							=	tokenizer('''Hello there'''   ,		return_tensors='''pt'''    ).input_ids
     __A       :		str							=	tokenizer('''Hi I am'''   ,		return_tensors='''pt'''    ).input_ids

     __A       :		Any							=	model(input_ids.to(__lowerCamelCase    )   ,		labels=labels.to(__lowerCamelCase    )    ).loss
     __A       :		Tuple							=	-(labels.shape[-1] * loss.item())

     __A       :		int							=	-8_4.9_1_2_7
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE    ) < 1e-4    )




