






"""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()
lowercase    			=       logging.get_logger(__name__)

def _lowerCAmelCase	(  __lowerCamelCase:Union[str, Any]   ):

						'''simple docstring'''






						__magic_name__            =     MobileViTConfig()

						# size of the architecture
						if "mobilevit_s" in mobilevit_name:
												__magic_name__            =     [1_4_4, 1_9_2, 2_4_0]
												__magic_name__            =     [1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
						elif "mobilevit_xs" in mobilevit_name:
												__magic_name__            =     [9_6, 1_2_0, 1_4_4]
												__magic_name__            =     [1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
						elif "mobilevit_xxs" in mobilevit_name:
												__magic_name__            =     [6_4, 8_0, 9_6]
												__magic_name__            =     [1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
												__magic_name__            =     0.0_5
												__magic_name__            =     2.0

						if mobilevit_name.startswith("deeplabv3_"   ):
												__magic_name__            =     5_1_2
												__magic_name__            =     1_6
												__magic_name__            =     2_1
												__magic_name__            =     "pascal-voc-id2label.json"
						else:
												__magic_name__            =     1_0_0_0
												__magic_name__            =     "imagenet-1k-id2label.json"

						__magic_name__            =     "huggingface/label-files"
						__magic_name__            =     json.load(open(hf_hub_download(_lowercase ,						_lowercase ,						repo_type="dataset"   ) ,						"r"   )   )
						__magic_name__            =     {int(_lowercase   ): v for k, v in idalabel.items()}
						__magic_name__            =     idalabel
						__magic_name__            =     {v: k for k, v in idalabel.items()}

						return config

def _lowerCAmelCase	(  __lowerCamelCase:Optional[Any] ,						__lowerCamelCase:str=False   ):

						'''simple docstring'''






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

						if "conv_1." in name:
												__magic_name__            =     name.replace("conv_1." ,						"conv_stem."   )
						if ".block." in name:
												__magic_name__            =     name.replace(".block." ,						"."   )

						if "exp_1x1" in name:
												__magic_name__            =     name.replace("exp_1x1" ,						"expand_1x1"   )
						if "red_1x1" in name:
												__magic_name__            =     name.replace("red_1x1" ,						"reduce_1x1"   )
						if ".local_rep.conv_3x3." in name:
												__magic_name__            =     name.replace(".local_rep.conv_3x3." ,						".conv_kxk."   )
						if ".local_rep.conv_1x1." in name:
												__magic_name__            =     name.replace(".local_rep.conv_1x1." ,						".conv_1x1."   )
						if ".norm." in name:
												__magic_name__            =     name.replace(".norm." ,						".normalization."   )
						if ".conv." in name:
												__magic_name__            =     name.replace(".conv." ,						".convolution."   )
						if ".conv_proj." in name:
												__magic_name__            =     name.replace(".conv_proj." ,						".conv_projection."   )

						for i in range(0 ,						2   ):
												for j in range(0 ,						4   ):
																		if f'''.{i}.{j}.''' in name:
																								__magic_name__            =     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:
																								__magic_name__            =     name.replace(f'''.{i}.{j}.''' ,						f'''.{i}.'''   )
																								if "expand_1x1" in name:
																														__magic_name__            =     name.replace("expand_1x1" ,						"downsampling_layer.expand_1x1"   )
																								if "conv_3x3" in name:
																														__magic_name__            =     name.replace("conv_3x3" ,						"downsampling_layer.conv_3x3"   )
																								if "reduce_1x1" in name:
																														__magic_name__            =     name.replace("reduce_1x1" ,						"downsampling_layer.reduce_1x1"   )

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

						if ".global_rep." in name:
												__magic_name__            =     name.replace(".global_rep." ,						".transformer."   )
						if ".pre_norm_mha.0." in name:
												__magic_name__            =     name.replace(".pre_norm_mha.0." ,						".layernorm_before."   )
						if ".pre_norm_mha.1.out_proj." in name:
												__magic_name__            =     name.replace(".pre_norm_mha.1.out_proj." ,						".attention.output.dense."   )
						if ".pre_norm_ffn.0." in name:
												__magic_name__            =     name.replace(".pre_norm_ffn.0." ,						".layernorm_after."   )
						if ".pre_norm_ffn.1." in name:
												__magic_name__            =     name.replace(".pre_norm_ffn.1." ,						".intermediate.dense."   )
						if ".pre_norm_ffn.4." in name:
												__magic_name__            =     name.replace(".pre_norm_ffn.4." ,						".output.dense."   )
						if ".transformer." in name:
												__magic_name__            =     name.replace(".transformer." ,						".transformer.layer."   )

						if ".aspp_layer." in name:
												__magic_name__            =     name.replace(".aspp_layer." ,						"."   )
						if ".aspp_pool." in name:
												__magic_name__            =     name.replace(".aspp_pool." ,						"."   )
						if "seg_head." in name:
												__magic_name__            =     name.replace("seg_head." ,						"segmentation_head."   )
						if "segmentation_head.classifier.classifier." in name:
												__magic_name__            =     name.replace("segmentation_head.classifier.classifier." ,						"segmentation_head.classifier."   )

						if "classifier.fc." in name:
												__magic_name__            =     name.replace("classifier.fc." ,						"classifier."   )
						elif (not base_model) and ("segmentation_head." not in name):
												__magic_name__            =     "mobilevit." + name

						return name

def _lowerCAmelCase	(  __lowerCamelCase:List[Any] ,						__lowerCamelCase:Optional[int] ,						__lowerCamelCase:Tuple=False   ):

						'''simple docstring'''






						if base_model:
												__magic_name__            =     ""
						else:
												__magic_name__            =     "mobilevit."

						for key in orig_state_dict.copy().keys():
												__magic_name__            =     orig_state_dict.pop(_lowercase   )

												if key[:8] == "encoder.":
																		__magic_name__            =     key[8:]

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

						return orig_state_dict

def _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     "http://images.cocodataset.org/val2017/000000039769.jpg"
						__magic_name__            =     Image.open(requests.get(_lowercase ,						stream=_lowercase   ).raw   )
						return im

@torch.no_grad()
def _lowerCAmelCase	(  __lowerCamelCase:str ,						__lowerCamelCase:Dict ,						__lowerCamelCase:Dict ,						__lowerCamelCase:Union[str, Any]=False   ):

						'''simple docstring'''






						__magic_name__            =     get_mobilevit_config(_lowercase   )

						# load original state_dict
						__magic_name__            =     torch.load(_lowercase ,						map_location="cpu"   )

						# load 🤗 model
						if mobilevit_name.startswith("deeplabv3_"   ):
												__magic_name__            =     MobileViTForSemanticSegmentation(_lowercase   ).eval()
						else:
												__magic_name__            =     MobileViTForImageClassification(_lowercase   ).eval()

						__magic_name__            =     convert_state_dict(_lowercase ,						_lowercase   )
						model.load_state_dict(_lowercase   )

						# Check outputs on an image, prepared by MobileViTImageProcessor
						__magic_name__            =     MobileViTImageProcessor(crop_size=config.image_size ,						size=config.image_size + 3_2   )
						__magic_name__            =     image_processor(images=prepare_img() ,						return_tensors="pt"   )
						__magic_name__            =     model(**_lowercase   )
						__magic_name__            =     outputs.logits

						if mobilevit_name.startswith("deeplabv3_"   ):
												assert logits.shape == (1, 2_1, 3_2, 3_2)

												if mobilevit_name == "deeplabv3_mobilevit_s":
																		__magic_name__            =     torch.tensor(
																		    [
																		        [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
																		        [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
																		        [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
																		    ]   )
												elif mobilevit_name == "deeplabv3_mobilevit_xs":
																		__magic_name__            =     torch.tensor(
																		    [
																		        [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
																		        [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
																		        [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
																		    ]   )
												elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																		__magic_name__            =     torch.tensor(
																		    [
																		        [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
																		        [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
																		        [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
																		    ]   )
												else:
																		raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''   )

												assert torch.allclose(logits[0, :3, :3, :3] ,						_lowercase ,						atol=1E-4   )
						else:
												assert logits.shape == (1, 1_0_0_0)

												if mobilevit_name == "mobilevit_s":
																		__magic_name__            =     torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]   )
												elif mobilevit_name == "mobilevit_xs":
																		__magic_name__            =     torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]   )
												elif mobilevit_name == "mobilevit_xxs":
																		__magic_name__            =     torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]   )
												else:
																		raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''   )

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

						Path(_lowercase   ).mkdir(exist_ok=_lowercase   )
						print(f'''Saving model {mobilevit_name} to {pytorch_dump_folder_path}'''   )
						model.save_pretrained(_lowercase   )
						print(f'''Saving image processor to {pytorch_dump_folder_path}'''   )
						image_processor.save_pretrained(_lowercase   )

						if push_to_hub:
												__magic_name__            =     {
												    "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..."   )
												__magic_name__            =     model_mapping[mobilevit_name]
												image_processor.push_to_hub(_lowercase ,						organization="apple"   )
												model.push_to_hub(_lowercase ,						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
			)











"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase    			=       {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       ['''XLMRobertaTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       ['''XLMRobertaTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMRobertaForCausalLM''',
			    '''XLMRobertaForMaskedLM''',
			    '''XLMRobertaForMultipleChoice''',
			    '''XLMRobertaForQuestionAnswering''',
			    '''XLMRobertaForSequenceClassification''',
			    '''XLMRobertaForTokenClassification''',
			    '''XLMRobertaModel''',
			    '''XLMRobertaPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMRobertaForCausalLM''',
			    '''TFXLMRobertaForMaskedLM''',
			    '''TFXLMRobertaForMultipleChoice''',
			    '''TFXLMRobertaForQuestionAnswering''',
			    '''TFXLMRobertaForSequenceClassification''',
			    '''TFXLMRobertaForTokenClassification''',
			    '''TFXLMRobertaModel''',
			    '''TFXLMRobertaPreTrainedModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FlaxXLMRobertaForMaskedLM''',
			    '''FlaxXLMRobertaForCausalLM''',
			    '''FlaxXLMRobertaForMultipleChoice''',
			    '''FlaxXLMRobertaForQuestionAnswering''',
			    '''FlaxXLMRobertaForSequenceClassification''',
			    '''FlaxXLMRobertaForTokenClassification''',
			    '''FlaxXLMRobertaModel''',
			    '''FlaxXLMRobertaPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta import (
			    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaConfig,
			    XLMRobertaOnnxConfig,
			)

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta import XLMRobertaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta import (
						    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaForCausalLM,
						    XLMRobertaForMaskedLM,
						    XLMRobertaForMultipleChoice,
						    XLMRobertaForQuestionAnswering,
						    XLMRobertaForSequenceClassification,
						    XLMRobertaForTokenClassification,
						    XLMRobertaModel,
						    XLMRobertaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm_roberta import (
						    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMRobertaForCausalLM,
						    TFXLMRobertaForMaskedLM,
						    TFXLMRobertaForMultipleChoice,
						    TFXLMRobertaForQuestionAnswering,
						    TFXLMRobertaForSequenceClassification,
						    TFXLMRobertaForTokenClassification,
						    TFXLMRobertaModel,
						    TFXLMRobertaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xlm_roberta import (
						    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxXLMRobertaForCausalLM,
						    FlaxXLMRobertaForMaskedLM,
						    FlaxXLMRobertaForMultipleChoice,
						    FlaxXLMRobertaForQuestionAnswering,
						    FlaxXLMRobertaForSequenceClassification,
						    FlaxXLMRobertaForTokenClassification,
						    FlaxXLMRobertaModel,
						    FlaxXLMRobertaPreTrainedModel,
						)

else:
			import sys

			lowercase    			=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



