




import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


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







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Tuple  ):
					print("Loading config file..."  )

					def flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_							:       str   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]=""   ,   SCREAMING_SNAKE_CASE_							:       Dict="."  ):
										__lowerCAmelCase            = []
										for k, v in d.items():
															__lowerCAmelCase            = parent_key + sep + k if parent_key else k
															if isinstance(SCREAMING_SNAKE_CASE_   ,   collections.abc.MutableMapping  ):
																				items.extend(flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   sep=SCREAMING_SNAKE_CASE_  ).items()  )
															else:
																				items.append((new_key, v)  )
										return dict(SCREAMING_SNAKE_CASE_  )

					__lowerCAmelCase            = argparse.Namespace()
					with open(SCREAMING_SNAKE_CASE_   ,   "r"  ) as yaml_file:
										try:
															__lowerCAmelCase            = yaml.load(SCREAMING_SNAKE_CASE_   ,   Loader=yaml.FullLoader  )

															__lowerCAmelCase            = flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_  )
															for k, v in flat_cfg.items():
																				setattr(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )
										except yaml.YAMLError as exc:
															logger.error("Error while loading config file: {}. Error message: {}".format(SCREAMING_SNAKE_CASE_   ,   str(SCREAMING_SNAKE_CASE_  )  )  )
					return config







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Optional[Any]   ,   SCREAMING_SNAKE_CASE_							:       str  ):
					__lowerCAmelCase            = MobileViTVaConfig()

					__lowerCAmelCase            = False

					# dataset
					if task_name.startswith("imagenet1k_"  ):
										__lowerCAmelCase            = 10_00
										if int(task_name.strip().split("_"  )[-1]  ) == 3_84:
															__lowerCAmelCase            = 3_84
										else:
															__lowerCAmelCase            = 2_56
										__lowerCAmelCase            = "imagenet-1k-id2label.json"
					elif task_name.startswith("imagenet21k_to_1k_"  ):
										__lowerCAmelCase            = 2_10_00
										if int(task_name.strip().split("_"  )[-1]  ) == 3_84:
															__lowerCAmelCase            = 3_84
										else:
															__lowerCAmelCase            = 2_56
										__lowerCAmelCase            = "imagenet-22k-id2label.json"
					elif task_name.startswith("ade20k_"  ):
										__lowerCAmelCase            = 1_51
										__lowerCAmelCase            = 5_12
										__lowerCAmelCase            = "ade20k-id2label.json"
										__lowerCAmelCase            = True
					elif task_name.startswith("voc_"  ):
										__lowerCAmelCase            = 21
										__lowerCAmelCase            = 5_12
										__lowerCAmelCase            = "pascal-voc-id2label.json"
										__lowerCAmelCase            = True

					# orig_config
					__lowerCAmelCase            = load_orig_config_file(SCREAMING_SNAKE_CASE_  )
					assert getattr(SCREAMING_SNAKE_CASE_   ,   "model.classification.name"   ,   -1  ) == "mobilevit_v2", "Invalid model"
					__lowerCAmelCase            = getattr(SCREAMING_SNAKE_CASE_   ,   "model.classification.mitv2.width_multiplier"   ,   1.0  )
					assert (
					    getattr(SCREAMING_SNAKE_CASE_   ,   "model.classification.mitv2.attn_norm_layer"   ,   -1  ) == "layer_norm_2d"
					), "Norm layers other than layer_norm_2d is not supported"
					__lowerCAmelCase            = getattr(SCREAMING_SNAKE_CASE_   ,   "model.classification.activation.name"   ,   "swish"  )
					# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

					if is_segmentation_model:
										__lowerCAmelCase            = getattr(SCREAMING_SNAKE_CASE_   ,   "model.segmentation.output_stride"   ,   16  )
										if "_deeplabv3" in task_name:
															__lowerCAmelCase            = getattr(SCREAMING_SNAKE_CASE_   ,   "model.segmentation.deeplabv3.aspp_rates"   ,   [12, 24, 36]  )
															__lowerCAmelCase            = getattr(SCREAMING_SNAKE_CASE_   ,   "model.segmentation.deeplabv3.aspp_out_channels"   ,   5_12  )
															__lowerCAmelCase            = getattr(SCREAMING_SNAKE_CASE_   ,   "model.segmentation.deeplabv3.aspp_dropout"   ,   0.1  )

    # id2label
					__lowerCAmelCase            = "huggingface/label-files"
					__lowerCAmelCase            = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   repo_type="dataset"  )   ,   "r"  )  )
					__lowerCAmelCase            = {int(SCREAMING_SNAKE_CASE_  ): v for k, v in idalabel.items()}
					__lowerCAmelCase            = idalabel
					__lowerCAmelCase            = {v: k for k, v in idalabel.items()}

					return config







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Dict   ,   SCREAMING_SNAKE_CASE_							:       List[str]   ,   SCREAMING_SNAKE_CASE_							:       Any  ):
					__lowerCAmelCase            = dct.pop(SCREAMING_SNAKE_CASE_  )
					__lowerCAmelCase            = val







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Union[str, Any]   ,   SCREAMING_SNAKE_CASE_							:       Dict=False  ):
					if base_model:
										__lowerCAmelCase            = ""
					else:
										__lowerCAmelCase            = "mobilevitv2."

					__lowerCAmelCase            = []
					for k in state_dict.keys():
										if k[:8] == "encoder.":
															__lowerCAmelCase            = k[8:]
										else:
															__lowerCAmelCase            = k

										if ".block." in k:
															__lowerCAmelCase            = k_new.replace(".block."   ,   "."  )
										if ".conv." in k:
															__lowerCAmelCase            = k_new.replace(".conv."   ,   ".convolution."  )
										if ".norm." in k:
															__lowerCAmelCase            = k_new.replace(".norm."   ,   ".normalization."  )

										if "conv_1." in k:
															__lowerCAmelCase            = k_new.replace("conv_1."   ,   F"""{model_prefix}conv_stem."""  )
										for i in [1, 2]:
															if F"""layer_{i}.""" in k:
																				__lowerCAmelCase            = k_new.replace(F"""layer_{i}."""   ,   F"""{model_prefix}encoder.layer.{i-1}.layer."""  )
										if ".exp_1x1." in k:
															__lowerCAmelCase            = k_new.replace(".exp_1x1."   ,   ".expand_1x1."  )
										if ".red_1x1." in k:
															__lowerCAmelCase            = k_new.replace(".red_1x1."   ,   ".reduce_1x1."  )

										for i in [3, 4, 5]:
															if F"""layer_{i}.0.""" in k:
																				__lowerCAmelCase            = k_new.replace(F"""layer_{i}.0."""   ,   F"""{model_prefix}encoder.layer.{i-1}.downsampling_layer."""  )
															if F"""layer_{i}.1.local_rep.0.""" in k:
																				__lowerCAmelCase            = k_new.replace(F"""layer_{i}.1.local_rep.0."""   ,   F"""{model_prefix}encoder.layer.{i-1}.conv_kxk."""  )
															if F"""layer_{i}.1.local_rep.1.""" in k:
																				__lowerCAmelCase            = k_new.replace(F"""layer_{i}.1.local_rep.1."""   ,   F"""{model_prefix}encoder.layer.{i-1}.conv_1x1."""  )

										for i in [3, 4, 5]:
															if i == 3:
																				__lowerCAmelCase            = [0, 1]
															elif i == 4:
																				__lowerCAmelCase            = [0, 1, 2, 3]
															elif i == 5:
																				__lowerCAmelCase            = [0, 1, 2]

															for j in j_in:
																				if F"""layer_{i}.1.global_rep.{j}.""" in k:
																									__lowerCAmelCase            = k_new.replace(
																									    F"""layer_{i}.1.global_rep.{j}."""   ,   F"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."""  )
															if F"""layer_{i}.1.global_rep.{j+1}.""" in k:
																				__lowerCAmelCase            = k_new.replace(
																				    F"""layer_{i}.1.global_rep.{j+1}."""   ,   F"""{model_prefix}encoder.layer.{i-1}.layernorm."""  )

															if F"""layer_{i}.1.conv_proj.""" in k:
																				__lowerCAmelCase            = k_new.replace(F"""layer_{i}.1.conv_proj."""   ,   F"""{model_prefix}encoder.layer.{i-1}.conv_projection."""  )

										if "pre_norm_attn.0." in k:
															__lowerCAmelCase            = k_new.replace("pre_norm_attn.0."   ,   "layernorm_before."  )
										if "pre_norm_attn.1." in k:
															__lowerCAmelCase            = k_new.replace("pre_norm_attn.1."   ,   "attention."  )
										if "pre_norm_ffn.0." in k:
															__lowerCAmelCase            = k_new.replace("pre_norm_ffn.0."   ,   "layernorm_after."  )
										if "pre_norm_ffn.1." in k:
															__lowerCAmelCase            = k_new.replace("pre_norm_ffn.1."   ,   "ffn.conv1."  )
										if "pre_norm_ffn.3." in k:
															__lowerCAmelCase            = k_new.replace("pre_norm_ffn.3."   ,   "ffn.conv2."  )

										if "classifier.1." in k:
															__lowerCAmelCase            = k_new.replace("classifier.1."   ,   "classifier."  )

										if "seg_head." in k:
															__lowerCAmelCase            = k_new.replace("seg_head."   ,   "segmentation_head."  )
										if ".aspp_layer." in k:
															__lowerCAmelCase            = k_new.replace(".aspp_layer."   ,   "."  )
										if ".aspp_pool." in k:
															__lowerCAmelCase            = k_new.replace(".aspp_pool."   ,   "."  )

										rename_keys.append((k, k_new)  )
					return rename_keys







def 			_a   (		SCREAMING_SNAKE_CASE_							:       List[Any]  ):
					__lowerCAmelCase            = []
					for k in state_dict.keys():
										if k.startswith("seg_head.aux_head."  ):
															keys_to_ignore.append(SCREAMING_SNAKE_CASE_  )
					for k in keys_to_ignore:
										state_dict.pop(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )







def 			_a   (		):
					__lowerCAmelCase            = "http://images.cocodataset.org/val2017/000000039769.jpg"
					# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
					__lowerCAmelCase            = Image.open(requests.get(SCREAMING_SNAKE_CASE_   ,   stream=SCREAMING_SNAKE_CASE_  ).raw  )
					return im







@torch.no_grad()
def 			_a   (		SCREAMING_SNAKE_CASE_							:       List[str]   ,   SCREAMING_SNAKE_CASE_							:       Dict   ,   SCREAMING_SNAKE_CASE_							:       Tuple   ,   SCREAMING_SNAKE_CASE_							:       List[Any]  ):
					__lowerCAmelCase            = get_mobilevitva_config(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )

					# load original state_dict
					__lowerCAmelCase            = torch.load(SCREAMING_SNAKE_CASE_   ,   map_location="cpu"  )

					# load huggingface model
					if task_name.startswith("ade20k_"  ) or task_name.startswith("voc_"  ):
										__lowerCAmelCase            = MobileViTVaForSemanticSegmentation(SCREAMING_SNAKE_CASE_  ).eval()
										__lowerCAmelCase            = False
					else:
										__lowerCAmelCase            = MobileViTVaForImageClassification(SCREAMING_SNAKE_CASE_  ).eval()
										__lowerCAmelCase            = False

					# remove and rename some keys of load the original model
					__lowerCAmelCase            = checkpoint
					remove_unused_keys(SCREAMING_SNAKE_CASE_  )
					__lowerCAmelCase            = create_rename_keys(SCREAMING_SNAKE_CASE_   ,   base_model=SCREAMING_SNAKE_CASE_  )
					for rename_key_src, rename_key_dest in rename_keys:
										rename_key(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )

					# load modified state_dict
					model.load_state_dict(SCREAMING_SNAKE_CASE_  )

					# Check outputs on an image, prepared by MobileViTImageProcessor
					__lowerCAmelCase            = MobileViTImageProcessor(crop_size=config.image_size   ,   size=config.image_size + 32  )
					__lowerCAmelCase            = image_processor(images=prepare_img()   ,   return_tensors="pt"  )
					__lowerCAmelCase            = model(**SCREAMING_SNAKE_CASE_  )

					# verify classification model
					if task_name.startswith("imagenet"  ):
										__lowerCAmelCase            = outputs.logits
										__lowerCAmelCase            = logits.argmax(-1  ).item()
										print("Predicted class:"   ,   model.config.idalabel[predicted_class_idx]  )
										if task_name.startswith("imagenet1k_256"  ) and config.width_multiplier == 1.0:
															# expected_logits for base variant
															__lowerCAmelCase            = torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01]  )
															assert torch.allclose(logits[0, :3]   ,   SCREAMING_SNAKE_CASE_   ,   atol=1E-4  )

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


if __name__ == "__main__":
					UpperCamelCase__					    =    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--task""",
					    default="""imagenet1k_256""",
					    type=str,
					    help=(
					        """Name of the task for which the MobileViTV2 model you'd like to convert is trained on . """
					        """
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            """
					    ),
					    choices=[
					        """imagenet1k_256""",
					        """imagenet1k_384""",
					        """imagenet21k_to_1k_256""",
					        """imagenet21k_to_1k_384""",
					        """ade20k_deeplabv3""",
					        """voc_deeplabv3""",
					    ],
					)

					parser.add_argument(
					    """--orig_checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
					)
					parser.add_argument("""--orig_config_path""", required=True, type=str, help="""Path to the original config file.""")
					parser.add_argument(
					    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
					)

					UpperCamelCase__					    =    parser.parse_args()
					convert_mobilevitva_checkpoint(
					    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
					)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__					    =    logging.get_logger(__name__)

UpperCamelCase__					    =    {
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
class     a__					(      snake_case__   ):
						_a							:					Tuple      =					"""nllb-moe"""
						_a							:					Dict      =					["""past_key_values"""]
						_a							:					Optional[int]      =					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






						def __init__(     self	,    _A=1_2_8_1_1_2	,    _A=1_0_2_4	,    _A=1_2	,    _A=4_0_9_6	,    _A=1_6	,    _A=1_2	,    _A=4_0_9_6	,    _A=1_6	,    _A=0.05	,    _A=0.05	,    _A=True	,    _A=True	,    _A="relu"	,    _A=1_0_2_4	,    _A=0.1	,    _A=0.1	,    _A=0.0	,    _A=0.02	,    _A=2	,    _A=True	,    _A=False	,    _A="float32"	,    _A=False	,    _A=1_2_8	,    _A=6_4	,    _A=4	,    _A=4	,    _A=0.0_01	,    _A=0.0_01	,    _A="all"	,    _A=False	,    _A=False	,    _A=1.0	,    _A=0.2	,    _A=1	,    _A=0	,    _A=2	,    _A=False	,    **_A	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = vocab_size
											__lowerCAmelCase            = max_position_embeddings
											__lowerCAmelCase            = d_model
											__lowerCAmelCase            = encoder_ffn_dim
											__lowerCAmelCase            = encoder_layers
											__lowerCAmelCase            = encoder_attention_heads
											__lowerCAmelCase            = decoder_ffn_dim
											__lowerCAmelCase            = decoder_layers
											__lowerCAmelCase            = decoder_attention_heads
											__lowerCAmelCase            = dropout
											__lowerCAmelCase            = attention_dropout
											__lowerCAmelCase            = activation_dropout
											__lowerCAmelCase            = activation_function
											__lowerCAmelCase            = init_std
											__lowerCAmelCase            = encoder_layerdrop
											__lowerCAmelCase            = decoder_layerdrop
											__lowerCAmelCase            = use_cache
											__lowerCAmelCase            = encoder_layers
											__lowerCAmelCase            = scale_embedding  # scale factor will be sqrt(d_model) if True
											__lowerCAmelCase            = router_z_loss_coef
											__lowerCAmelCase            = router_aux_loss_coef
											__lowerCAmelCase            = decoder_sparse_step
											__lowerCAmelCase            = encoder_sparse_step
											__lowerCAmelCase            = num_experts
											__lowerCAmelCase            = expert_capacity
											__lowerCAmelCase            = router_bias
											if router_dtype not in ["float32", "float16", "bfloat16"]:
																raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""    )
											__lowerCAmelCase            = router_dtype

											__lowerCAmelCase            = router_ignore_padding_tokens
											__lowerCAmelCase            = batch_prioritized_routing
											__lowerCAmelCase            = second_expert_policy
											__lowerCAmelCase            = normalize_router_prob_before_dropping
											__lowerCAmelCase            = moe_eval_capacity_token_fraction
											__lowerCAmelCase            = moe_token_dropout
											__lowerCAmelCase            = output_router_logits
											super().__init__(
											    pad_token_id=_A	,    bos_token_id=_A	,    eos_token_id=_A	,    is_encoder_decoder=_A	,    decoder_start_token_id=_A	,    **_A	,    )




