'''simple docstring'''

import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Optional[Any],			_lowerCAmelCase						: Optional[int])						->		List[str]:



						"""simple docstring"""

						snake_case							,				snake_case							,				snake_case   :					Optional[int]							=						pt_attn_layer.in_proj_weight.chunk(3,			dim=0)
						snake_case							,				snake_case							,				snake_case   :					Union[str, Any]							=						pt_attn_layer.in_proj_bias.chunk(3,			dim=0)

						snake_case   :					str							=						pt_attn_layer.out_proj.weight
						snake_case   :					Optional[Any]							=						pt_attn_layer.out_proj.bias

						snake_case   :					Tuple							=						q_proj
						snake_case   :					Dict							=						q_proj_bias

						snake_case   :					Optional[int]							=						k_proj
						snake_case   :					Any							=						k_proj_bias

						snake_case   :					Any							=						v_proj
						snake_case   :					Any							=						v_proj_bias

						snake_case   :					Optional[Any]							=						out_proj_weights
						snake_case   :					Any							=						out_proj_bias


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Optional[int],			_lowerCAmelCase						: List[str])						->		List[Any]:



						"""simple docstring"""

						copy_linear(hf_mlp.fca,			pt_mlp.c_fc)
						copy_linear(hf_mlp.fca,			pt_mlp.c_proj)


def 					UpperCAmelCase__      (							_lowerCAmelCase						: str,			_lowerCAmelCase						: Union[str, Any])						->		Optional[int]:



						"""simple docstring"""

						snake_case   :					Tuple							=						pt_linear.weight
						snake_case   :					str							=						pt_linear.bias


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Union[str, Any],			_lowerCAmelCase						: List[Any])						->		str:



						"""simple docstring"""

						copy_linear(hf_layer.layer_norma,			pt_layer.ln_a)
						copy_linear(hf_layer.layer_norma,			pt_layer.ln_a)

						# copy MLP
						copy_mlp(hf_layer.mlp,			pt_layer.mlp)

						# copy attn
						copy_attn_layer(hf_layer.self_attn,			pt_layer.attn)


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Tuple,			_lowerCAmelCase						: Union[str, Any])						->		List[str]:



						"""simple docstring"""

						for hf_layer, pt_layer in zip(_lowerCAmelCase,			_lowerCAmelCase):
												copy_layer(_lowerCAmelCase,			_lowerCAmelCase)


def 					UpperCAmelCase__      (							_lowerCAmelCase						: List[str],			_lowerCAmelCase						: Optional[Any])						->		str:



						"""simple docstring"""

						snake_case   :					int							=						pt_model.token_embedding.weight
						snake_case   :					Tuple							=						pt_model.positional_embedding

						# copy layer norm
						copy_linear(hf_encoder.final_layer_norm,			pt_model.ln_final)

						# copy hidden layers
						copy_layers(hf_encoder.encoder.layers,			pt_model.transformer.resblocks)


def 					UpperCAmelCase__      (							_lowerCAmelCase						: List[str],			_lowerCAmelCase						: Any)						->		List[Any]:



						"""simple docstring"""

						snake_case   :					str							=						pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model,			_lowerCAmelCase)


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Optional[Any],			_lowerCAmelCase						: int)						->		Optional[int]:



						"""simple docstring"""

						snake_case   :					Dict							=						pt_model.visual.proj.data.T

						# copy layer norms
						copy_linear(hf_model.vision_model.pre_layrnorm,			pt_model.visual.ln_pre)
						copy_linear(hf_model.vision_model.post_layernorm,			pt_model.visual.ln_post)

						# copy embeds
						snake_case   :					Union[str, Any]							=						pt_model.visual.conva.weight.data
						snake_case   :					str							=						pt_model.visual.class_embedding
						snake_case   :					List[Any]							=						pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers,			pt_model.visual.transformer.resblocks)


@torch.no_grad()
def 					UpperCAmelCase__      (							_lowerCAmelCase						: Union[str, Any],			_lowerCAmelCase						: List[str],			_lowerCAmelCase						: Any=None)						->		Optional[int]:



						"""simple docstring"""

						if config_path is not None:
												snake_case   :					int							=						CLIPConfig.from_pretrained(_lowerCAmelCase)
						else:
												snake_case   :					Union[str, Any]							=						CLIPConfig(projection_dim=512,			text_config={},			vision_config={})

						snake_case   :					Optional[Any]							=						CLIPModel(_lowerCAmelCase).eval()

						snake_case							,				snake_case   :					int							=						load(_lowerCAmelCase,			device="cpu",			jit=_lowerCAmelCase)
						snake_case   :					Optional[int]							=						pt_model.eval()

						copy_text_model_and_projection(_lowerCAmelCase,			_lowerCAmelCase)
						copy_vison_model_and_projection(_lowerCAmelCase,			_lowerCAmelCase)
						snake_case   :					List[Any]							=						pt_model.logit_scale

						snake_case   :					int							=						torch.arange(0,			77).unsqueeze(0)
						snake_case   :					Optional[Any]							=						torch.randn(1,			3,			224,			224)

						snake_case   :					int							=						hf_model(input_ids=_lowerCAmelCase,			pixel_values=_lowerCAmelCase,			return_dict=_lowerCAmelCase)
						snake_case   :					str							=						hf_outputs.logits_per_image
						snake_case   :					Tuple							=						hf_outputs.logits_per_text
						snake_case							,				snake_case   :					Optional[Any]							=						pt_model(_lowerCAmelCase,			_lowerCAmelCase)

						assert torch.allclose(_lowerCAmelCase,			_lowerCAmelCase,			atol=1E-3)
						assert torch.allclose(_lowerCAmelCase,			_lowerCAmelCase,			atol=1E-3)

						hf_model.save_pretrained(_lowerCAmelCase)


if __name__ == "__main__":
						A_        =   argparse.ArgumentParser()
						parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
						parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
						A_        =   parser.parse_args()

						convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_        =   logging.get_logger(__name__)

A_        =   {
    "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
    "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
    "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
    "apple/deeplabv3-mobilevit-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-x-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-xx-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}




class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			__magic_name__   :							Optional[Any]             =					"""mobilevit"""



			def __init__(       self							, snake_case_=3							, snake_case_=256							, snake_case_=2							, snake_case_=[144, 192, 240]							, snake_case_=[16, 32, 64, 96, 128, 160, 640]							, snake_case_=4							, snake_case_=2.0							, snake_case_=4.0							, snake_case_="silu"							, snake_case_=3							, snake_case_=32							, snake_case_=0.1							, snake_case_=0.0							, snake_case_=0.1							, snake_case_=0.02							, snake_case_=1e-5							, snake_case_=True							, snake_case_=256							, snake_case_=[6, 12, 18]							, snake_case_=0.1							, snake_case_=255							, **snake_case_							, ):
									super().__init__(**snake_case_				)

									snake_case   :					Any							=						num_channels
									snake_case   :					Optional[Any]							=						image_size
									snake_case   :					Union[str, Any]							=						patch_size
									snake_case   :					Union[str, Any]							=						hidden_sizes
									snake_case   :					List[Any]							=						neck_hidden_sizes
									snake_case   :					Optional[Any]							=						num_attention_heads
									snake_case   :					Any							=						mlp_ratio
									snake_case   :					List[str]							=						expand_ratio
									snake_case   :					Tuple							=						hidden_act
									snake_case   :					Tuple							=						conv_kernel_size
									snake_case   :					int							=						output_stride
									snake_case   :					Dict							=						hidden_dropout_prob
									snake_case   :					List[str]							=						attention_probs_dropout_prob
									snake_case   :					int							=						classifier_dropout_prob
									snake_case   :					str							=						initializer_range
									snake_case   :					Optional[int]							=						layer_norm_eps
									snake_case   :					Dict							=						qkv_bias

									# decode head attributes for semantic segmentation
									snake_case   :					Optional[int]							=						aspp_out_channels
									snake_case   :					Dict							=						atrous_rates
									snake_case   :					Any							=						aspp_dropout_prob
									snake_case   :					Any							=						semantic_loss_ignore_index







class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			__magic_name__   :							Union[str, Any]             =					version.parse("""1.11"""			)



			@property
			def      lowerCamelCase   (       self				):
									return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]				)



			@property
			def      lowerCamelCase   (       self				):
									if self.task == "image-classification":
															return OrderedDict([("logits", {0: "batch"})]				)
									else:
															return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]				)






			@property
			def      lowerCamelCase   (       self				):
									return 1e-4





