import argparse
import json
from pathlib import Path

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

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase    :  int										= logging.get_logger(__name__)

def   a_					(_lowerCAmelCase  :   int			,	_lowerCAmelCase  :   Union[str, Any]=False			,	_lowerCAmelCase  :   Dict=False       )->				List[str]:
    snake_case:						List[str]         =   """backbone.""" if is_semantic else """"""

    snake_case:						Optional[Any]         =   []
    for i in range(config.num_hidden_layers       ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((F"{prefix}blocks.{i}.norm1.weight", F"beit.encoder.layer.{i}.layernorm_before.weight")       )
        rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias")       )
        rename_keys.append(
            (F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight")       )
        rename_keys.append(
            (F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias")       )
        rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight")       )
        rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias")       )
        rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight")       )
        rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias")       )
        rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight")       )
        rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias")       )

    # projection layer + position embeddings
    rename_keys.extend(
        [
            (F"{prefix}cls_token", """beit.embeddings.cls_token"""),
            (F"{prefix}patch_embed.proj.weight", """beit.embeddings.patch_embeddings.projection.weight"""),
            (F"{prefix}patch_embed.proj.bias", """beit.embeddings.patch_embeddings.projection.bias"""),
        ]       )

    if has_lm_head:
        # mask token + shared relative position bias + layernorm
        rename_keys.extend(
            [
                ("""mask_token""", """beit.embeddings.mask_token"""),
                (
                    """rel_pos_bias.relative_position_bias_table""",
                    """beit.encoder.relative_position_bias.relative_position_bias_table""",
                ),
                (
                    """rel_pos_bias.relative_position_index""",
                    """beit.encoder.relative_position_bias.relative_position_index""",
                ),
                ("""norm.weight""", """layernorm.weight"""),
                ("""norm.bias""", """layernorm.bias"""),
            ]       )
    elif is_semantic:
        # semantic segmentation classification heads
        rename_keys.extend(
            [
                ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
                ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
                ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
                ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
            ]       )
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
                ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
                ("""head.weight""", """classifier.weight"""),
                ("""head.bias""", """classifier.bias"""),
            ]       )

    return rename_keys

def   a_					(_lowerCAmelCase  :   Optional[Any]			,	_lowerCAmelCase  :   Optional[Any]			,	_lowerCAmelCase  :   List[str]=False			,	_lowerCAmelCase  :   Optional[int]=False       )->				List[str]:


    for i in range(config.num_hidden_layers       ):
        snake_case:						List[str]         =   """backbone.""" if is_semantic else """"""
        # queries, keys and values
        snake_case:						str         =   state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"       )
        snake_case:						Tuple         =   state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"       )
        snake_case:						Optional[Any]         =   state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"       )

        snake_case:						Optional[Any]         =   in_proj_weight[
            : config.hidden_size, :
        ]
        snake_case:						Any         =   q_bias
        snake_case:						Dict         =   in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        snake_case:						Optional[int]         =   in_proj_weight[
            -config.hidden_size :, :
        ]
        snake_case:						Tuple         =   v_bias

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        snake_case:						Any         =   state_dict.pop(F"{prefix}blocks.{i}.gamma_1"       )
        snake_case:						Any         =   state_dict.pop(F"{prefix}blocks.{i}.gamma_2"       )

        snake_case:						Union[str, Any]         =   gamma_a
        snake_case:						str         =   gamma_a

        # relative_position bias table + index
        if not has_lm_head:
            # each layer has its own relative position bias
            snake_case:						int         =   state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_bias_table"       )
            snake_case:						Dict         =   state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_index"       )

            snake_case:						Optional[Any]         =   table
            snake_case:						Any         =   index

def   a_					(_lowerCAmelCase  :   Union[str, Any]			,	_lowerCAmelCase  :   Dict			,	_lowerCAmelCase  :   Optional[int]       )->				List[str]:
    snake_case:						List[Any]         =   dct.pop(__snake_case       )
    snake_case:						Dict         =   val

def   a_					()->				Any:
    snake_case:						Tuple         =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
    snake_case:						Dict         =   Image.open(requests.get(__snake_case			,	stream=__snake_case       ).raw       )
    return im

@torch.no_grad()
def   a_					(_lowerCAmelCase  :   Tuple			,	_lowerCAmelCase  :   Union[str, Any]       )->				Union[str, Any]:
    snake_case:						Dict         =   BeitConfig()
    snake_case:						int         =   False
    snake_case:						str         =   False
    snake_case:						Optional[int]         =   """huggingface/label-files"""
    # set config parameters based on URL
    if checkpoint_url[-9:-4] == "pt22k":
        # masked image modeling
        snake_case:						int         =   True
        snake_case:						Union[str, Any]         =   True
        snake_case:						Tuple         =   True
    elif checkpoint_url[-9:-4] == "ft22k":
        # intermediate fine-tuning on ImageNet-22k
        snake_case:						int         =   True
        snake_case:						Optional[Any]         =   21841
        snake_case:						Optional[Any]         =   """imagenet-22k-id2label.json"""
        snake_case:						List[Any]         =   json.load(open(hf_hub_download(__snake_case			,	__snake_case			,	repo_type="""dataset"""       )			,	"""r"""       )       )
        snake_case:						List[Any]         =   {int(__snake_case       ): v for k, v in idalabel.items()}
        # this dataset contains 21843 labels but the model only has 21841
        # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
        del idalabel[9205]
        del idalabel[15027]
        snake_case:						Optional[Any]         =   idalabel
        snake_case:						List[str]         =   {v: k for k, v in idalabel.items()}
    elif checkpoint_url[-8:-4] == "to1k":
        # fine-tuning on ImageNet-1k
        snake_case:						List[str]         =   True
        snake_case:						List[Any]         =   1000
        snake_case:						Dict         =   """imagenet-1k-id2label.json"""
        snake_case:						List[str]         =   json.load(open(hf_hub_download(__snake_case			,	__snake_case			,	repo_type="""dataset"""       )			,	"""r"""       )       )
        snake_case:						Optional[Any]         =   {int(__snake_case       ): v for k, v in idalabel.items()}
        snake_case:						Optional[int]         =   idalabel
        snake_case:						Optional[int]         =   {v: k for k, v in idalabel.items()}
        if "384" in checkpoint_url:
            snake_case:						Optional[int]         =   384
        if "512" in checkpoint_url:
            snake_case:						str         =   512
    elif "ade20k" in checkpoint_url:
        # fine-tuning
        snake_case:						int         =   True
        snake_case:						Dict         =   150
        snake_case:						List[Any]         =   """ade20k-id2label.json"""
        snake_case:						List[Any]         =   json.load(open(hf_hub_download(__snake_case			,	__snake_case			,	repo_type="""dataset"""       )			,	"""r"""       )       )
        snake_case:						Dict         =   {int(__snake_case       ): v for k, v in idalabel.items()}
        snake_case:						Optional[int]         =   idalabel
        snake_case:						List[Any]         =   {v: k for k, v in idalabel.items()}
        snake_case:						Optional[int]         =   640
        snake_case:						int         =   True
    else:
        raise ValueError("""Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\'"""       )

    # size of the architecture
    if "base" in checkpoint_url:
        pass
    elif "large" in checkpoint_url:
        snake_case:						List[Any]         =   1024
        snake_case:						List[Any]         =   4096
        snake_case:						Union[str, Any]         =   24
        snake_case:						Any         =   16
        if "ade20k" in checkpoint_url:
            snake_case:						Optional[int]         =   640
            snake_case:						int         =   [7, 11, 15, 23]
    else:
        raise ValueError("""Should either find \'base\' or \'large\' in checkpoint URL"""       )

    # load state_dict of original model, remove and rename some keys
    snake_case:						Optional[Any]         =   torch.hub.load_state_dict_from_url(__snake_case			,	map_location="""cpu"""			,	check_hash=__snake_case       )
    snake_case:						Optional[int]         =   state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

    snake_case:						List[str]         =   create_rename_keys(__snake_case			,	has_lm_head=__snake_case			,	is_semantic=__snake_case       )
    for src, dest in rename_keys:
        rename_key(__snake_case			,	__snake_case			,	__snake_case       )
    read_in_q_k_v(__snake_case			,	__snake_case			,	has_lm_head=__snake_case			,	is_semantic=__snake_case       )
    if is_semantic:
        # add prefix to decoder keys
        for key, val in state_dict.copy().items():
            snake_case:						Optional[int]         =   state_dict.pop(__snake_case       )
            if key.startswith("""backbone.fpn"""       ):
                snake_case:						Union[str, Any]         =   key.replace("""backbone.fpn"""			,	"""fpn"""       )
            snake_case:						str         =   val

             # load HuggingFace model
    if checkpoint_url[-9:-4] == "pt22k":
        snake_case:						Tuple         =   BeitForMaskedImageModeling(__snake_case       )
    elif "ade20k" in checkpoint_url:
        snake_case:						List[str]         =   BeitForSemanticSegmentation(__snake_case       )
    else:
        snake_case:						Any         =   BeitForImageClassification(__snake_case       )
    model.eval()
    model.load_state_dict(__snake_case       )

    # Check outputs on an image
    if is_semantic:
        snake_case:						Union[str, Any]         =   BeitImageProcessor(size=config.image_size			,	do_center_crop=__snake_case       )
        snake_case:						List[str]         =   load_dataset("""hf-internal-testing/fixtures_ade20k"""			,	split="""test"""       )
        snake_case:						Tuple         =   Image.open(ds[0]["""file"""]       )
    else:
        snake_case:						Any         =   BeitImageProcessor(
            size=config.image_size			,	resample=PILImageResampling.BILINEAR			,	do_center_crop=__snake_case       )
        snake_case:						Any         =   prepare_img()

    snake_case:						List[str]         =   image_processor(images=__snake_case			,	return_tensors="""pt"""       )
    snake_case:						Any         =   encoding["""pixel_values"""]

    snake_case:						Union[str, Any]         =   model(__snake_case       )
    snake_case:						str         =   outputs.logits

    # verify logits
    snake_case:						Optional[int]         =   torch.Size([1, 1000]       )
    if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""       ):
        snake_case:						Tuple         =   torch.Size([1, 196, 8192]       )
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""       ):
        snake_case:						Tuple         =   torch.Size([1, 196, 8192]       )
    elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""       ):
        snake_case:						Any         =   torch.Size([1, 21841]       )
        snake_case:						Any         =   torch.tensor([2.2_288, 2.4_671, 0.7_395]       )
        snake_case:						List[str]         =   2397
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""       ):
        snake_case:						str         =   torch.Size([1, 21841]       )
        snake_case:						Dict         =   torch.tensor([1.6_881, -0.2_787, 0.5_901]       )
        snake_case:						int         =   2396
    elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""       ):
        snake_case:						List[Any]         =   torch.tensor([0.1_241, 0.0_798, -0.6_569]       )
        snake_case:						Tuple         =   285
    elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""       ):
        snake_case:						List[Any]         =   torch.tensor([-1.2_385, -1.0_987, -1.0_108]       )
        snake_case:						Dict         =   281
    elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""       ):
        snake_case:						List[str]         =   torch.tensor([-1.5_303, -0.9_484, -0.3_147]       )
        snake_case:						List[Any]         =   761
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""       ):
        snake_case:						str         =   torch.tensor([0.4_610, -0.0_928, 0.2_086]       )
        snake_case:						List[str]         =   761
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""       ):
        snake_case:						List[str]         =   torch.tensor([-0.4_804, 0.6_257, -0.1_837]       )
        snake_case:						Optional[Any]         =   761
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""       ):
        snake_case:						int         =   torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]       )
        snake_case:						Any         =   761
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""       ):
        snake_case:						Optional[int]         =   torch.tensor([-0.3_062, 0.7_261, 0.4_852]       )
        snake_case:						Tuple         =   761
    elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""       ):
        snake_case:						str         =   (1, 150, 160, 160)
        snake_case:						Union[str, Any]         =   torch.tensor(
            [
                [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
                [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
                [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
            ]       )
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_640_pt22k_ft22ktoade20k"""       ):
        snake_case:						Optional[Any]         =   (1, 150, 160, 160)
        snake_case:						List[Any]         =   torch.tensor(
            [
                [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
                [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
                [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
            ]       )
    else:
        raise ValueError("""Can\'t verify logits as model is not supported"""       )

    assert logits.shape == expected_shape, "Shape of logits not as expected"
    if not has_lm_head:
        if is_semantic:
            assert torch.allclose(
                logits[0, :3, :3, :3]			,	__snake_case			,	atol=1e-3       ), "First elements of logits not as expected"
        else:
            print("""Predicted class idx:"""			,	logits.argmax(-1       ).item()       )
            assert torch.allclose(
                logits[0, :3]			,	__snake_case			,	atol=1e-3       ), "First elements of logits not as expected"
            assert logits.argmax(-1       ).item() == expected_class_idx, "Predicted class index not as expected"

    Path(__snake_case       ).mkdir(exist_ok=__snake_case       )
    print(F"Saving model 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 __name__ == "__main__":
    __lowerCAmelCase    :  Union[str, Any]										= argparse.ArgumentParser()

    parser.add_argument(
        '--checkpoint_url',
        default='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth',
        type=str,
        help='URL to the original PyTorch checkpoint (.pth file).',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
    )
    __lowerCAmelCase    :  str										= parser.parse_args()
    convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     42





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      super().__init__()

      _lowercase				 =PNDMScheduler.from_config(scheduler.config			)

      self.register_modules(unet=UpperCAmelCase						,	scheduler=UpperCAmelCase			)





     @torch.no_grad()
     def __call__(self						,	UpperCAmelCase = 1						,	UpperCAmelCase = 5_0						,	UpperCAmelCase = None						,	UpperCAmelCase = "pil"						,	UpperCAmelCase = True						,	**UpperCAmelCase						,	)	->  Union[ImagePipelineOutput, Tuple]:
      _lowercase				 =randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,	generator=UpperCAmelCase						,	device=self.device						,	)

      self.scheduler.set_timesteps(UpperCAmelCase			)
      for t in self.progress_bar(self.scheduler.timesteps			):
       _lowercase				 =self.unet(UpperCAmelCase						,	UpperCAmelCase			).sample

       _lowercase				 =self.scheduler.step(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			).prev_sample

      _lowercase				 =(image / 2 + 0.5).clamp(0						,	1			)
      _lowercase				 =image.cpu().permute(0						,	2						,	3						,	1			).numpy()
      if output_type == "pil":
       _lowercase				 =self.numpy_to_pil(UpperCAmelCase			)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase			)

