
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()
a__				:      List[Any]    		=     logging.get_logger(__name__)







def 				UpperCAmelCase_(    a__ ,						a__=False ,						a__=False   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Dict				      = '''backbone.''' if is_semantic else ''''''

    SCREAMING_SNAKE_CASE				:				List[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 				UpperCAmelCase_(    a__ ,						a__ ,						a__=False ,						a__=False   ):



    """simple docstring"""



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

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

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

        SCREAMING_SNAKE_CASE				:				Tuple				      = gamma_a
        SCREAMING_SNAKE_CASE				:				str				      = gamma_a

        # relative_position bias table + index
        if not has_lm_head:
            # each layer has its own relative position bias
            SCREAMING_SNAKE_CASE				:				Optional[int]				      = state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_bias_table"""   )
            SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_index"""   )

            SCREAMING_SNAKE_CASE				:				Dict				      = table
            SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = index







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dct.pop(a__   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = val







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = Image.open(requests.get(a__ ,						stream=a__   ).raw   )
    return im







@torch.no_grad()
def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = BeitConfig()
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = False
    SCREAMING_SNAKE_CASE				:				Any				      = False
    SCREAMING_SNAKE_CASE				:				List[str]				      = '''huggingface/label-files'''
    # set config parameters based on URL
    if checkpoint_url[-9:-4] == "pt22k":
        # masked image modeling
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = True
        SCREAMING_SNAKE_CASE				:				int				      = True
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = True
    elif checkpoint_url[-9:-4] == "ft22k":
        # intermediate fine-tuning on ImageNet-22k
        SCREAMING_SNAKE_CASE				:				int				      = True
        SCREAMING_SNAKE_CASE				:				int				      = 21_841
        SCREAMING_SNAKE_CASE				:				Tuple				      = '''imagenet-22k-id2label.json'''
        SCREAMING_SNAKE_CASE				:				List[Any]				      = json.load(open(hf_hub_download(a__ ,						a__ ,						repo_type='''dataset'''   ) ,						'''r'''   )   )
        SCREAMING_SNAKE_CASE				:				int				      = {int(a__   ): 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[9_205]
        del idalabel[15_027]
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = idalabel
        SCREAMING_SNAKE_CASE				:				List[Any]				      = {v: k for k, v in idalabel.items()}
    elif checkpoint_url[-8:-4] == "to1k":
        # fine-tuning on ImageNet-1k
        SCREAMING_SNAKE_CASE				:				Tuple				      = True
        SCREAMING_SNAKE_CASE				:				str				      = 1_000
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = '''imagenet-1k-id2label.json'''
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = json.load(open(hf_hub_download(a__ ,						a__ ,						repo_type='''dataset'''   ) ,						'''r'''   )   )
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = {int(a__   ): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE				:				str				      = idalabel
        SCREAMING_SNAKE_CASE				:				str				      = {v: k for k, v in idalabel.items()}
        if "384" in checkpoint_url:
            SCREAMING_SNAKE_CASE				:				Tuple				      = 384
        if "512" in checkpoint_url:
            SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 512
    elif "ade20k" in checkpoint_url:
        # fine-tuning
        SCREAMING_SNAKE_CASE				:				Any				      = True
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = 150
        SCREAMING_SNAKE_CASE				:				str				      = '''ade20k-id2label.json'''
        SCREAMING_SNAKE_CASE				:				Tuple				      = json.load(open(hf_hub_download(a__ ,						a__ ,						repo_type='''dataset'''   ) ,						'''r'''   )   )
        SCREAMING_SNAKE_CASE				:				str				      = {int(a__   ): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = idalabel
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = {v: k for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE				:				Tuple				      = 640
        SCREAMING_SNAKE_CASE				:				str				      = 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:
        SCREAMING_SNAKE_CASE				:				List[str]				      = 1_024
        SCREAMING_SNAKE_CASE				:				str				      = 4_096
        SCREAMING_SNAKE_CASE				:				Any				      = 24
        SCREAMING_SNAKE_CASE				:				List[Any]				      = 16
        if "ade20k" in checkpoint_url:
            SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = 640
            SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = [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
    SCREAMING_SNAKE_CASE				:				int				      = torch.hub.load_state_dict_from_url(a__ ,						map_location='''cpu''' ,						check_hash=a__   )
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

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

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

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

    SCREAMING_SNAKE_CASE				:				Dict				      = image_processor(images=a__ ,						return_tensors='''pt'''   )
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = encoding['''pixel_values''']

    SCREAMING_SNAKE_CASE				:				Any				      = model(a__   )
    SCREAMING_SNAKE_CASE				:				Tuple				      = outputs.logits

    # verify logits
    SCREAMING_SNAKE_CASE				:				Any				      = torch.Size([1, 1_000]   )
    if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''   ):
        SCREAMING_SNAKE_CASE				:				Dict				      = torch.Size([1, 196, 8_192]   )
    elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''   ):
        SCREAMING_SNAKE_CASE				:				int				      = torch.Size([1, 196, 8_192]   )
    elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''   ):
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch.Size([1, 21_841]   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch.tensor([2.2_288, 2.4_671, 0.7_395]   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = 2_397
    elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''   ):
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.Size([1, 21_841]   )
        SCREAMING_SNAKE_CASE				:				Dict				      = torch.tensor([1.6_881, -0.2_787, 0.5_901]   )
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 2_396
    elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''   ):
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.tensor([0.1_241, 0.0_798, -0.6_569]   )
        SCREAMING_SNAKE_CASE				:				Any				      = 285
    elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''   ):
        SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.tensor([-1.2_385, -1.0_987, -1.0_108]   )
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = 281
    elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''   ):
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = torch.tensor([-1.5_303, -0.9_484, -0.3_147]   )
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 761
    elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''   ):
        SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.tensor([0.4_610, -0.0_928, 0.2_086]   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = 761
    elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''   ):
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch.tensor([-0.4_804, 0.6_257, -0.1_837]   )
        SCREAMING_SNAKE_CASE				:				str				      = 761
    elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''   ):
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]   )
        SCREAMING_SNAKE_CASE				:				int				      = 761
    elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''   ):
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.3_062, 0.7_261, 0.4_852]   )
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = 761
    elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''   ):
        SCREAMING_SNAKE_CASE				:				Dict				      = (1, 150, 160, 160)
        SCREAMING_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'''   ):
        SCREAMING_SNAKE_CASE				:				int				      = (1, 150, 160, 160)
        SCREAMING_SNAKE_CASE				:				Optional[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] ,						a__ ,						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] ,						a__ ,						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(a__   ).mkdir(exist_ok=a__   )
    print(F"""Saving model to {pytorch_dump_folder_path}"""   )
    model.save_pretrained(a__   )
    print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
    image_processor.save_pretrained(a__   )


if __name__ == "__main__":
      a__				:      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.'''
      )
      a__				:      int    		=     parser.parse_args()
      convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


def 						A_			(				snake_case				: Union[str, Any]			,   snake_case				: Dict			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      (boundary[1] - boundary[0]) / steps
						__UpperCamelCase  	=      boundary[0]
						__UpperCamelCase  	=      boundary[1]
						__UpperCamelCase  	=      make_points(snake_case			,   snake_case			,   snake_case			)
						__UpperCamelCase  	=      0.0
						y += (h / 3.0) * f(snake_case			)
						__UpperCamelCase  	=      2
						for i in x_i:
												y += (h / 3) * (4 - 2 * (cnt % 2)) * f(snake_case			)
												cnt += 1
						y += (h / 3.0) * f(snake_case			)
						return y




def 						A_			(				snake_case				: str			,   snake_case				: Optional[int]			,   snake_case				: Any			)   ->     Optional[int]:



						'''simple docstring'''

						__UpperCamelCase  	=      a + h
						while x < (b - h):
												yield x
												__UpperCamelCase  	=      x + h




def 						A_			(				snake_case				: Union[str, Any]			)   ->     str:  # enter your function here



						'''simple docstring'''

						__UpperCamelCase  	=      (x - 0) * (x - 0)
						return y




def 						A_			(				)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      0.0  # Lower bound of integration
						__UpperCamelCase  	=      1.0  # Upper bound of integration
						__UpperCamelCase  	=      10.0  # define number of steps or resolution
						__UpperCamelCase  	=      [a, b]  # define boundary of integration
						__UpperCamelCase  	=      method_a(snake_case			,   snake_case			)
						print(f"y = {y}"			)


if __name__ == "__main__":
				main()




