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()
_SCREAMING_SNAKE_CASE:    Tuple     						=							logging.get_logger(__name__)






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_=False				,						lowerCamelCase_=False	):



			"""simple docstring"""





			_snake_case														=   "backbone." if is_semantic else ""

			_snake_case														=   []
			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__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_=False				,						lowerCamelCase_=False	):



			"""simple docstring"""





			for i in range(config.num_hidden_layers	):
						_snake_case														=   "backbone." if is_semantic else ""
						# queries, keys and values
						_snake_case														=   state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'	)
						_snake_case														=   state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'	)
						_snake_case														=   state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'	)

						_snake_case														=   in_proj_weight[
						    : config.hidden_size, :
						]
						_snake_case														=   q_bias
						_snake_case														=   in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						_snake_case														=   in_proj_weight[
						    -config.hidden_size :, :
						]
						_snake_case														=   v_bias

						# gamma_1 and gamma_2
						# we call them lambda because otherwise they are renamed when using .from_pretrained
						_snake_case														=   state_dict.pop(f'{prefix}blocks.{i}.gamma_1'	)
						_snake_case														=   state_dict.pop(f'{prefix}blocks.{i}.gamma_2'	)

						_snake_case														=   gamma_a
						_snake_case														=   gamma_a

						# relative_position bias table + index
						if not has_lm_head:
									# each layer has its own relative position bias
									_snake_case														=   state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_bias_table'	)
									_snake_case														=   state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_index'	)

									_snake_case														=   table
									_snake_case														=   index






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   dct.pop(lowerCamelCase_	)
			_snake_case														=   val






def  UpperCAmelCase__(    ):



			"""simple docstring"""





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






@torch.no_grad()
def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   BeitConfig()
			_snake_case														=   False
			_snake_case														=   False
			_snake_case														=   "huggingface/label-files"
			# set config parameters based on URL
			if checkpoint_url[-9:-4] == "pt22k":
						# masked image modeling
						_snake_case														=   True
						_snake_case														=   True
						_snake_case														=   True
			elif checkpoint_url[-9:-4] == "ft22k":
						# intermediate fine-tuning on ImageNet-22k
						_snake_case														=   True
						_snake_case														=   21841
						_snake_case														=   "imagenet-22k-id2label.json"
						_snake_case														=   json.load(open(hf_hub_download(lowerCamelCase_				,						lowerCamelCase_				,						repo_type="dataset"	)				,						"r"	)	)
						_snake_case														=   {int(lowerCamelCase_	): 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														=   idalabel
						_snake_case														=   {v: k for k, v in idalabel.items()}
			elif checkpoint_url[-8:-4] == "to1k":
						# fine-tuning on ImageNet-1k
						_snake_case														=   True
						_snake_case														=   1000
						_snake_case														=   "imagenet-1k-id2label.json"
						_snake_case														=   json.load(open(hf_hub_download(lowerCamelCase_				,						lowerCamelCase_				,						repo_type="dataset"	)				,						"r"	)	)
						_snake_case														=   {int(lowerCamelCase_	): v for k, v in idalabel.items()}
						_snake_case														=   idalabel
						_snake_case														=   {v: k for k, v in idalabel.items()}
						if "384" in checkpoint_url:
									_snake_case														=   384
						if "512" in checkpoint_url:
									_snake_case														=   512
			elif "ade20k" in checkpoint_url:
						# fine-tuning
						_snake_case														=   True
						_snake_case														=   150
						_snake_case														=   "ade20k-id2label.json"
						_snake_case														=   json.load(open(hf_hub_download(lowerCamelCase_				,						lowerCamelCase_				,						repo_type="dataset"	)				,						"r"	)	)
						_snake_case														=   {int(lowerCamelCase_	): v for k, v in idalabel.items()}
						_snake_case														=   idalabel
						_snake_case														=   {v: k for k, v in idalabel.items()}
						_snake_case														=   640
						_snake_case														=   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														=   1024
						_snake_case														=   4096
						_snake_case														=   24
						_snake_case														=   16
						if "ade20k" in checkpoint_url:
									_snake_case														=   640
									_snake_case														=   [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														=   torch.hub.load_state_dict_from_url(lowerCamelCase_				,						map_location="cpu"				,						check_hash=lowerCamelCase_	)
			_snake_case														=   state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

			_snake_case														=   create_rename_keys(lowerCamelCase_				,						has_lm_head=lowerCamelCase_				,						is_semantic=lowerCamelCase_	)
			for src, dest in rename_keys:
						rename_key(lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	)
			read_in_q_k_v(lowerCamelCase_				,						lowerCamelCase_				,						has_lm_head=lowerCamelCase_				,						is_semantic=lowerCamelCase_	)
			if is_semantic:
						# add prefix to decoder keys
						for key, val in state_dict.copy().items():
									_snake_case														=   state_dict.pop(lowerCamelCase_	)
									if key.startswith("backbone.fpn"	):
												_snake_case														=   key.replace("backbone.fpn"				,						"fpn"	)
									_snake_case														=   val

    # load HuggingFace model
			if checkpoint_url[-9:-4] == "pt22k":
						_snake_case														=   BeitForMaskedImageModeling(lowerCamelCase_	)
			elif "ade20k" in checkpoint_url:
						_snake_case														=   BeitForSemanticSegmentation(lowerCamelCase_	)
			else:
						_snake_case														=   BeitForImageClassification(lowerCamelCase_	)
			model.eval()
			model.load_state_dict(lowerCamelCase_	)

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

			_snake_case														=   image_processor(images=lowerCamelCase_				,						return_tensors="pt"	)
			_snake_case														=   encoding["pixel_values"]

			_snake_case														=   model(lowerCamelCase_	)
			_snake_case														=   outputs.logits

			# verify logits
			_snake_case														=   torch.Size([1, 1000]	)
			if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"	):
						_snake_case														=   torch.Size([1, 196, 8192]	)
			elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"	):
						_snake_case														=   torch.Size([1, 196, 8192]	)
			elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"	):
						_snake_case														=   torch.Size([1, 21841]	)
						_snake_case														=   torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]	)
						_snake_case														=   2397
			elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"	):
						_snake_case														=   torch.Size([1, 21841]	)
						_snake_case														=   torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]	)
						_snake_case														=   2396
			elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"	):
						_snake_case														=   torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]	)
						_snake_case														=   285
			elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"	):
						_snake_case														=   torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]	)
						_snake_case														=   281
			elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"	):
						_snake_case														=   torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]	)
						_snake_case														=   761
			elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"	):
						_snake_case														=   torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]	)
						_snake_case														=   761
			elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"	):
						_snake_case														=   torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]	)
						_snake_case														=   761
			elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"	):
						_snake_case														=   torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]	)
						_snake_case														=   761
			elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"	):
						_snake_case														=   torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]	)
						_snake_case														=   761
			elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"	):
						_snake_case														=   (1, 150, 160, 160)
						_snake_case														=   torch.tensor(
						    [
						        [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
						        [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
						        [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
						    ]	)
			elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"	):
						_snake_case														=   (1, 150, 160, 160)
						_snake_case														=   torch.tensor(
						    [
						        [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
						        [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
						        [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
						    ]	)
			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]				,						lowerCamelCase_				,						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]				,						lowerCamelCase_				,						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(lowerCamelCase_	).mkdir(exist_ok=lowerCamelCase_	)
			print(f'Saving model to {pytorch_dump_folder_path}'	)
			model.save_pretrained(lowerCamelCase_	)
			print(f'Saving image processor to {pytorch_dump_folder_path}'	)
			image_processor.save_pretrained(lowerCamelCase_	)


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE:    List[str]     						=							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.'''
			)
			_SCREAMING_SNAKE_CASE:    Dict     						=							parser.parse_args()
			convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_SCREAMING_SNAKE_CASE:    Optional[Any]     						=							{
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    Dict     						=							[
			    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SamModel''',
			    '''SamPreTrainedModel''',
			]
try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    Optional[Any]     						=							[
			    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFSamModel''',
			    '''TFSamPreTrainedModel''',
			]
try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    List[Any]     						=							['''SamImageProcessor''']


if TYPE_CHECKING:
			from .configuration_sam import (
			    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SamConfig,
			    SamMaskDecoderConfig,
			    SamPromptEncoderConfig,
			    SamVisionConfig,
			)
			from .processing_sam import SamProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_sam import SamImageProcessor

else:
			import sys

			_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

