"""simple docstring"""
import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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





def    lowerCAmelCase__    (			_UpperCamelCase       :				Tuple ,			_UpperCamelCase       :				List[str]=False					)					->				List[str]:





      """simple docstring"""






      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"""blocks.{i}.norm1.weight""", f"""deit.encoder.layer.{i}.layernorm_before.weight""")					)
            rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""")					)
            rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""")					)
            rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""")					)
            rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""")					)
            rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""")					)
            rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""")					)
            rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""")					)
            rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""")					)
            rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""")					)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ('cls_token', 'deit.embeddings.cls_token'),
              ('dist_token', 'deit.embeddings.distillation_token'),
              ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
              ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
              ('pos_embed', 'deit.embeddings.position_embeddings'),
          ]					)

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ('norm.weight', 'layernorm.weight'),
                    ('norm.bias', 'layernorm.bias'),
                    ('pre_logits.fc.weight', 'pooler.dense.weight'),
                    ('pre_logits.fc.bias', 'pooler.dense.bias'),
                ]					)

            # if just the base model, we should remove "deit" from all keys that start with "deit"
            snake_case								=			[(pair[0], pair[1][4:]) if pair[1].startswith('deit'					) else pair for pair in rename_keys]
      else:
            # layernorm + classification heads
            rename_keys.extend(
                [
                    ('norm.weight', 'deit.layernorm.weight'),
                    ('norm.bias', 'deit.layernorm.bias'),
                    ('head.weight', 'cls_classifier.weight'),
                    ('head.bias', 'cls_classifier.bias'),
                    ('head_dist.weight', 'distillation_classifier.weight'),
                    ('head_dist.bias', 'distillation_classifier.bias'),
                ]					)

      return rename_keys





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				Tuple ,			_UpperCamelCase       :				int=False					)					->				Optional[int]:





      """simple docstring"""






      for i in range(config.num_hidden_layers					):
            if base_model:
                  snake_case								=			''
            else:
                  snake_case								=			'deit.'
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            snake_case								=			state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""					)
            snake_case								=			state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""					)
            # next, add query, keys and values (in that order) to the state dict
            snake_case								=			in_proj_weight[
                : config.hidden_size, :
            ]
            snake_case								=			in_proj_bias[: config.hidden_size]
            snake_case								=			in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            snake_case								=			in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            snake_case								=			in_proj_weight[
                -config.hidden_size :, :
            ]
            snake_case								=			in_proj_bias[-config.hidden_size :]





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[int] ,			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				Optional[int]					)					->				Any:





      """simple docstring"""






      snake_case								=			dct.pop(_UpperCamelCase					)
      snake_case								=			val





def    lowerCAmelCase__    (			)					->				Tuple:





      """simple docstring"""






      snake_case								=			'http://images.cocodataset.org/val2017/000000039769.jpg'
      snake_case								=			Image.open(requests.get(_UpperCamelCase ,			stream=_UpperCamelCase					).raw					)
      return im





@torch.no_grad()
def    lowerCAmelCase__    (			_UpperCamelCase       :				int ,			_UpperCamelCase       :				List[str]					)					->				Tuple:





      """simple docstring"""






      snake_case								=			DeiTConfig()
      # all deit models have fine-tuned heads
      snake_case								=			False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      snake_case								=			1_0_0_0
      snake_case								=			'huggingface/label-files'
      snake_case								=			'imagenet-1k-id2label.json'
      snake_case								=			json.load(open(hf_hub_download(_UpperCamelCase ,			_UpperCamelCase ,			repo_type='dataset'					) ,			'r'					)					)
      snake_case								=			{int(_UpperCamelCase					): v for k, v in idalabel.items()}
      snake_case								=			idalabel
      snake_case								=			{v: k for k, v in idalabel.items()}
      snake_case								=			int(deit_name[-6:-4]					)
      snake_case								=			int(deit_name[-3:]					)
      # size of the architecture
      if deit_name[9:].startswith('tiny'					):
            snake_case								=			1_9_2
            snake_case								=			7_6_8
            snake_case								=			1_2
            snake_case								=			3
      elif deit_name[9:].startswith('small'					):
            snake_case								=			3_8_4
            snake_case								=			1_5_3_6
            snake_case								=			1_2
            snake_case								=			6
      if deit_name[9:].startswith('base'					):
            pass
      elif deit_name[4:].startswith('large'					):
            snake_case								=			1_0_2_4
            snake_case								=			4_0_9_6
            snake_case								=			2_4
            snake_case								=			1_6

      # load original model from timm
      snake_case								=			timm.create_model(_UpperCamelCase ,			pretrained=_UpperCamelCase					)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      snake_case								=			timm_model.state_dict()
      snake_case								=			create_rename_keys(_UpperCamelCase ,			_UpperCamelCase					)
      for src, dest in rename_keys:
            rename_key(_UpperCamelCase ,			_UpperCamelCase ,			_UpperCamelCase					)
      read_in_q_k_v(_UpperCamelCase ,			_UpperCamelCase ,			_UpperCamelCase					)

      # load HuggingFace model
      snake_case								=			DeiTForImageClassificationWithTeacher(_UpperCamelCase					).eval()
      model.load_state_dict(_UpperCamelCase					)

      # Check outputs on an image, prepared by DeiTImageProcessor
      snake_case								=			int(
          (2_5_6 / 2_2_4) * config.image_size					)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      snake_case								=			DeiTImageProcessor(size=_UpperCamelCase ,			crop_size=config.image_size					)
      snake_case								=			image_processor(images=prepare_img() ,			return_tensors='pt'					)
      snake_case								=			encoding['pixel_values']
      snake_case								=			model(_UpperCamelCase					)

      snake_case								=			timm_model(_UpperCamelCase					)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(_UpperCamelCase ,			outputs.logits ,			atol=1e-3					)

      Path(_UpperCamelCase					).mkdir(exist_ok=_UpperCamelCase					)
      print(f"""Saving model {deit_name} to {pytorch_dump_folder_path}"""					)
      model.save_pretrained(_UpperCamelCase					)
      print(f"""Saving image processor to {pytorch_dump_folder_path}"""					)
      image_processor.save_pretrained(_UpperCamelCase					)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__      						=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--deit_name",
         default="vit_deit_base_distilled_patch16_224",
         type=str,
         help="Name of the DeiT timm model you'd like to convert.",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
     )

     SCREAMING_SNAKE_CASE__      						=  parser.parse_args()
     convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



"""simple docstring"""
class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				pass





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				pass





class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self    ):






										"""simple docstring"""

										snake_case								=			[
										    [],
										    [],
										    [],
										]

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										try:
																if len(self.queues[priority]    ) >= 1_00:
																						raise OverflowError('Maximum queue size is 100'    )
																self.queues[priority].append(lowerCAmelCase    )
										except IndexError:
																raise ValueError('Valid priorities are 0, 1, and 2'    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										for queue in self.queues:
																if queue:
																						return queue.pop(0    )
										raise UnderFlowError('All queues are empty'    )

				def __str__(				self    ):






										"""simple docstring"""

										return "\n".join(F"""Priority {i}: {q}""" for i, q in enumerate(self.queues    )    )





class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self    ):






										"""simple docstring"""

										snake_case								=			[]

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if len(self.queue    ) == 1_00:
																raise OverFlowError('Maximum queue size is 100'    )
										self.queue.append(lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										if not self.queue:
																raise UnderFlowError('The queue is empty'    )
										else:
																snake_case								=			min(self.queue    )
																self.queue.remove(lowerCAmelCase    )
																return data

				def __str__(				self    ):






										"""simple docstring"""

										return str(self.queue    )





def    lowerCAmelCase__    (			)					->				int:





						"""simple docstring"""






						snake_case								=			FixedPriorityQueue()
						fpq.enqueue(0 ,			1_0					)
						fpq.enqueue(1 ,			7_0					)
						fpq.enqueue(0 ,			1_0_0					)
						fpq.enqueue(2 ,			1					)
						fpq.enqueue(2 ,			5					)
						fpq.enqueue(1 ,			7					)
						fpq.enqueue(2 ,			4					)
						fpq.enqueue(1 ,			6_4					)
						fpq.enqueue(0 ,			1_2_8					)
						print(_UpperCamelCase					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(_UpperCamelCase					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)





def    lowerCAmelCase__    (			)					->				List[str]:





						"""simple docstring"""






						snake_case								=			ElementPriorityQueue()
						epq.enqueue(1_0					)
						epq.enqueue(7_0					)
						epq.enqueue(1_0_0					)
						epq.enqueue(1					)
						epq.enqueue(5					)
						epq.enqueue(7					)
						epq.enqueue(4					)
						epq.enqueue(6_4					)
						epq.enqueue(1_2_8					)
						print(_UpperCamelCase					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(_UpperCamelCase					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)


if __name__ == "__main__":
					fixed_priority_queue()
					element_priority_queue()


