
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    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			int       ,lowerCAmelCase_				:			int=False							)    ->   int:







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	:							Tuple											=[]
							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"
														SCREAMING_SNAKE_CASE_	:							List[Any]											=[(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    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			Tuple       ,lowerCAmelCase_				:			List[str]       ,lowerCAmelCase_				:			List[Any]=False							)    ->   Optional[int]:







							"""simple docstring"""






							for i in range(config.num_hidden_layers							):
														if base_model:
																					SCREAMING_SNAKE_CASE_	:							Optional[int]											=""
														else:
																					SCREAMING_SNAKE_CASE_	:							Union[str, Any]											="deit."
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE_	:							List[str]											=state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""							)
														SCREAMING_SNAKE_CASE_	:							str											=state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""							)
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE_	:							Any											=in_proj_weight[
														    : config.hidden_size, :
														]
														SCREAMING_SNAKE_CASE_	:							List[str]											=in_proj_bias[: config.hidden_size]
														SCREAMING_SNAKE_CASE_	:							Tuple											=in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														SCREAMING_SNAKE_CASE_	:							Any											=in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														SCREAMING_SNAKE_CASE_	:							str											=in_proj_weight[
														    -config.hidden_size :, :
														]
														SCREAMING_SNAKE_CASE_	:							List[Any]											=in_proj_bias[-config.hidden_size :]
def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			Dict       ,lowerCAmelCase_				:			Dict       ,lowerCAmelCase_				:			Tuple							)    ->   int:







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	:							str											=dct.pop(lowerCAmelCase_							)
							SCREAMING_SNAKE_CASE_	:							Any											=val
def    SCREAMING_SNAKE_CASE__		( )    ->   Tuple:







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	:							Union[str, Any]											="http://images.cocodataset.org/val2017/000000039769.jpg"
							SCREAMING_SNAKE_CASE_	:							str											=Image.open(requests.get(lowerCAmelCase_       ,stream=lowerCAmelCase_							).raw							)
							return im
@torch.no_grad()
def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			Dict       ,lowerCAmelCase_				:			Optional[Any]							)    ->   Optional[int]:







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	:							Tuple											=DeiTConfig()
							# all deit models have fine-tuned heads
							SCREAMING_SNAKE_CASE_	:							Tuple											=False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							SCREAMING_SNAKE_CASE_	:							int											=1000
							SCREAMING_SNAKE_CASE_	:							Tuple											="huggingface/label-files"
							SCREAMING_SNAKE_CASE_	:							Union[str, Any]											="imagenet-1k-id2label.json"
							SCREAMING_SNAKE_CASE_	:							Dict											=json.load(open(hf_hub_download(lowerCAmelCase_       ,lowerCAmelCase_       ,repo_type='dataset'							)       ,'r'							)							)
							SCREAMING_SNAKE_CASE_	:							Union[str, Any]											={int(lowerCAmelCase_							): v for k, v in idalabel.items()}
							SCREAMING_SNAKE_CASE_	:							List[str]											=idalabel
							SCREAMING_SNAKE_CASE_	:							Dict											={v: k for k, v in idalabel.items()}
							SCREAMING_SNAKE_CASE_	:							Optional[int]											=int(deit_name[-6:-4]							)
							SCREAMING_SNAKE_CASE_	:							Optional[int]											=int(deit_name[-3:]							)
							# size of the architecture
							if deit_name[9:].startswith('tiny'							):
														SCREAMING_SNAKE_CASE_	:							List[str]											=192
														SCREAMING_SNAKE_CASE_	:							Dict											=768
														SCREAMING_SNAKE_CASE_	:							List[str]											=12
														SCREAMING_SNAKE_CASE_	:							Any											=3
							elif deit_name[9:].startswith('small'							):
														SCREAMING_SNAKE_CASE_	:							Optional[int]											=384
														SCREAMING_SNAKE_CASE_	:							List[str]											=1536
														SCREAMING_SNAKE_CASE_	:							Tuple											=12
														SCREAMING_SNAKE_CASE_	:							List[str]											=6
							if deit_name[9:].startswith('base'							):
														pass
							elif deit_name[4:].startswith('large'							):
														SCREAMING_SNAKE_CASE_	:							Optional[int]											=1024
														SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=4096
														SCREAMING_SNAKE_CASE_	:							int											=24
														SCREAMING_SNAKE_CASE_	:							Any											=16

							# load original model from timm
							SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=timm.create_model(lowerCAmelCase_       ,pretrained=lowerCAmelCase_							)
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							SCREAMING_SNAKE_CASE_	:							Tuple											=timm_model.state_dict()
							SCREAMING_SNAKE_CASE_	:							Optional[Any]											=create_rename_keys(lowerCAmelCase_       ,lowerCAmelCase_							)
							for src, dest in rename_keys:
														rename_key(lowerCAmelCase_       ,lowerCAmelCase_       ,lowerCAmelCase_							)
							read_in_q_k_v(lowerCAmelCase_       ,lowerCAmelCase_       ,lowerCAmelCase_							)

							# load HuggingFace model
							SCREAMING_SNAKE_CASE_	:							Optional[int]											=DeiTForImageClassificationWithTeacher(lowerCAmelCase_							).eval()
							model.load_state_dict(lowerCAmelCase_							)

							# Check outputs on an image, prepared by DeiTImageProcessor
							SCREAMING_SNAKE_CASE_	:							Dict											=int(
							    (256 / 224) * config.image_size							)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
							SCREAMING_SNAKE_CASE_	:							Dict											=DeiTImageProcessor(size=lowerCAmelCase_       ,crop_size=config.image_size							)
							SCREAMING_SNAKE_CASE_	:							Optional[Any]											=image_processor(images=prepare_img()       ,return_tensors='pt'							)
							SCREAMING_SNAKE_CASE_	:							Optional[Any]											=encoding["pixel_values"]
							SCREAMING_SNAKE_CASE_	:							List[str]											=model(lowerCAmelCase_							)

							SCREAMING_SNAKE_CASE_	:							List[str]											=timm_model(lowerCAmelCase_							)
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(lowerCAmelCase_       ,outputs.logits       ,atol=1e-3							)

							Path(lowerCAmelCase_							).mkdir(exist_ok=lowerCAmelCase_							)
							print(F"""Saving model {deit_name} 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						       =						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'''


import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm
      import torch_xla.debug.metrics as met







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     def __init__(   self			,     *lowerCamelCase			,     lowerCamelCase=None			,     lowerCamelCase=None			,     **lowerCamelCase		):


          '''simple docstring'''





          super().__init__(*lowerCamelCase			,     **lowerCamelCase		)
          __A	:       Tuple      =							eval_examples
          __A	:       List[str]      =							post_process_function


     def lowerCAmelCase__  (   self			,     lowerCamelCase=None			,     lowerCamelCase=None			,     lowerCamelCase=None			,     lowerCamelCase = "eval"		):


          '''simple docstring'''





          __A	:       Any      =							self.eval_dataset if eval_dataset is None else eval_dataset
          __A	:       Dict      =							self.get_eval_dataloader(lowerCamelCase		)
          __A	:       str      =							self.eval_examples if eval_examples is None else eval_examples

          # Temporarily disable metric computation, we will do it in the loop here.
          __A	:       Any      =							self.compute_metrics
          __A	:       Optional[int]      =							None
          __A	:       Tuple      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          __A	:       Dict      =							time.time()
          try:
               __A	:       List[str]      =							eval_loop(
                   lowerCamelCase			,     description="Evaluation"			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=lowerCamelCase			,     metric_key_prefix=lowerCamelCase			,     )
          finally:
               __A	:       List[str]      =							compute_metrics
          __A	:       List[Any]      =							self.args.eval_batch_size * self.args.world_size
          if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
               start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
          output.metrics.update(
              speed_metrics(
                  lowerCamelCase			,     lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size		)			,     )		)
          if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
               # Only the main node write the results by default
               __A	:       List[Any]      =							self.post_process_function(lowerCamelCase			,     lowerCamelCase			,     output.predictions		)
               __A	:       List[str]      =							self.compute_metrics(lowerCamelCase		)

               # Prefix all keys with metric_key_prefix + '_'
               for key in list(metrics.keys()		):
                    if not key.startswith(f"{metric_key_prefix}_"		):
                         __A	:       Union[str, Any]      =							metrics.pop(lowerCamelCase		)
               metrics.update(output.metrics		)
          else:
               __A	:       Any      =							output.metrics

          if self.args.should_log:
               # Only the main node log the results by default
               self.log(lowerCamelCase		)

          if self.args.tpu_metrics_debug or self.args.debug:
               # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
               xm.master_print(met.metrics_report()		)

          __A	:       str      =							self.callback_handler.on_evaluate(self.args			,     self.state			,     self.control			,     lowerCamelCase		)
          return metrics


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase=None			,     lowerCamelCase = "test"		):


          '''simple docstring'''





          __A	:       Tuple      =							self.get_test_dataloader(lowerCamelCase		)

          # Temporarily disable metric computation, we will do it in the loop here.
          __A	:       Union[str, Any]      =							self.compute_metrics
          __A	:       Union[str, Any]      =							None
          __A	:       Optional[Any]      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          __A	:       Tuple      =							time.time()
          try:
               __A	:       List[str]      =							eval_loop(
                   lowerCamelCase			,     description="Prediction"			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=lowerCamelCase			,     metric_key_prefix=lowerCamelCase			,     )
          finally:
               __A	:       int      =							compute_metrics
          __A	:       List[Any]      =							self.args.eval_batch_size * self.args.world_size
          if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
               start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
          output.metrics.update(
              speed_metrics(
                  lowerCamelCase			,     lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size		)			,     )		)

          if self.post_process_function is None or self.compute_metrics is None:
               return output

          __A	:       Dict      =							self.post_process_function(lowerCamelCase			,     lowerCamelCase			,     output.predictions			,     "predict"		)
          __A	:       Tuple      =							self.compute_metrics(lowerCamelCase		)

          # Prefix all keys with metric_key_prefix + '_'
          for key in list(metrics.keys()		):
               if not key.startswith(f"{metric_key_prefix}_"		):
                    __A	:       Any      =							metrics.pop(lowerCamelCase		)
          metrics.update(output.metrics		)
          return PredictionOutput(predictions=predictions.predictions			,     label_ids=predictions.label_ids			,     metrics=lowerCamelCase		)

