


'''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()
_UpperCamelCase 							=  logging.get_logger(__name__)


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE=False							):




     '''simple docstring'''





     __A	:       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"
          __A	:       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 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE=False							):




     '''simple docstring'''





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


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       str      =							dct.pop(SCREAMING_SNAKE_CASE							)
     __A	:       Any      =							val


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Union[str, Any]      =							"http://images.cocodataset.org/val2017/000000039769.jpg"
     __A	:       str      =							Image.open(requests.get(SCREAMING_SNAKE_CASE       ,					stream=SCREAMING_SNAKE_CASE							).raw							)
     return im


@torch.no_grad()
def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





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

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

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

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

     # Check outputs on an image, prepared by DeiTImageProcessor
     __A	:       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
     __A	:       Dict      =							DeiTImageProcessor(size=SCREAMING_SNAKE_CASE       ,					crop_size=config.image_size							)
     __A	:       Optional[Any]      =							image_processor(images=prepare_img()       ,					return_tensors="pt"							)
     __A	:       Optional[Any]      =							encoding["pixel_values"]
     __A	:       List[str]      =							model(SCREAMING_SNAKE_CASE							)

     __A	:       List[str]      =							timm_model(SCREAMING_SNAKE_CASE							)
     assert timm_logits.shape == outputs.logits.shape
     assert torch.allclose(SCREAMING_SNAKE_CASE       ,					outputs.logits       ,					atol=1E-3							)

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


if __name__ == "__main__":
      _UpperCamelCase 							=  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."""
      )

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





'''simple docstring'''


import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
      import jax

      from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
      from PIL import Image

      from transformers import BeitImageProcessor







class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase=100			,     lowerCamelCase=13			,     lowerCamelCase=30			,     lowerCamelCase=2			,     lowerCamelCase=3			,     lowerCamelCase=True			,     lowerCamelCase=True			,     lowerCamelCase=32			,     lowerCamelCase=5			,     lowerCamelCase=4			,     lowerCamelCase=37			,     lowerCamelCase="gelu"			,     lowerCamelCase=0.1			,     lowerCamelCase=0.1			,     lowerCamelCase=10			,     lowerCamelCase=0.02			,     lowerCamelCase=3			,     ):


          '''simple docstring'''





          __A	:       Tuple      =							parent
          __A	:       Dict      =							vocab_size
          __A	:       Union[str, Any]      =							batch_size
          __A	:       str      =							image_size
          __A	:       Optional[Any]      =							patch_size
          __A	:       Optional[Any]      =							num_channels
          __A	:       Optional[Any]      =							is_training
          __A	:       Any      =							use_labels
          __A	:       List[str]      =							hidden_size
          __A	:       Union[str, Any]      =							num_hidden_layers
          __A	:       Optional[Any]      =							num_attention_heads
          __A	:       Optional[int]      =							intermediate_size
          __A	:       Optional[int]      =							hidden_act
          __A	:       Dict      =							hidden_dropout_prob
          __A	:       str      =							attention_probs_dropout_prob
          __A	:       Optional[int]      =							type_sequence_label_size
          __A	:       Optional[int]      =							initializer_range

          # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          __A	:       Optional[int]      =							(image_size // patch_size) ** 2
          __A	:       Union[str, Any]      =							num_patches + 1


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

          __A	:       str      =							None
          if self.use_labels:
               __A	:       List[str]      =							ids_tensor([self.batch_size]			,     self.type_sequence_label_size		)

          __A	:       Dict      =							BeitConfig(
              vocab_size=self.vocab_size			,     image_size=self.image_size			,     patch_size=self.patch_size			,     num_channels=self.num_channels			,     hidden_size=self.hidden_size			,     num_hidden_layers=self.num_hidden_layers			,     num_attention_heads=self.num_attention_heads			,     intermediate_size=self.intermediate_size			,     hidden_act=self.hidden_act			,     hidden_dropout_prob=self.hidden_dropout_prob			,     attention_probs_dropout_prob=self.attention_probs_dropout_prob			,     is_decoder=lowerCamelCase			,     initializer_range=self.initializer_range			,     )

          return config, pixel_values, labels


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Optional[int]      =							FlaxBeitModel(config=lowerCamelCase		)
          __A	:       Optional[Any]      =							model(lowerCamelCase		)
          self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Dict      =							FlaxBeitForMaskedImageModeling(config=lowerCamelCase		)
          __A	:       Union[str, Any]      =							model(lowerCamelCase		)
          self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length - 1, self.vocab_size)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Any      =							self.type_sequence_label_size
          __A	:       List[str]      =							FlaxBeitForImageClassification(config=lowerCamelCase		)
          __A	:       Tuple      =							model(lowerCamelCase		)
          self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)		)

          # test greyscale images
          __A	:       Tuple      =							1
          __A	:       Optional[Any]      =							FlaxBeitForImageClassification(lowerCamelCase		)

          __A	:       Union[str, Any]      =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
          __A	:       str      =							model(lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Tuple      =							self.prepare_config_and_inputs()
          (
              (
              __A
          )  ,(
              __A
          )  ,(
              __A
          )  ,
          )	:       Dict      =							config_and_inputs
          __A	:       Dict      =							{"pixel_values": pixel_values}
          return config, inputs_dict







@require_flax
class 							__magic_name__	(       lowerCAmelCase    ,		unittest.TestCase ):

     """simple docstring"""






     lowerCamelCase__       						=				(
         (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
     )


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							FlaxBeitModelTester(self		)
          __A	:       List[Any]      =							ConfigTester(self			,     config_class=lowerCamelCase			,     has_text_modality=lowerCamelCase			,     hidden_size=37		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.config_tester.run_common_tests()


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A  ,__A	:       Optional[int]      =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A	:       int      =							model_class(lowerCamelCase		)
               __A	:       Dict      =							inspect.signature(model.__call__		)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __A	:       List[str]      =							[*signature.parameters.keys()]

               __A	:       Optional[Any]      =							["pixel_values"]
               self.assertListEqual(arg_names[:1]			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A  ,__A	:       Optional[int]      =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__		):
                    __A	:       Optional[int]      =							self._prepare_for_class(lowerCamelCase			,     lowerCamelCase		)
                    __A	:       Optional[int]      =							model_class(lowerCamelCase		)

                    @jax.jit
                    def model_jitted(lowerCamelCase			,     **lowerCamelCase		):
                         return model(pixel_values=lowerCamelCase			,     **lowerCamelCase		)

                    with self.subTest("JIT Enabled"		):
                         __A	:       Tuple      =							model_jitted(**lowerCamelCase		).to_tuple()

                    with self.subTest("JIT Disabled"		):
                         with jax.disable_jit():
                              __A	:       Dict      =							model_jitted(**lowerCamelCase		).to_tuple()

                    self.assertEqual(len(lowerCamelCase		)			,     len(lowerCamelCase		)		)
                    for jitted_output, output in zip(lowerCamelCase			,     lowerCamelCase		):
                         self.assertEqual(jitted_output.shape			,     output.shape		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[str]      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCamelCase		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for model_class_name in self.all_model_classes:
               __A	:       Any      =							model_class_name.from_pretrained("microsoft/beit-base-patch16-224"		)
               __A	:       Optional[int]      =							model(np.ones((1, 3, 224, 224)		)		)
               self.assertIsNotNone(lowerCamelCase		)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Dict      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
     return image







@require_vision
@require_flax
class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     @cached_property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"		) if is_vision_available() else None


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"		)

          __A	:       List[Any]      =							self.default_image_processor
          __A	:       Dict      =							prepare_img()
          __A	:       str      =							image_processor(images=lowerCamelCase			,     return_tensors="np"		).pixel_values

          # prepare bool_masked_pos
          __A	:       List[Any]      =							np.ones((1, 196)			,     dtype=lowerCamelCase		)

          # forward pass
          __A	:       Union[str, Any]      =							model(pixel_values=lowerCamelCase			,     bool_masked_pos=lowerCamelCase		)
          __A	:       Dict      =							outputs.logits

          # verify the logits
          __A	:       int      =							(1, 196, 8192)
          self.assertEqual(logits.shape			,     lowerCamelCase		)

          __A	:       Optional[int]      =							np.array(
              [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]		)

          self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]			,     lowerCamelCase			,     atol=1E-2		)		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"		)

          __A	:       Any      =							self.default_image_processor
          __A	:       List[Any]      =							prepare_img()
          __A	:       List[Any]      =							image_processor(images=lowerCamelCase			,     return_tensors="np"		)

          # forward pass
          __A	:       int      =							model(**lowerCamelCase		)
          __A	:       Any      =							outputs.logits

          # verify the logits
          __A	:       str      =							(1, 1000)
          self.assertEqual(logits.shape			,     lowerCamelCase		)

          __A	:       int      =							np.array([-1.2385, -1.0987, -1.0108]		)

          self.assertTrue(np.allclose(logits[0, :3]			,     lowerCamelCase			,     atol=1E-4		)		)

          __A	:       Optional[int]      =							281
          self.assertEqual(logits.argmax(-1		).item()			,     lowerCamelCase		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"		)

          __A	:       Optional[int]      =							self.default_image_processor
          __A	:       Tuple      =							prepare_img()
          __A	:       List[str]      =							image_processor(images=lowerCamelCase			,     return_tensors="np"		)

          # forward pass
          __A	:       List[Any]      =							model(**lowerCamelCase		)
          __A	:       Dict      =							outputs.logits

          # verify the logits
          __A	:       Union[str, Any]      =							(1, 2_1841)
          self.assertEqual(logits.shape			,     lowerCamelCase		)

          __A	:       Tuple      =							np.array([1.6881, -0.2787, 0.5901]		)

          self.assertTrue(np.allclose(logits[0, :3]			,     lowerCamelCase			,     atol=1E-4		)		)

          __A	:       Optional[int]      =							2396
          self.assertEqual(logits.argmax(-1		).item()			,     lowerCamelCase		)

