




'''simple docstring'''



import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TFEfficientFormerForImageClassification,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerModel,
      )
      from transformers.models.efficientformer.modeling_tf_efficientformer import (
          TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      )


if is_vision_available():
      from PIL import Image

      from transformers import EfficientFormerImageProcessor






class 				_a							:





       '''simple docstring'''






       def __init__(	self,				A,				A = 13,				A = 64,				A = 2,				A = 3,				A = 3,				A = True,				A = True,				A = 128,				A=[16, 32, 64, 128],				A = 7,				A = 4,				A = 37,				A = "gelu",				A = 0.1,				A = 0.1,				A = 10,				A = 0.02,				A = 2,				A = 1,				A = 128,				A = [2, 2, 2, 2],				A = 2,				A = 2,				):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		parent
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		batch_size
             SCREAMING_SNAKE_CASE					:	Any   =		image_size
             SCREAMING_SNAKE_CASE					:	List[Any]   =		patch_size
             SCREAMING_SNAKE_CASE					:	List[str]   =		num_channels
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		is_training
             SCREAMING_SNAKE_CASE					:	List[str]   =		use_labels
             SCREAMING_SNAKE_CASE					:	Any   =		hidden_size
             SCREAMING_SNAKE_CASE					:	Tuple   =		num_hidden_layers
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		num_attention_heads
             SCREAMING_SNAKE_CASE					:	Dict   =		intermediate_size
             SCREAMING_SNAKE_CASE					:	List[str]   =		hidden_act
             SCREAMING_SNAKE_CASE					:	List[Any]   =		hidden_dropout_prob
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		attention_probs_dropout_prob
             SCREAMING_SNAKE_CASE					:	Any   =		type_sequence_label_size
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		initializer_range
             SCREAMING_SNAKE_CASE					:	str   =		encoder_stride
             SCREAMING_SNAKE_CASE					:	Any   =		num_attention_outputs
             SCREAMING_SNAKE_CASE					:	int   =		embed_dim
             SCREAMING_SNAKE_CASE					:	Dict   =		embed_dim + 1
             SCREAMING_SNAKE_CASE					:	Dict   =		resolution
             SCREAMING_SNAKE_CASE					:	List[Any]   =		depths
             SCREAMING_SNAKE_CASE					:	Any   =		hidden_sizes
             SCREAMING_SNAKE_CASE					:	List[str]   =		dim
             SCREAMING_SNAKE_CASE					:	int   =		mlp_expansion_ratio







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[str]   =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

             SCREAMING_SNAKE_CASE					:	Optional[int]   =		None
             if self.use_labels:
                   SCREAMING_SNAKE_CASE					:	Optional[int]   =		ids_tensor([self.batch_size],				self.type_sequence_label_size   )

             SCREAMING_SNAKE_CASE					:	Any   =		self.get_config()

             return config, pixel_values, labels







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             return EfficientFormerConfig(
                 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=A,				initializer_range=self.initializer_range,				encoder_stride=self.encoder_stride,				resolution=self.resolution,				depths=self.depths,				hidden_sizes=self.hidden_sizes,				dim=self.dim,				mlp_expansion_ratio=self.mlp_expansion_ratio,				)







       def     UpperCamelCase_				(	self,				A,				A,				A   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		TFEfficientFormerModel(config=A   )
             SCREAMING_SNAKE_CASE					:	int   =		model(A,				training=A   )
             self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, self.seq_length, self.hidden_size)   )







       def     UpperCamelCase_				(	self,				A,				A,				A   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Tuple   =		self.type_sequence_label_size
             SCREAMING_SNAKE_CASE					:	List[str]   =		TFEfficientFormerForImageClassification(A   )
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		model(A,				labels=A,				training=A   )
             self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.type_sequence_label_size)   )

             # test greyscale images
             SCREAMING_SNAKE_CASE					:	Tuple   =		1
             SCREAMING_SNAKE_CASE					:	Dict   =		TFEfficientFormerForImageClassification(A   )

             SCREAMING_SNAKE_CASE					:	List[Any]   =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
             SCREAMING_SNAKE_CASE					:	List[str]   =		model(A,				labels=A   )
             self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.type_sequence_label_size)   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		self.prepare_config_and_inputs()
             SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE					:	Any   =		config_and_inputs
             SCREAMING_SNAKE_CASE					:	str   =		{'pixel_values': pixel_values}
             return config, inputs_dict






@require_tf
class 				_a							(       SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	unittest.TestCase      ):





       '''simple docstring'''






       A		:							List[str]								=		(
           (
               TFEfficientFormerModel,
               TFEfficientFormerForImageClassificationWithTeacher,
               TFEfficientFormerForImageClassification,
           )
           if is_tf_available()
           else ()
       )
       A		:							Optional[Any]								=		(
           {
               '''feature-extraction''': TFEfficientFormerModel,
               '''image-classification''': (
                   TFEfficientFormerForImageClassification,
                   TFEfficientFormerForImageClassificationWithTeacher,
               ),
           }
           if is_tf_available()
           else {}
       )

       A		:							List[str]								=		False

       A		:							Tuple								=		False
       A		:							Union[str, Any]								=		False
       A		:							Union[str, Any]								=		False
       A		:							List[Any]								=		False







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[Any]   =		TFEfficientFormerModelTester(self   )
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		ConfigTester(
                 self,				config_class=A,				has_text_modality=A,				hidden_size=37   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             self.config_tester.run_common_tests()







       @unittest.skip(reason='EfficientFormer does not use inputs_embeds'   )
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             pass







       @unittest.skip(reason='EfficientFormer does not support input and output embeddings'   )
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             pass







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE					:	List[str]   =		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   SCREAMING_SNAKE_CASE					:	Optional[Any]   =		model_class(A   )
                   SCREAMING_SNAKE_CASE					:	Dict   =		inspect.signature(model.call   )
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   SCREAMING_SNAKE_CASE					:	Any   =		[*signature.parameters.keys()]

                   SCREAMING_SNAKE_CASE					:	Tuple   =		['pixel_values']
                   self.assertListEqual(arg_names[:1],				A   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             def check_hidden_states_output(A,				A,				A   ):
                   SCREAMING_SNAKE_CASE					:	Dict   =		model_class(A   )

                   SCREAMING_SNAKE_CASE					:	List[Any]   =		model(**self._prepare_for_class(A,				A   ),				training=A   )
                   SCREAMING_SNAKE_CASE					:	List[Any]   =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                   SCREAMING_SNAKE_CASE					:	int   =		getattr(
                       self.model_tester,				'expected_num_hidden_layers',				self.model_tester.num_hidden_layers + 1   )
                   self.assertEqual(len(A   ),				A   )

                   if hasattr(self.model_tester,				'encoder_seq_length'   ):
                         SCREAMING_SNAKE_CASE					:	int   =		self.model_tester.encoder_seq_length
                         if hasattr(self.model_tester,				'chunk_length'   ) and self.model_tester.chunk_length > 1:
                               SCREAMING_SNAKE_CASE					:	Tuple   =		seq_length * self.model_tester.chunk_length
                   else:
                         SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		self.model_tester.seq_length

                   self.assertListEqual(
                       list(hidden_states[-1].shape[-2:]   ),				[seq_length, self.model_tester.hidden_size],				)

                   if config.is_encoder_decoder:
                         SCREAMING_SNAKE_CASE					:	Optional[int]   =		outputs.decoder_hidden_states

                         self.asseretIsInstance(A,				(list, tuple)   )
                         self.assertEqual(len(A   ),				A   )
                         SCREAMING_SNAKE_CASE					:	str   =		getattr(self.model_tester,				'seq_length',				A   )
                         SCREAMING_SNAKE_CASE					:	str   =		getattr(self.model_tester,				'decoder_seq_length',				A   )

                         self.assertListEqual(
                             list(hidden_states[-1].shape[-2:]   ),				[decoder_seq_length, self.model_tester.hidden_size],				)

             SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE					:	Tuple   =		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   SCREAMING_SNAKE_CASE					:	List[str]   =		True
                   check_hidden_states_output(A,				A,				A   )

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   SCREAMING_SNAKE_CASE					:	Optional[int]   =		True

                   check_hidden_states_output(A,				A,				A   )







       def     UpperCamelCase_				(	self,				A,				A,				A=False   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	int   =		super()._prepare_for_class(A,				A,				return_labels=A   )

             if return_labels:
                   if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
                         del inputs_dict["labels"]

             return inputs_dict







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	str   =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*A   )







       @unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'   )
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Dict   =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_image_modeling(*A   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[Any]   =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*A   )







       @slow
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   SCREAMING_SNAKE_CASE					:	List[Any]   =		TFEfficientFormerModel.from_pretrained(A   )
                   self.assertIsNotNone(A   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE					:	List[Any]   =		self.model_tester.prepare_config_and_inputs_for_common()
             SCREAMING_SNAKE_CASE					:	Any   =		True

             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		getattr(self.model_tester,				'seq_length',				A   )
             SCREAMING_SNAKE_CASE					:	Dict   =		getattr(self.model_tester,				'encoder_seq_length',				A   )
             SCREAMING_SNAKE_CASE					:	int   =		getattr(self.model_tester,				'key_length',				A   )
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		getattr(self.model_tester,				'chunk_length',				A   )

             if chunk_length is not None and hasattr(self.model_tester,				'num_hashes'   ):
                   SCREAMING_SNAKE_CASE					:	Optional[int]   =		encoder_seq_length * self.model_tester.num_hashes

             for model_class in self.all_model_classes:
                   SCREAMING_SNAKE_CASE					:	Any   =		True
                   SCREAMING_SNAKE_CASE					:	Tuple   =		False
                   SCREAMING_SNAKE_CASE					:	Optional[Any]   =		True
                   SCREAMING_SNAKE_CASE					:	str   =		model_class(A   )

                   SCREAMING_SNAKE_CASE					:	Dict   =		model(**self._prepare_for_class(A,				A   ),				training=A   )
                   SCREAMING_SNAKE_CASE					:	Optional[Any]   =		outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                   self.assertEqual(len(A   ),				self.model_tester.num_attention_outputs   )

                   # check that output_attentions also work using config
                   del inputs_dict["output_attentions"]
                   SCREAMING_SNAKE_CASE					:	List[Any]   =		True
                   SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		model_class(A   )
                   SCREAMING_SNAKE_CASE					:	Tuple   =		model(**self._prepare_for_class(A,				A   ),				training=A   )

                   SCREAMING_SNAKE_CASE					:	Optional[int]   =		outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                   self.assertEqual(len(A   ),				self.model_tester.num_attention_outputs   )

                   if chunk_length is not None:
                         self.assertListEqual(
                             list(attentions[0].shape[-4:]   ),				[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],				)
                   else:
                         self.assertListEqual(
                             list(attentions[0].shape[-3:]   ),				[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],				)







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







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

             for model_class in self.all_model_classes:
                   # Prepare our model
                   SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		model_class(A   )
                   # These are maximally general inputs for the model, with multiple None dimensions
                   # Hopefully this will catch any conditionals that fail for flexible shapes
                   SCREAMING_SNAKE_CASE					:	str   =		{
                       key: tf.keras.Input(shape=val.shape[1:],				dtype=val.dtype,				name=A   )
                       for key, val in model.input_signature.items()
                       if key in model.dummy_inputs
                   }
                   SCREAMING_SNAKE_CASE					:	Optional[Any]   =		model(A   )
                   self.assertTrue(outputs_dict is not None   )




def    lowercase__(       ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					:	List[str]   =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
      return image






@require_tf
@require_vision
class 				_a							(       unittest.TestCase      ):





       '''simple docstring'''






       @cached_property
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             return (
                 EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'   )
                 if is_vision_available()
                 else None
             )







       @slow
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	int   =		TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'   )
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		self.default_image_processor
             SCREAMING_SNAKE_CASE					:	List[str]   =		prepare_img()
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		image_processor(images=A,				return_tensors='tf'   )
             # forward pass
             SCREAMING_SNAKE_CASE					:	List[str]   =		model(**A,				training=A   )
             # verify the logits
             SCREAMING_SNAKE_CASE					:	Dict   =		tf.TensorShape((1, 1_000)   )
             self.assertEqual(outputs.logits.shape,				A   )
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		tf.constant([-0.05_55, 0.48_25, -0.08_52]   )
             self.assertTrue(np.allclose(outputs.logits[0, :3],				A,				atol=1E-4   )   )







       @slow
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Any   =		TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
                 'snap-research/efficientformer-l1-300'   )
             SCREAMING_SNAKE_CASE					:	Dict   =		self.default_image_processor
             SCREAMING_SNAKE_CASE					:	List[str]   =		prepare_img()
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		image_processor(images=A,				return_tensors='tf'   )
             # forward pass
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		model(**A,				training=A   )
             # verify the logits
             SCREAMING_SNAKE_CASE					:	Tuple   =		tf.TensorShape((1, 1_000)   )
             self.assertEqual(outputs.logits.shape,				A   )
             SCREAMING_SNAKE_CASE					:	List[str]   =		tf.constant([-0.13_12, 0.43_53, -1.04_99]   )
             self.assertTrue(np.allclose(outputs.logits[0, :3],				A,				atol=1E-4   )   )



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()
A   :     str       =				logging.get_logger(__name__)


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase=False ):
      SCREAMING_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"
            SCREAMING_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    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase=False ):
      for i in range(config.num_hidden_layers ):
            if base_model:
                  SCREAMING_SNAKE_CASE_      =     ""
            else:
                  SCREAMING_SNAKE_CASE_      =     "deit."
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            SCREAMING_SNAKE_CASE_      =     state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
            SCREAMING_SNAKE_CASE_      =     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_      =     in_proj_weight[
                : config.hidden_size, :
            ]
            SCREAMING_SNAKE_CASE_      =     in_proj_bias[: config.hidden_size]
            SCREAMING_SNAKE_CASE_      =     in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE_      =     in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            SCREAMING_SNAKE_CASE_      =     in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE_      =     in_proj_bias[-config.hidden_size :]


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     dct.pop(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     val


def    a__       (						):
      SCREAMING_SNAKE_CASE_      =     "http://images.cocodataset.org/val2017/000000039769.jpg"
      SCREAMING_SNAKE_CASE_      =     Image.open(requests.get(__UpperCamelCase   ,					stream=__UpperCamelCase ).raw )
      return im


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

      # load original model from timm
      SCREAMING_SNAKE_CASE_      =     timm.create_model(__UpperCamelCase   ,					pretrained=__UpperCamelCase )
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      SCREAMING_SNAKE_CASE_      =     timm_model.state_dict()
      SCREAMING_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
      SCREAMING_SNAKE_CASE_      =     DeiTForImageClassificationWithTeacher(__UpperCamelCase ).eval()
      model.load_state_dict(__UpperCamelCase )

      # Check outputs on an image, prepared by DeiTImageProcessor
      SCREAMING_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
      SCREAMING_SNAKE_CASE_      =     DeiTImageProcessor(size=__UpperCamelCase   ,					crop_size=config.image_size )
      SCREAMING_SNAKE_CASE_      =     image_processor(images=prepare_img()   ,					return_tensors="pt" )
      SCREAMING_SNAKE_CASE_      =     encoding["pixel_values"]
      SCREAMING_SNAKE_CASE_      =     model(__UpperCamelCase )

      SCREAMING_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__":
      A   :     Tuple       =				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."
      )

      A   :     Dict       =				parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


