



"""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 :int   ,  lowercase_ :int   ,  lowercase_ :int = 13   ,  lowercase_ :int = 64   ,  lowercase_ :int = 2   ,  lowercase_ :int = 3   ,  lowercase_ :int = 3   ,  lowercase_ :bool = True   ,  lowercase_ :bool = True   ,  lowercase_ :int = 1_28   ,  lowercase_ :Union[str, Any]=[16, 32, 64, 1_28]   ,  lowercase_ :int = 7   ,  lowercase_ :int = 4   ,  lowercase_ :int = 37   ,  lowercase_ :str = "gelu"   ,  lowercase_ :float = 0.1   ,  lowercase_ :float = 0.1   ,  lowercase_ :int = 10   ,  lowercase_ :float = 0.02   ,  lowercase_ :int = 2   ,  lowercase_ :int = 1   ,  lowercase_ :int = 1_28   ,  lowercase_ :List[int] = [2, 2, 2, 2]   ,  lowercase_ :int = 2   ,  lowercase_ :int = 2   ,  )				->			str:
												UpperCAmelCase             = parent
												UpperCAmelCase             = batch_size
												UpperCAmelCase             = image_size
												UpperCAmelCase             = patch_size
												UpperCAmelCase             = num_channels
												UpperCAmelCase             = is_training
												UpperCAmelCase             = use_labels
												UpperCAmelCase             = hidden_size
												UpperCAmelCase             = num_hidden_layers
												UpperCAmelCase             = num_attention_heads
												UpperCAmelCase             = intermediate_size
												UpperCAmelCase             = hidden_act
												UpperCAmelCase             = hidden_dropout_prob
												UpperCAmelCase             = attention_probs_dropout_prob
												UpperCAmelCase             = type_sequence_label_size
												UpperCAmelCase             = initializer_range
												UpperCAmelCase             = encoder_stride
												UpperCAmelCase             = num_attention_outputs
												UpperCAmelCase             = embed_dim
												UpperCAmelCase             = embed_dim + 1
												UpperCAmelCase             = resolution
												UpperCAmelCase             = depths
												UpperCAmelCase             = hidden_sizes
												UpperCAmelCase             = dim
												UpperCAmelCase             = mlp_expansion_ratio






						def 	UpperCAmelCase__     (					self :List[Any]					)				->			List[str]:
												UpperCAmelCase             = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

												UpperCAmelCase             = None
												if self.use_labels:
																		UpperCAmelCase             = ids_tensor([self.batch_size]   ,  self.type_sequence_label_size					)

												UpperCAmelCase             = self.get_config()

												return config, pixel_values, labels






						def 	UpperCAmelCase__     (					self :List[Any]					)				->			Optional[int]:
												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=lowercase_   ,  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 :Optional[int]   ,  lowercase_ :Union[str, Any]   ,  lowercase_ :List[str]   ,  lowercase_ :Any					)				->			str:
												UpperCAmelCase             = TFEfficientFormerModel(config=lowercase_					)
												UpperCAmelCase             = model(lowercase_   ,  training=lowercase_					)
												self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, self.seq_length, self.hidden_size)					)






						def 	UpperCAmelCase__     (					self :List[Any]   ,  lowercase_ :str   ,  lowercase_ :List[str]   ,  lowercase_ :Optional[int]					)				->			int:
												UpperCAmelCase             = self.type_sequence_label_size
												UpperCAmelCase             = TFEfficientFormerForImageClassification(lowercase_					)
												UpperCAmelCase             = model(lowercase_   ,  labels=lowercase_   ,  training=lowercase_					)
												self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.type_sequence_label_size)					)

												# test greyscale images
												UpperCAmelCase             = 1
												UpperCAmelCase             = TFEfficientFormerForImageClassification(lowercase_					)

												UpperCAmelCase             = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
												UpperCAmelCase             = model(lowercase_   ,  labels=lowercase_					)
												self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.type_sequence_label_size)					)






						def 	UpperCAmelCase__     (					self :Tuple					)				->			str:
												UpperCAmelCase             = self.prepare_config_and_inputs()
												UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase             = config_and_inputs
												UpperCAmelCase             = {'pixel_values': pixel_values}
												return config, inputs_dict







@require_tf
class       A_			(     SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   unittest.TestCase				):



						"""simple docstring"""
						__UpperCamelCase						=							(
						    (
						        TFEfficientFormerModel,
						        TFEfficientFormerForImageClassificationWithTeacher,
						        TFEfficientFormerForImageClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						__UpperCamelCase						=							(
						    {
						        """feature-extraction""": TFEfficientFormerModel,
						        """image-classification""": (
						            TFEfficientFormerForImageClassification,
						            TFEfficientFormerForImageClassificationWithTeacher,
						        ),
						    }
						    if is_tf_available()
						    else {}
						)

						__UpperCamelCase						=							False

						__UpperCamelCase						=							False
						__UpperCamelCase						=							False
						__UpperCamelCase						=							False
						__UpperCamelCase						=							False






						def 	UpperCAmelCase__     (					self :List[Any]					)				->			int:
												UpperCAmelCase             = TFEfficientFormerModelTester(self					)
												UpperCAmelCase             = ConfigTester(
												    self   ,  config_class=lowercase_   ,  has_text_modality=lowercase_   ,  hidden_size=37					)






						def 	UpperCAmelCase__     (					self :List[str]					)				->			Optional[int]:
												self.config_tester.run_common_tests()






						@unittest.skip(reason='EfficientFormer does not use inputs_embeds'					)
						def 	UpperCAmelCase__     (					self :Tuple					)				->			Dict:
												pass






						@unittest.skip(reason='EfficientFormer does not support input and output embeddings'					)
						def 	UpperCAmelCase__     (					self :Dict					)				->			Any:
												pass






						def 	UpperCAmelCase__     (					self :Tuple					)				->			int:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()

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

																		UpperCAmelCase             = ['pixel_values']
																		self.assertListEqual(arg_names[:1]   ,  lowercase_					)






						def 	UpperCAmelCase__     (					self :Dict					)				->			str:
												def check_hidden_states_output(lowercase_ :Union[str, Any]   ,  lowercase_ :Optional[Any]   ,  lowercase_ :int					):
																		UpperCAmelCase             = model_class(lowercase_					)

																		UpperCAmelCase             = model(**self._prepare_for_class(lowercase_   ,  lowercase_					)   ,  training=lowercase_					)
																		UpperCAmelCase             = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		UpperCAmelCase             = getattr(
																		    self.model_tester   ,  'expected_num_hidden_layers'   ,  self.model_tester.num_hidden_layers + 1					)
																		self.assertEqual(len(lowercase_					)   ,  lowercase_					)

																		if hasattr(self.model_tester   ,  'encoder_seq_length'					):
																								UpperCAmelCase             = self.model_tester.encoder_seq_length
																								if hasattr(self.model_tester   ,  'chunk_length'					) and self.model_tester.chunk_length > 1:
																														UpperCAmelCase             = seq_length * self.model_tester.chunk_length
																		else:
																								UpperCAmelCase             = 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:
																								UpperCAmelCase             = outputs.decoder_hidden_states

																								self.asseretIsInstance(lowercase_   ,  (list, tuple)					)
																								self.assertEqual(len(lowercase_					)   ,  lowercase_					)
																								UpperCAmelCase             = getattr(self.model_tester   ,  'seq_length'   ,  lowercase_					)
																								UpperCAmelCase             = getattr(self.model_tester   ,  'decoder_seq_length'   ,  lowercase_					)

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

												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		UpperCAmelCase             = True
																		check_hidden_states_output(lowercase_   ,  lowercase_   ,  lowercase_					)

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		UpperCAmelCase             = True

																		check_hidden_states_output(lowercase_   ,  lowercase_   ,  lowercase_					)






						def 	UpperCAmelCase__     (					self :List[Any]   ,  lowercase_ :Union[str, Any]   ,  lowercase_ :int   ,  lowercase_ :Tuple=False					)				->			Optional[int]:
												UpperCAmelCase             = super()._prepare_for_class(lowercase_   ,  lowercase_   ,  return_labels=lowercase_					)

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

												return inputs_dict






						def 	UpperCAmelCase__     (					self :Tuple					)				->			Any:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*lowercase_					)






						@unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'					)
						def 	UpperCAmelCase__     (					self :Tuple					)				->			List[Any]:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_image_modeling(*lowercase_					)






						def 	UpperCAmelCase__     (					self :Dict					)				->			Union[str, Any]:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*lowercase_					)






						@slow
						def 	UpperCAmelCase__     (					self :Any					)				->			Optional[int]:
												for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCAmelCase             = TFEfficientFormerModel.from_pretrained(lowercase_					)
																		self.assertIsNotNone(lowercase_					)






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			List[str]:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase             = True

												UpperCAmelCase             = getattr(self.model_tester   ,  'seq_length'   ,  lowercase_					)
												UpperCAmelCase             = getattr(self.model_tester   ,  'encoder_seq_length'   ,  lowercase_					)
												UpperCAmelCase             = getattr(self.model_tester   ,  'key_length'   ,  lowercase_					)
												UpperCAmelCase             = getattr(self.model_tester   ,  'chunk_length'   ,  lowercase_					)

												if chunk_length is not None and hasattr(self.model_tester   ,  'num_hashes'					):
																		UpperCAmelCase             = encoder_seq_length * self.model_tester.num_hashes

												for model_class in self.all_model_classes:
																		UpperCAmelCase             = True
																		UpperCAmelCase             = False
																		UpperCAmelCase             = True
																		UpperCAmelCase             = model_class(lowercase_					)

																		UpperCAmelCase             = model(**self._prepare_for_class(lowercase_   ,  lowercase_					)   ,  training=lowercase_					)
																		UpperCAmelCase             = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
																		self.assertEqual(len(lowercase_					)   ,  self.model_tester.num_attention_outputs					)

																		# check that output_attentions also work using config
																		del inputs_dict["output_attentions"]
																		UpperCAmelCase             = True
																		UpperCAmelCase             = model_class(lowercase_					)
																		UpperCAmelCase             = model(**self._prepare_for_class(lowercase_   ,  lowercase_					)   ,  training=lowercase_					)

																		UpperCAmelCase             = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
																		self.assertEqual(len(lowercase_					)   ,  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 :int					)				->			List[Any]:
												# We use a simplified version of this test for EfficientFormer because it requires training=False
												# and Keras refuses to let us force that during functional construction
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()

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


def  _lowerCAmelCase      (   ):
						UpperCAmelCase             = 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 :Optional[int]					)				->			List[Any]:
												return (
												    EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'					)
												    if is_vision_available()
												    else None
												)






						@slow
						def 	UpperCAmelCase__     (					self :str					)				->			Optional[Any]:
												UpperCAmelCase             = TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'					)
												UpperCAmelCase             = self.default_image_processor
												UpperCAmelCase             = prepare_img()
												UpperCAmelCase             = image_processor(images=lowercase_   ,  return_tensors='tf'					)
												# forward pass
												UpperCAmelCase             = model(**lowercase_   ,  training=lowercase_					)
												# verify the logits
												UpperCAmelCase             = tf.TensorShape((1, 10_00)					)
												self.assertEqual(outputs.logits.shape   ,  lowercase_					)
												UpperCAmelCase             = tf.constant([-0.0555, 0.4825, -0.0852]					)
												self.assertTrue(np.allclose(outputs.logits[0, :3]   ,  lowercase_   ,  atol=1E-4					)					)






						@slow
						def 	UpperCAmelCase__     (					self :int					)				->			Optional[Any]:
												UpperCAmelCase             = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
												    'snap-research/efficientformer-l1-300'					)
												UpperCAmelCase             = self.default_image_processor
												UpperCAmelCase             = prepare_img()
												UpperCAmelCase             = image_processor(images=lowercase_   ,  return_tensors='tf'					)
												# forward pass
												UpperCAmelCase             = model(**lowercase_   ,  training=lowercase_					)
												# verify the logits
												UpperCAmelCase             = tf.TensorShape((1, 10_00)					)
												self.assertEqual(outputs.logits.shape   ,  lowercase_					)
												UpperCAmelCase             = tf.constant([-0.1312, 0.4353, -1.0499]					)
												self.assertTrue(np.allclose(outputs.logits[0, :3]   ,  lowercase_   ,  atol=1E-4					)					)










"""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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__    :  Dict							    =	logging.get_logger(__name__)




def __lowercase			(							_a		,			_a=False    ):
    snake_case_ :     List[str]				=	[]

    # fmt: off
    # stem:
    rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''')    )
    rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''')    )

    rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''')    )
    rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''')    )

    # backbone
    rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''')    )
    rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''')    )
    rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''')    )

    for stage_idx in range(len(config.backbone_config.depths    )    ):
        for layer_idx in range(config.backbone_config.depths[stage_idx]    ):
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")    )

        rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")    )
        rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")    )
        rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")    )

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

    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 "vit" from all keys that start with "vit"
        snake_case_ :     Optional[int]				=	[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''    ) else pair for pair in rename_keys]
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ('''norm.weight''', '''vit.layernorm.weight'''),
                ('''norm.bias''', '''vit.layernorm.bias'''),
                ('''head.weight''', '''classifier.weight'''),
                ('''head.bias''', '''classifier.bias'''),
            ]    )
    # fmt: on

    return rename_keys




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




def __lowercase			(							_a    ):
    snake_case_ :     Dict				=	['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(_a		,			_a    )




def __lowercase			(							_a		,			_a		,			_a    ):
    snake_case_ :     Union[str, Any]				=	dct.pop(_a    )
    snake_case_ :     Union[str, Any]				=	val




def __lowercase			(							):
    snake_case_ :     Any				=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    snake_case_ :     Tuple				=	Image.open(requests.get(_a		,			stream=_a    ).raw    )
    return im




@torch.no_grad()
def __lowercase			(							_a		,			_a		,			_a=False    ):
    snake_case_ :     str				=	BitConfig(
        global_padding='''same'''		,			layer_type='''bottleneck'''		,			depths=(3, 4, 9)		,			out_features=['''stage3''']		,			embedding_dynamic_padding=_a		,			)
    snake_case_ :     Tuple				=	ViTHybridConfig(backbone_config=_a		,			image_size=384		,			num_labels=1_000    )
    snake_case_ :     int				=	False

    # load original model from timm
    snake_case_ :     str				=	timm.create_model(_a		,			pretrained=_a    )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    snake_case_ :     Any				=	timm_model.state_dict()
    if base_model:
        remove_classification_head_(_a    )
    snake_case_ :     int				=	create_rename_keys(_a		,			_a    )
    for src, dest in rename_keys:
        rename_key(_a		,			_a		,			_a    )
    read_in_q_k_v(_a		,			_a		,			_a    )

    snake_case_ :     Optional[Any]				=	'''huggingface/label-files'''
    snake_case_ :     Any				=	'''imagenet-1k-id2label.json'''
    snake_case_ :     Dict				=	json.load(open(hf_hub_download(_a		,			_a		,			repo_type='''dataset'''    )		,			'''r'''    )    )
    snake_case_ :     Dict				=	{int(_a    ): v for k, v in idalabel.items()}
    snake_case_ :     Optional[int]				=	idalabel
    snake_case_ :     Optional[Any]				=	{v: k for k, v in idalabel.items()}

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        snake_case_ :     Optional[Any]				=	ViTHybridModel(_a    ).eval()
    else:
        snake_case_ :     Any				=	ViTHybridForImageClassification(_a    ).eval()
    model.load_state_dict(_a    )

    # create image processor
    snake_case_ :     Optional[Any]				=	create_transform(**resolve_data_config({}		,			model=_a    )    )
    snake_case_ :     List[Any]				=	transform.transforms

    snake_case_ :     Optional[Any]				=	{
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    snake_case_ :     List[Any]				=	ViTHybridImageProcessor(
        do_resize=_a		,			size={'''shortest_edge''': timm_transforms[0].size}		,			resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,			do_center_crop=_a		,			crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}		,			do_normalize=_a		,			image_mean=timm_transforms[-1].mean.tolist()		,			image_std=timm_transforms[-1].std.tolist()		,			)

    snake_case_ :     Optional[int]				=	prepare_img()
    snake_case_ :     Optional[int]				=	transform(_a    ).unsqueeze(0    )
    snake_case_ :     int				=	processor(_a		,			return_tensors='''pt'''    ).pixel_values

    # verify pixel values
    assert torch.allclose(_a		,			_a    )

    # verify logits
    with torch.no_grad():
        snake_case_ :     List[str]				=	model(_a    )
        snake_case_ :     Any				=	outputs.logits

    print('''Predicted class:'''		,			logits.argmax(-1    ).item()    )
    if base_model:
        snake_case_ :     Optional[Any]				=	timm_model.forward_features(_a    )
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(_a		,			outputs.pooler_output		,			atol=1E-3    )
    else:
        snake_case_ :     int				=	timm_model(_a    )
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(_a		,			outputs.logits		,			atol=1E-3    )
    print('''Looks ok!'''    )

    if pytorch_dump_folder_path is not None:
        Path(_a    ).mkdir(exist_ok=_a    )
        print(f"Saving model {vit_name} to {pytorch_dump_folder_path}"    )
        model.save_pretrained(_a    )
        print(f"Saving processor to {pytorch_dump_folder_path}"    )
        processor.save_pretrained(_a    )

    if push_to_hub:
        print(f"Pushing model and processor to the hub {vit_name}"    )
        model.push_to_hub(f"ybelkada/{vit_name}"    )
        processor.push_to_hub(f"ybelkada/{vit_name}"    )


if __name__ == "__main__":
     lowercase__    :  int							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--vit_name''',
         default='''vit_base_r50_s16_384''',
         type=str,
         help='''Name of the hybrid ViT 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.'''
     )
     parser.add_argument(
         '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
     )

     lowercase__    :  Any							    =	parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)





