'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






def 				snake_case__							(			_A:					Any )    ->	int:

		'''simple docstring'''


		if "cls_token" in name:
				lowerCAmelCase   					=  name.replace("""cls_token"""      ,   """vit.embeddings.cls_token""" )
		if "mask_token" in name:
				lowerCAmelCase   					=  name.replace("""mask_token"""      ,   """decoder.mask_token""" )
		if "decoder_pos_embed" in name:
				lowerCAmelCase   					=  name.replace("""decoder_pos_embed"""      ,   """decoder.decoder_pos_embed""" )
		if "pos_embed" in name and "decoder" not in name:
				lowerCAmelCase   					=  name.replace("""pos_embed"""      ,   """vit.embeddings.position_embeddings""" )
		if "patch_embed.proj" in name:
				lowerCAmelCase   					=  name.replace("""patch_embed.proj"""      ,   """vit.embeddings.patch_embeddings.projection""" )
		if "patch_embed.norm" in name:
				lowerCAmelCase   					=  name.replace("""patch_embed.norm"""      ,   """vit.embeddings.norm""" )
		if "decoder_blocks" in name:
				lowerCAmelCase   					=  name.replace("""decoder_blocks"""      ,   """decoder.decoder_layers""" )
		if "blocks" in name:
				lowerCAmelCase   					=  name.replace("""blocks"""      ,   """vit.encoder.layer""" )
		if "attn.proj" in name:
				lowerCAmelCase   					=  name.replace("""attn.proj"""      ,   """attention.output.dense""" )
		if "attn" in name:
				lowerCAmelCase   					=  name.replace("""attn"""      ,   """attention.self""" )
		if "norm1" in name:
				lowerCAmelCase   					=  name.replace("""norm1"""      ,   """layernorm_before""" )
		if "norm2" in name:
				lowerCAmelCase   					=  name.replace("""norm2"""      ,   """layernorm_after""" )
		if "mlp.fc1" in name:
				lowerCAmelCase   					=  name.replace("""mlp.fc1"""      ,   """intermediate.dense""" )
		if "mlp.fc2" in name:
				lowerCAmelCase   					=  name.replace("""mlp.fc2"""      ,   """output.dense""" )
		if "decoder_embed" in name:
				lowerCAmelCase   					=  name.replace("""decoder_embed"""      ,   """decoder.decoder_embed""" )
		if "decoder_norm" in name:
				lowerCAmelCase   					=  name.replace("""decoder_norm"""      ,   """decoder.decoder_norm""" )
		if "decoder_pred" in name:
				lowerCAmelCase   					=  name.replace("""decoder_pred"""      ,   """decoder.decoder_pred""" )
		if "norm.weight" in name and "decoder" not in name:
				lowerCAmelCase   					=  name.replace("""norm.weight"""      ,   """vit.layernorm.weight""" )
		if "norm.bias" in name and "decoder" not in name:
				lowerCAmelCase   					=  name.replace("""norm.bias"""      ,   """vit.layernorm.bias""" )

		return name






def 				snake_case__							(			_A:					int      ,   _A:					Optional[int] )    ->	Union[str, Any]:

		'''simple docstring'''


		for key in orig_state_dict.copy().keys():
				lowerCAmelCase   					=  orig_state_dict.pop(snake_case_ )

				if "qkv" in key:
						lowerCAmelCase   					=  key.split(""".""" )
						lowerCAmelCase   					=  int(key_split[1] )
						if "decoder_blocks" in key:
								lowerCAmelCase   					=  config.decoder_hidden_size
								lowerCAmelCase   					=  """decoder.decoder_layers."""
								if "weight" in key:
										lowerCAmelCase   					=  val[:dim, :]
										lowerCAmelCase   					=  val[dim : dim * 2, :]
										lowerCAmelCase   					=  val[-dim:, :]
								elif "bias" in key:
										lowerCAmelCase   					=  val[:dim]
										lowerCAmelCase   					=  val[dim : dim * 2]
										lowerCAmelCase   					=  val[-dim:]
						else:
								lowerCAmelCase   					=  config.hidden_size
								lowerCAmelCase   					=  """vit.encoder.layer."""
								if "weight" in key:
										lowerCAmelCase   					=  val[:dim, :]
										lowerCAmelCase   					=  val[dim : dim * 2, :]
										lowerCAmelCase   					=  val[-dim:, :]
								elif "bias" in key:
										lowerCAmelCase   					=  val[:dim]
										lowerCAmelCase   					=  val[dim : dim * 2]
										lowerCAmelCase   					=  val[-dim:]

				else:
						lowerCAmelCase   					=  val

		return orig_state_dict






def 				snake_case__							(			_A:					int      ,   _A:					Any )    ->	Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase   					=  ViTMAEConfig()
		if "large" in checkpoint_url:
				lowerCAmelCase   					=  1024
				lowerCAmelCase   					=  4096
				lowerCAmelCase   					=  24
				lowerCAmelCase   					=  16
		elif "huge" in checkpoint_url:
				lowerCAmelCase   					=  14
				lowerCAmelCase   					=  1280
				lowerCAmelCase   					=  5120
				lowerCAmelCase   					=  32
				lowerCAmelCase   					=  16

		lowerCAmelCase   					=  ViTMAEForPreTraining(snake_case_ )

		lowerCAmelCase   					=  torch.hub.load_state_dict_from_url(snake_case_      ,   map_location="""cpu""" )["""model"""]

		lowerCAmelCase   					=  ViTMAEImageProcessor(size=config.image_size )

		lowerCAmelCase   					=  convert_state_dict(snake_case_      ,   snake_case_ )

		model.load_state_dict(snake_case_ )
		model.eval()

		lowerCAmelCase   					=  """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

		lowerCAmelCase   					=  Image.open(requests.get(snake_case_      ,   stream=snake_case_ ).raw )
		lowerCAmelCase   					=  ViTMAEImageProcessor(size=config.image_size )
		lowerCAmelCase   					=  image_processor(images=snake_case_      ,   return_tensors="""pt""" )

		# forward pass
		torch.manual_seed(2 )
		lowerCAmelCase   					=  model(**snake_case_ )
		lowerCAmelCase   					=  outputs.logits

		if "large" in checkpoint_url:
				lowerCAmelCase   					=  torch.tensor(
				    [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]] )
		elif "huge" in checkpoint_url:
				lowerCAmelCase   					=  torch.tensor(
				    [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]] )
		else:
				lowerCAmelCase   					=  torch.tensor(
				    [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]] )

		# verify logits
		assert torch.allclose(logits[0, :3, :3]      ,   snake_case_      ,   atol=1e-4 )

		print(f"Saving model to {pytorch_dump_folder_path}" )
		model.save_pretrained(snake_case_ )

		print(f"Saving image processor to {pytorch_dump_folder_path}" )
		image_processor.save_pretrained(snake_case_ )


if __name__ == "__main__":
						__lowercase          =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--checkpoint_url''',
						    default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
						    type=str,
						    help='''URL of the checkpoint you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
						)

						__lowercase          =  parser.parse_args()
						convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class 						lowercase							(   unittest.TestCase		):

     @slow
     def 					a__	(  self				)						->							Any:
           _A							:     Tuple			=						TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""				)

           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]		,     dtype=tf.intaa		,     )  # J'aime le camembert !"

           _A							:     List[str]			=						model(_a				)["""last_hidden_state"""]
           _A							:     Union[str, Any]			=						tf.TensorShape((1, 10, 768)				)
           self.assertEqual(output.shape		,     _a				)
           # compare the actual values for a slice.
           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]		,     dtype=tf.floataa		,     )
           # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
           # camembert.eval()
           # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()		,     expected_slice.numpy()		,     atol=1e-4				)				)

