'''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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:					str  =logging.get_logger(__name__)





def      lowerCAmelCase_    ( snake_case_	:			Dict ,   snake_case_	:			int=False      )   ->				Optional[Any]:


					'''simple docstring'''




					UpperCAmelCase_	   =       []
					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""")      )

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        ("cls_token", "vit.embeddings.cls_token"),
					        ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
					        ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
					        ("pos_embed", "vit.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 "vit" from all keys that start with "vit"
										UpperCAmelCase_	   =       [(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"),
										    ]      )

					return rename_keys





def      lowerCAmelCase_    ( snake_case_	:			str ,   snake_case_	:			Optional[int] ,   snake_case_	:			Union[str, Any]=False      )   ->				Optional[int]:


					'''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			Any      )   ->				int:


					'''simple docstring'''




					UpperCAmelCase_	   =       ["head.weight", "head.bias"]
					for k in ignore_keys:
										state_dict.pop(snake_case_ ,   snake_case_      )





def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			int ,   snake_case_	:			Tuple      )   ->				List[str]:


					'''simple docstring'''




					UpperCAmelCase_	   =       dct.pop(snake_case_      )
					UpperCAmelCase_	   =       val





def      lowerCAmelCase_    ( )   ->				Any:


					'''simple docstring'''




					UpperCAmelCase_	   =       "http://images.cocodataset.org/val2017/000000039769.jpg"
					UpperCAmelCase_	   =       Image.open(requests.get(snake_case_ ,   stream=snake_case_      ).raw      )
					return im





@torch.no_grad()
def      lowerCAmelCase_    ( snake_case_	:			Tuple ,   snake_case_	:			List[Any]      )   ->				Dict:


					'''simple docstring'''




					UpperCAmelCase_	   =       ViTConfig()
					UpperCAmelCase_	   =       False
					# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
					if vit_name[-5:] == "in21k":
										UpperCAmelCase_	   =       True
										UpperCAmelCase_	   =       int(vit_name[-12:-10]      )
										UpperCAmelCase_	   =       int(vit_name[-9:-6]      )
					else:
										UpperCAmelCase_	   =       10_00
										UpperCAmelCase_	   =       "huggingface/label-files"
										UpperCAmelCase_	   =       "imagenet-1k-id2label.json"
										UpperCAmelCase_	   =       json.load(open(hf_hub_download(snake_case_ ,   snake_case_ ,   repo_type="dataset"      ) ,   "r"      )      )
										UpperCAmelCase_	   =       {int(snake_case_      ): v for k, v in idalabel.items()}
										UpperCAmelCase_	   =       idalabel
										UpperCAmelCase_	   =       {v: k for k, v in idalabel.items()}
										UpperCAmelCase_	   =       int(vit_name[-6:-4]      )
										UpperCAmelCase_	   =       int(vit_name[-3:]      )
					# size of the architecture
					if "deit" in vit_name:
										if vit_name[9:].startswith("tiny"      ):
															UpperCAmelCase_	   =       1_92
															UpperCAmelCase_	   =       7_68
															UpperCAmelCase_	   =       12
															UpperCAmelCase_	   =       3
										elif vit_name[9:].startswith("small"      ):
															UpperCAmelCase_	   =       3_84
															UpperCAmelCase_	   =       15_36
															UpperCAmelCase_	   =       12
															UpperCAmelCase_	   =       6
										else:
															pass
					else:
										if vit_name[4:].startswith("small"      ):
															UpperCAmelCase_	   =       7_68
															UpperCAmelCase_	   =       23_04
															UpperCAmelCase_	   =       8
															UpperCAmelCase_	   =       8
										elif vit_name[4:].startswith("base"      ):
															pass
										elif vit_name[4:].startswith("large"      ):
															UpperCAmelCase_	   =       10_24
															UpperCAmelCase_	   =       40_96
															UpperCAmelCase_	   =       24
															UpperCAmelCase_	   =       16
										elif vit_name[4:].startswith("huge"      ):
															UpperCAmelCase_	   =       12_80
															UpperCAmelCase_	   =       51_20
															UpperCAmelCase_	   =       32
															UpperCAmelCase_	   =       16

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

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

					# load HuggingFace model
					if vit_name[-5:] == "in21k":
										UpperCAmelCase_	   =       ViTModel(snake_case_      ).eval()
					else:
										UpperCAmelCase_	   =       ViTForImageClassification(snake_case_      ).eval()
					model.load_state_dict(snake_case_      )

					# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
					if "deit" in vit_name:
										UpperCAmelCase_	   =       DeiTImageProcessor(size=config.image_size      )
					else:
										UpperCAmelCase_	   =       ViTImageProcessor(size=config.image_size      )
					UpperCAmelCase_	   =       image_processor(images=prepare_img() ,   return_tensors="pt"      )
					UpperCAmelCase_	   =       encoding["pixel_values"]
					UpperCAmelCase_	   =       model(snake_case_      )

					if base_model:
										UpperCAmelCase_	   =       timm_model.forward_features(snake_case_      )
										assert timm_pooled_output.shape == outputs.pooler_output.shape
										assert torch.allclose(snake_case_ ,   outputs.pooler_output ,   atol=1E-3      )
					else:
										UpperCAmelCase_	   =       timm_model(snake_case_      )
										assert timm_logits.shape == outputs.logits.shape
										assert torch.allclose(snake_case_ ,   outputs.logits ,   atol=1E-3      )

					Path(snake_case_      ).mkdir(exist_ok=snake_case_      )
					print(f"""Saving model {vit_name} 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__":
				SCREAMING_SNAKE_CASE_:					Any  =argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--vit_name',
				    default='vit_base_patch16_224',
				    type=str,
				    help='Name of the 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.'
				)

				SCREAMING_SNAKE_CASE_:					int  =parser.parse_args()
				convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)





'''simple docstring'''





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 	__A				(			unittest.TestCase     ):



    @slow
    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       TFCamembertModel.from_pretrained("jplu/tf-camembert-base"			)

         UpperCAmelCase_	   =       tf.convert_to_tensor(
             [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]       ,      dtype=tf.intaa       ,      )  # J'aime le camembert !"

         UpperCAmelCase_	   =       model(__a			)["last_hidden_state"]
         UpperCAmelCase_	   =       tf.TensorShape((1, 10, 768)			)
         self.assertEqual(output.shape       ,      __a			)
         # compare the actual values for a slice.
         UpperCAmelCase_	   =       tf.convert_to_tensor(
             [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]       ,      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			)			)




