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






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






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   Tuple:
    __lowerCamelCase          =  ['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(UpperCamelCase__    ,  UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   int:
    __lowerCamelCase          =  dct.pop(UpperCamelCase__    )
    __lowerCamelCase          =  val






def __lowerCAmelCase			(							)     ->   int:
    __lowerCamelCase          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __lowerCamelCase          =  Image.open(requests.get(UpperCamelCase__    ,  stream=UpperCamelCase__    ).raw    )
    return im






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

    # load original model from timm
    __lowerCamelCase          =  timm.create_model(UpperCamelCase__    ,  pretrained=UpperCamelCase__    )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    __lowerCamelCase          =  timm_model.state_dict()
    if base_model:
        remove_classification_head_(UpperCamelCase__    )
    __lowerCamelCase          =  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
    if vit_name[-5:] == "in21k":
        __lowerCamelCase          =  ViTModel(UpperCamelCase__    ).eval()
    else:
        __lowerCamelCase          =  ViTForImageClassification(UpperCamelCase__    ).eval()
    model.load_state_dict(UpperCamelCase__    )

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

    if base_model:
        __lowerCamelCase          =  timm_model.forward_features(UpperCamelCase__    )
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(UpperCamelCase__    ,  outputs.pooler_output    ,  atol=1E-3    )
    else:
        __lowerCamelCase          =  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 {vit_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__":
  __UpperCAmelCase												=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."
  )

  __UpperCAmelCase												=parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)

'''simple docstring'''




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers.generation import (
      MaxLengthCriteria,
      MaxNewTokensCriteria,
      MaxTimeCriteria,
      StoppingCriteriaList,
      validate_stopping_criteria,
  )




@require_torch
class    a__				(       unittest.TestCase			):






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		int   ):





         """simple docstring"""



         __lowerCamelCase          =  3
         __lowerCamelCase          =  2_50

         __lowerCamelCase          =  ids_tensor((batch_size, length)  ,    a   )
         __lowerCamelCase          =  torch.ones((batch_size, length)  ,    device=a  ,    dtype=torch.float   ) / length
         return input_ids, scores






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )

         __lowerCamelCase          =  StoppingCriteriaList(
             [
                 MaxLengthCriteria(max_length=10   ),
                 MaxTimeCriteria(max_time=0.1   ),
             ]   )

         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(9   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(10   )
         self.assertTrue(criteria(a  ,    a   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  MaxLengthCriteria(max_length=10   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(9   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(10   )
         self.assertTrue(criteria(a  ,    a   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  MaxNewTokensCriteria(start_length=5  ,    max_new_tokens=5   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(9   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(10   )
         self.assertTrue(criteria(a  ,    a   )   )

         __lowerCamelCase          =  StoppingCriteriaList([criteria]   )
         self.assertEqual(criteria_list.max_length  ,    10   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )

         __lowerCamelCase          =  MaxTimeCriteria(max_time=0.1   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase          =  MaxTimeCriteria(max_time=0.1  ,    initial_timestamp=time.time() - 0.2   )
         self.assertTrue(criteria(a  ,    a   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )  ,    10   )

         with self.assertWarns(a   ):
             validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )  ,    11   )

         __lowerCamelCase          =  validate_stopping_criteria(StoppingCriteriaList()  ,    11   )

         self.assertEqual(len(a   )  ,    1   )
