




'''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_		:     str          =		logging.get_logger(__name__)





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

     """simple docstring"""




     _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"""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"
          _snake_case				= [(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       __a      (  _UpperCamelCase:       int   ,		_UpperCamelCase:       Any   ,		_UpperCamelCase:       Optional[int]=False       )		->					Union[str, Any]:

     """simple docstring"""




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





def       __a      (  _UpperCamelCase:       Optional[Any]       )		->					List[str]:

     """simple docstring"""




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





def       __a      (  _UpperCamelCase:       Optional[int]   ,		_UpperCamelCase:       Optional[int]   ,		_UpperCamelCase:       List[str]       )		->					Any:

     """simple docstring"""




     _snake_case				= dct.pop(_UpperCamelCase       )
     _snake_case				= val





def       __a      (  )		->					List[Any]:

     """simple docstring"""




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





@torch.no_grad()
def       __a      (  _UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Any       )		->					Optional[Any]:

     """simple docstring"""




     _snake_case				= ViTConfig()
     _snake_case				= False
     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     if vit_name[-5:] == "in21k":
          _snake_case				= True
          _snake_case				= int(vit_name[-12:-10]       )
          _snake_case				= int(vit_name[-9:-6]       )
     else:
          _snake_case				= 1_000
          _snake_case				= "huggingface/label-files"
          _snake_case				= "imagenet-1k-id2label.json"
          _snake_case				= json.load(open(hf_hub_download(_UpperCamelCase   ,		_UpperCamelCase   ,		repo_type="dataset"       )   ,		"r"       )       )
          _snake_case				= {int(_UpperCamelCase       ): v for k, v in idalabel.items()}
          _snake_case				= idalabel
          _snake_case				= {v: k for k, v in idalabel.items()}
          _snake_case				= int(vit_name[-6:-4]       )
          _snake_case				= int(vit_name[-3:]       )
     # size of the architecture
     if "deit" in vit_name:
          if vit_name[9:].startswith("tiny"       ):
               _snake_case				= 192
               _snake_case				= 768
               _snake_case				= 12
               _snake_case				= 3
          elif vit_name[9:].startswith("small"       ):
               _snake_case				= 384
               _snake_case				= 1_536
               _snake_case				= 12
               _snake_case				= 6
          else:
               pass
     else:
          if vit_name[4:].startswith("small"       ):
               _snake_case				= 768
               _snake_case				= 2_304
               _snake_case				= 8
               _snake_case				= 8
          elif vit_name[4:].startswith("base"       ):
               pass
          elif vit_name[4:].startswith("large"       ):
               _snake_case				= 1_024
               _snake_case				= 4_096
               _snake_case				= 24
               _snake_case				= 16
          elif vit_name[4:].startswith("huge"       ):
               _snake_case				= 1_280
               _snake_case				= 5_120
               _snake_case				= 32
               _snake_case				= 16

    # load original model from timm
     _snake_case				= timm.create_model(_UpperCamelCase   ,		pretrained=_UpperCamelCase       )
     timm_model.eval()

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

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

     if base_model:
          _snake_case				= 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:
          _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 {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_		:     str          =		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_		:     Tuple          =		parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)








'''simple docstring'''





from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image


UpperCamelCase_		:     int          =		['''text''', '''image''', '''audio''']





def       __a      (  _UpperCamelCase:       List[str]       )		->					Dict:

     """simple docstring"""




     _snake_case				= []

     for input_type in input_types:
          if input_type == "text":
               inputs.append("Text input"       )
          elif input_type == "image":
               inputs.append(
                   Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"       )       ) / "000000039769.png"       ).resize((512, 512)       )       )
          elif input_type == "audio":
               inputs.append(torch.ones(3_000       )       )
          elif isinstance(_UpperCamelCase   ,		_UpperCamelCase       ):
               inputs.append(create_inputs(_UpperCamelCase       )       )
          else:
               raise ValueError(F"""Invalid type requested: {input_type}"""       )

     return inputs





def       __a      (  _UpperCamelCase:       List       )		->					Dict:

     """simple docstring"""




     _snake_case				= []

     for output in outputs:
          if isinstance(_UpperCamelCase   ,		(str, AgentText)       ):
               output_types.append("text"       )
          elif isinstance(_UpperCamelCase   ,		(Image.Image, AgentImage)       ):
               output_types.append("image"       )
          elif isinstance(_UpperCamelCase   ,		(torch.Tensor, AgentAudio)       ):
               output_types.append("audio"       )
          else:
               raise ValueError(F"""Invalid output: {output}"""       )

     return output_types





@is_tool_test
class   _a							:






     def 							_lowercase      (					self       )				->     Any:
          self.assertTrue(hasattr(self.tool   ,"inputs"       )       )
          self.assertTrue(hasattr(self.tool   ,"outputs"       )       )

          _snake_case				= self.tool.inputs
          for _input in inputs:
               if isinstance(_input   ,_SCREAMING_SNAKE_CASE       ):
                    for __input in _input:
                         self.assertTrue(__input in authorized_types       )
               else:
                    self.assertTrue(_input in authorized_types       )

          _snake_case				= self.tool.outputs
          for _output in outputs:
               self.assertTrue(_output in authorized_types       )






     def 							_lowercase      (					self       )				->     Any:
          _snake_case				= create_inputs(self.tool.inputs       )
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

          # There is a single output
          if len(self.tool.outputs       ) == 1:
               _snake_case				= [outputs]

          self.assertListEqual(output_types(_SCREAMING_SNAKE_CASE       )   ,self.tool.outputs       )






     def 							_lowercase      (					self       )				->     str:
          self.assertTrue(hasattr(self.tool   ,"description"       )       )
          self.assertTrue(hasattr(self.tool   ,"default_checkpoint"       )       )
          self.assertTrue(self.tool.description.startswith("This is a tool that"       )       )






     def 							_lowercase      (					self       )				->     Tuple:
          _snake_case				= create_inputs(self.tool.inputs       )
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

          if not isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= [outputs]

          self.assertEqual(len(_SCREAMING_SNAKE_CASE       )   ,len(self.tool.outputs       )       )

          for output, output_type in zip(_SCREAMING_SNAKE_CASE   ,self.tool.outputs       ):
               _snake_case				= AGENT_TYPE_MAPPING[output_type]
               self.assertTrue(isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )       )






     def 							_lowercase      (					self       )				->     Optional[Any]:
          _snake_case				= create_inputs(self.tool.inputs       )

          _snake_case				= []

          for _input, input_type in zip(_SCREAMING_SNAKE_CASE   ,self.tool.inputs       ):
               if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                    _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input       ) for _input_type in input_type]       )
               else:
                    _inputs.append(AGENT_TYPE_MAPPING[input_type](_input       )       )

        # Should not raise an error
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

          if not isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= [outputs]

          self.assertEqual(len(_SCREAMING_SNAKE_CASE       )   ,len(self.tool.outputs       )       )


