






from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

lowerCamelCase	:							Union[str, Any]				=logging.get_logger(__name__)





@add_end_docstrings(A__       )
class 			__a  (    A__       ):







 def __init__(		self					:   int							,   *SCREAMING_SNAKE_CASE					:   Optional[int]							,   **SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      super().__init__(*SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)
      requires_backends(self							,   "vision"						)
      self.check_model_type(
          TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING						)







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   List[str]=None							,   SCREAMING_SNAKE_CASE					:   Tuple=None							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=None						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  {}
      UpperCamelCase__			:   List[str]       				=  {}

      if prompt is not None:
           UpperCamelCase__			:   Dict       				=  prompt

      if generate_kwargs is not None:
           UpperCamelCase__			:   Tuple       				=  generate_kwargs
      if max_new_tokens is not None:
           if "generate_kwargs" not in forward_kwargs:
                UpperCamelCase__			:   Union[str, Any]       				=  {}
           if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
                raise ValueError(
                    "'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"
                    " please use only one"						)
           UpperCamelCase__			:   Tuple       				=  max_new_tokens
      return preprocess_params, forward_kwargs, {}







 def __call__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Union[str, List[str], "Image.Image", List["Image.Image"]]							,   **SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






      return super().__call__(SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   List[str]							,   SCREAMING_SNAKE_CASE					:   str=None						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  load_image(SCREAMING_SNAKE_CASE						)

      if prompt is not None:
           if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
                raise ValueError(
                    F'Received an invalid text input, got - {type(SCREAMING_SNAKE_CASE						)} - but expected a single string. '
                    "Note also that one single text can be provided for conditional image to text generation."						)

           UpperCamelCase__			:   Tuple       				=  self.model.config.model_type

           if model_type == "git":
                UpperCamelCase__			:   Any       				=  self.image_processor(images=SCREAMING_SNAKE_CASE							,   return_tensors=self.framework						)
                UpperCamelCase__			:   int       				=  self.tokenizer(text=SCREAMING_SNAKE_CASE							,   add_special_tokens=SCREAMING_SNAKE_CASE						).input_ids
                UpperCamelCase__			:   Dict       				=  [self.tokenizer.cls_token_id] + input_ids
                UpperCamelCase__			:   List[Any]       				=  torch.tensor(SCREAMING_SNAKE_CASE						).unsqueeze(0						)
                model_inputs.update({"input_ids": input_ids}						)

           elif model_type == "pix2struct":
                UpperCamelCase__			:   Tuple       				=  self.image_processor(images=SCREAMING_SNAKE_CASE							,   header_text=SCREAMING_SNAKE_CASE							,   return_tensors=self.framework						)

           elif model_type != "vision-encoder-decoder":
                # vision-encoder-decoder does not support conditional generation
                UpperCamelCase__			:   str       				=  self.image_processor(images=SCREAMING_SNAKE_CASE							,   return_tensors=self.framework						)
                UpperCamelCase__			:   str       				=  self.tokenizer(SCREAMING_SNAKE_CASE							,   return_tensors=self.framework						)
                model_inputs.update(SCREAMING_SNAKE_CASE						)

           else:
                raise ValueError(F'Model type {model_type} does not support conditional text generation'						)

      else:
           UpperCamelCase__			:   int       				=  self.image_processor(images=SCREAMING_SNAKE_CASE							,   return_tensors=self.framework						)

      if self.model.config.model_type == "git" and prompt is None:
           UpperCamelCase__			:   Tuple       				=  None

      return model_inputs







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Any=None						):


      '''simple docstring'''






      if (
          "input_ids" in model_inputs
          and isinstance(model_inputs["input_ids"]							,   SCREAMING_SNAKE_CASE						)
          and all(x is None for x in model_inputs["input_ids"]						)
      ):
           UpperCamelCase__			:   str       				=  None

      if generate_kwargs is None:
           UpperCamelCase__			:   List[Any]       				=  {}
      # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
      #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
      #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
      #  in the `_prepare_model_inputs` method.
      UpperCamelCase__			:   Any       				=  model_inputs.pop(self.model.main_input_name						)
      UpperCamelCase__			:   Dict       				=  self.model.generate(SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)
      return model_outputs







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  []
      for output_ids in model_outputs:
           UpperCamelCase__			:   Tuple       				=  {
               "generated_text": self.tokenizer.decode(
                   SCREAMING_SNAKE_CASE							,   skip_special_tokens=SCREAMING_SNAKE_CASE							,   )
           }
           records.append(SCREAMING_SNAKE_CASE						)
      return records







import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase	:							Optional[int]				=logging.get_logger()





@dataclass
class 			__a  :
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       List[nn.Module]        = field(default_factory=A__       )
 _lowerCAmelCase       :       list        = field(default_factory=A__       )







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Tensor							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  len(list(m.modules()						)						) == 1 or isinstance(SCREAMING_SNAKE_CASE							,   nn.Convad						) or isinstance(SCREAMING_SNAKE_CASE							,   nn.BatchNormad						)
      if has_not_submodules:
           self.traced.append(SCREAMING_SNAKE_CASE						)







 def __call__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      for m in self.module.modules():
           self.handles.append(m.register_forward_hook(self._forward_hook						)						)
      self.module(SCREAMING_SNAKE_CASE						)
      [x.remove() for x in self.handles]
      return self







 @property
 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      return list(filter(lambda SCREAMING_SNAKE_CASE						: len(list(x.state_dict().keys()						)						) > 0							,   self.traced						)						)







@dataclass
class 			__a  :
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       int        = 0
 _lowerCAmelCase       :       List        = field(default_factory=A__       )
 _lowerCAmelCase       :       List        = field(default_factory=A__       )







 def __call__(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  Tracker(self.dest						)(SCREAMING_SNAKE_CASE						).parametrized
      UpperCamelCase__			:   Any       				=  Tracker(self.src						)(SCREAMING_SNAKE_CASE						).parametrized

      UpperCamelCase__			:   str       				=  list(filter(lambda SCREAMING_SNAKE_CASE						: type(SCREAMING_SNAKE_CASE						) not in self.src_skip							,   SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   List[Any]       				=  list(filter(lambda SCREAMING_SNAKE_CASE						: type(SCREAMING_SNAKE_CASE						) not in self.dest_skip							,   SCREAMING_SNAKE_CASE						)						)

      if len(SCREAMING_SNAKE_CASE						) != len(SCREAMING_SNAKE_CASE						):
           raise Exception(
               F'Numbers of operations are different. Source module has {len(SCREAMING_SNAKE_CASE						)} operations while'
               F' destination module has {len(SCREAMING_SNAKE_CASE						)}.'						)

      for dest_m, src_m in zip(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           dest_m.load_state_dict(src_m.state_dict()						)
           if self.verbose == 1:
                print(F'Transfered from={src_m} to={dest_m}'						)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase = True			) ->	Optional[int]:
     print(f'Converting {name}...'			)
     with torch.no_grad():
          UpperCamelCase__			:   Union[str, Any]       				=  timm.create_model(__lowerCAmelCase					,    pretrained=__lowerCAmelCase			).eval()
          UpperCamelCase__			:   List[Any]       				=  ResNetForImageClassification(__lowerCAmelCase			).eval()
          UpperCamelCase__			:   Optional[Any]       				=  ModuleTransfer(src=__lowerCAmelCase					,    dest=__lowerCAmelCase			)
          UpperCamelCase__			:   int       				=  torch.randn((1, 3, 224, 224)			)
          module_transfer(__lowerCAmelCase			)

     assert torch.allclose(from_model(__lowerCAmelCase			)					,    our_model(__lowerCAmelCase			).logits			), "The model logits don't match the original one."

     UpperCamelCase__			:   List[Any]       				=  f'resnet{"-".join(name.split("resnet"			)			)}'
     print(__lowerCAmelCase			)

     if push_to_hub:
          our_model.push_to_hub(
              repo_path_or_name=save_directory / checkpoint_name					,    commit_message="Add model"					,    use_temp_dir=__lowerCAmelCase					,    )

          # we can use the convnext one
          UpperCamelCase__			:   List[Any]       				=  AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"			)
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / checkpoint_name					,    commit_message="Add image processor"					,    use_temp_dir=__lowerCAmelCase					,    )

          print(f'Pushed {checkpoint_name}'			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase = None					,    __lowerCAmelCase = True			) ->	Dict:
     UpperCamelCase__			:   Dict       				=  "imagenet-1k-id2label.json"
     UpperCamelCase__			:   Optional[int]       				=  1000
     UpperCamelCase__			:   Any       				=  (1, num_labels)

     UpperCamelCase__			:   Union[str, Any]       				=  "huggingface/label-files"
     UpperCamelCase__			:   Optional[int]       				=  num_labels
     UpperCamelCase__			:   str       				=  json.load(open(hf_hub_download(__lowerCAmelCase					,    __lowerCAmelCase					,    repo_type="dataset"			)					,    "r"			)			)
     UpperCamelCase__			:   Optional[int]       				=  {int(__lowerCAmelCase			): v for k, v in idalabel.items()}

     UpperCamelCase__			:   Tuple       				=  idalabel
     UpperCamelCase__			:   Union[str, Any]       				=  {v: k for k, v in idalabel.items()}

     UpperCamelCase__			:   Union[str, Any]       				=  partial(__lowerCAmelCase					,    num_labels=__lowerCAmelCase					,    idalabel=__lowerCAmelCase					,    labelaid=__lowerCAmelCase			)

     UpperCamelCase__			:   Union[str, Any]       				=  {
         "resnet18": ImageNetPreTrainedConfig(
             depths=[2, 2, 2, 2]					,    hidden_sizes=[64, 128, 256, 512]					,    layer_type="basic"			),
         "resnet26": ImageNetPreTrainedConfig(
             depths=[2, 2, 2, 2]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
         "resnet34": ImageNetPreTrainedConfig(
             depths=[3, 4, 6, 3]					,    hidden_sizes=[64, 128, 256, 512]					,    layer_type="basic"			),
         "resnet50": ImageNetPreTrainedConfig(
             depths=[3, 4, 6, 3]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
         "resnet101": ImageNetPreTrainedConfig(
             depths=[3, 4, 23, 3]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
         "resnet152": ImageNetPreTrainedConfig(
             depths=[3, 8, 36, 3]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
     }

     if model_name:
          convert_weight_and_push(__lowerCAmelCase					,    names_to_config[model_name]					,    __lowerCAmelCase					,    __lowerCAmelCase			)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			)
     return config, expected_shape


if __name__ == "__main__":
 lowerCamelCase	:							Union[str, Any]				=argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default=None,
     type=str,
     help=(
         '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
         ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=Path,
     required=True,
     help='''Path to the output PyTorch model directory.''',
 )
 parser.add_argument(
     '''--push_to_hub''',
     default=True,
     type=bool,
     required=False,
     help='''If True, push model and image processor to the hub.''',
 )

 lowerCamelCase	:							int				=parser.parse_args()
 lowerCamelCase	:							Path				=args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)