

"""simple docstring"""


from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
			import tensorflow as tf

			from ..tf_utils import stable_softmax


if is_torch_available():
			import torch


__A						   =			logging.get_logger(__name__)





@add_end_docstrings(
    A__,							R"""
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    """,							)
class     snake_case	(				A__  ):

				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				GenericTensor)->   str:



						'''simple docstring'''


						if self.framework == "tf":
								__lowerCAmelCase:       Any          =			tf.where(input_ids == self.tokenizer.mask_token_id).numpy()
						elif self.framework == "pt":
								__lowerCAmelCase:       Any          =			torch.nonzero(input_ids == self.tokenizer.mask_token_id     ,  as_tuple=UpperCamelCase__)
						else:
								raise ValueError("Unsupported framework")
						return masked_index

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				GenericTensor)->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.get_masked_index(UpperCamelCase__)
						__lowerCAmelCase:       Optional[Any]          =			np.prod(masked_index.shape)
						if numel < 1:
								raise PipelineException(
								    "fill-mask"     ,  self.model.base_model_prefix     ,  f"No mask_token ({self.tokenizer.mask_token}) found on the input"     ,  )

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				GenericTensor)->   Union[str, Any]:



						'''simple docstring'''


						if isinstance(UpperCamelCase__     ,  UpperCamelCase__):
								for model_input in model_inputs:
										self._ensure_exactly_one_mask_token(model_input["input_ids"][0])
						else:
								for input_ids in model_inputs["input_ids"]:
										self._ensure_exactly_one_mask_token(UpperCamelCase__)

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				int=None     ,  **UpperCamelCase__							:				str)->   Tuple:



						'''simple docstring'''


						if return_tensors is None:
								__lowerCAmelCase:       Optional[int]          =			self.framework
						__lowerCAmelCase:       str          =			self.tokenizer(UpperCamelCase__     ,  return_tensors=UpperCamelCase__)
						self.ensure_exactly_one_mask_token(UpperCamelCase__)
						return model_inputs

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				List[str])->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			self.model(**UpperCamelCase__)
						__lowerCAmelCase:       List[str]          =			model_inputs["input_ids"]
						return model_outputs

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				Tuple     ,  UpperCamelCase__							:				str=5     ,  UpperCamelCase__							:				List[str]=None)->   Union[str, Any]:



						'''simple docstring'''


						if target_ids is not None and target_ids.shape[0] < top_k:
								__lowerCAmelCase:       Dict          =			target_ids.shape[0]
						__lowerCAmelCase:       int          =			model_outputs["input_ids"][0]
						__lowerCAmelCase:       Tuple          =			model_outputs["logits"]

						if self.framework == "tf":
								__lowerCAmelCase:       int          =			tf.where(input_ids == self.tokenizer.mask_token_id).numpy()[:, 0]

								__lowerCAmelCase:       int          =			outputs.numpy()

								__lowerCAmelCase:       int          =			outputs[0, masked_index, :]
								__lowerCAmelCase:       Tuple          =			stable_softmax(UpperCamelCase__     ,  axis=-1)
								if target_ids is not None:
										__lowerCAmelCase:       List[str]          =			tf.gather_nd(tf.squeeze(UpperCamelCase__     ,  0)     ,  target_ids.reshape(-1     ,  1))
										__lowerCAmelCase:       Union[str, Any]          =			tf.expand_dims(UpperCamelCase__     ,  0)

								__lowerCAmelCase:       int          =			tf.math.top_k(UpperCamelCase__     ,  k=UpperCamelCase__)
								__lowerCAmelCase:       int          =			topk.values.numpy(), topk.indices.numpy()
						else:
								__lowerCAmelCase:       Dict          =			torch.nonzero(input_ids == self.tokenizer.mask_token_id     ,  as_tuple=UpperCamelCase__).squeeze(-1)
								# Fill mask pipeline supports only one ${mask_token} per sample

								__lowerCAmelCase:       int          =			outputs[0, masked_index, :]
								__lowerCAmelCase:       Dict          =			logits.softmax(dim=-1)
								if target_ids is not None:
										__lowerCAmelCase:       Union[str, Any]          =			probs[..., target_ids]

								__lowerCAmelCase:       int          =			probs.topk(UpperCamelCase__)

						__lowerCAmelCase:       str          =			[]
						__lowerCAmelCase:       List[Any]          =			values.shape[0] == 1
						for i, (_values, _predictions) in enumerate(zip(values.tolist()     ,  predictions.tolist())):
								__lowerCAmelCase:       Optional[Any]          =			[]
								for v, p in zip(_values     ,  _predictions):
										# Copy is important since we're going to modify this array in place
										__lowerCAmelCase:       Optional[int]          =			input_ids.numpy().copy()
										if target_ids is not None:
												__lowerCAmelCase:       Any          =			target_ids[p].tolist()

										__lowerCAmelCase:       Dict          =			p
										# Filter padding out:
										__lowerCAmelCase:       Optional[Any]          =			tokens[np.where(tokens != self.tokenizer.pad_token_id)]
										# Originally we skip special tokens to give readable output.
										# For multi masks though, the other [MASK] would be removed otherwise
										# making the output look odd, so we add them back
										__lowerCAmelCase:       str          =			self.tokenizer.decode(UpperCamelCase__     ,  skip_special_tokens=UpperCamelCase__)
										__lowerCAmelCase:       List[str]          =			{"score": v, "token": p, "token_str": self.tokenizer.decode([p]), "sequence": sequence}
										row.append(UpperCamelCase__)
								result.append(UpperCamelCase__)
						if single_mask:
								return result[0]
						return result

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				Optional[Any]     ,  UpperCamelCase__							:				Tuple=None)->   List[str]:



						'''simple docstring'''


						if isinstance(UpperCamelCase__     ,  UpperCamelCase__):
								__lowerCAmelCase:       Optional[Any]          =			[targets]
						try:
								__lowerCAmelCase:       Any          =			self.tokenizer.get_vocab()
						except Exception:
								__lowerCAmelCase:       Dict          =			{}
						__lowerCAmelCase:       Any          =			[]
						for target in targets:
								__lowerCAmelCase:       List[Any]          =			vocab.get(UpperCamelCase__     ,  UpperCamelCase__)
								if id_ is None:
										__lowerCAmelCase:       List[Any]          =			self.tokenizer(
										    UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__     ,  return_attention_mask=UpperCamelCase__     ,  return_token_type_ids=UpperCamelCase__     ,  max_length=1     ,  truncation=UpperCamelCase__     ,  )["input_ids"]
										if len(UpperCamelCase__) == 0:
												logger.warning(
												    f"The specified target token `{target}` does not exist in the model vocabulary. "
												    "We cannot replace it with anything meaningful, ignoring it")
												continue
										__lowerCAmelCase:       List[str]          =			input_ids[0]
										# XXX: If users encounter this pass
										# it becomes pretty slow, so let's make sure
										# The warning enables them to fix the input to
										# get faster performance.
										logger.warning(
										    f"The specified target token `{target}` does not exist in the model vocabulary. "
										    f"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_)}`.")
								target_ids.append(id_)
						__lowerCAmelCase:       Union[str, Any]          =			list(set(UpperCamelCase__))
						if len(UpperCamelCase__) == 0:
								raise ValueError("At least one target must be provided when passed.")
						__lowerCAmelCase:       str          =			np.array(UpperCamelCase__)
						return target_ids

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				Optional[Any]=None     ,  UpperCamelCase__							:				Tuple=None)->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			{}

						if targets is not None:
								__lowerCAmelCase:       Optional[Any]          =			self.get_target_ids(UpperCamelCase__     ,  UpperCamelCase__)
								__lowerCAmelCase:       int          =			target_ids

						if top_k is not None:
								__lowerCAmelCase:       str          =			top_k

						if self.tokenizer.mask_token_id is None:
								raise PipelineException(
								    "fill-mask"     ,  self.model.base_model_prefix     ,  "The tokenizer does not define a `mask_token`.")
						return {}, {}, postprocess_params




				def __call__(	self							:				List[str]     ,  UpperCamelCase__							:				Optional[int]     ,  *UpperCamelCase__							:				int     ,  **UpperCamelCase__							:				List[str])->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			super().__call__(UpperCamelCase__     ,  **UpperCamelCase__)
						if isinstance(UpperCamelCase__     ,  UpperCamelCase__) and len(UpperCamelCase__) == 1:
								return outputs[0]
						return outputs










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)