




"""simple docstring"""
import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


_SCREAMING_SNAKE_CASE						 =     {
    """vit_b32""": {
        """embed_dim""": 5_1_2,
        """image_resolution""": 7_6_8,
        """context_length""": 1_6,
        """vocab_size""": 4_9_4_0_8,
        """vision_layers""": 1_2,
        """vision_width""": 7_6_8,
        """vision_patch_size""": 3_2,
        """transformer_width""": 5_1_2,
        """transformer_heads""": 8,
        """transformer_layers""": 1_2,
    },
    """vit_b16""": {
        """embed_dim""": 5_1_2,
        """image_resolution""": 7_6_8,
        """context_length""": 1_6,
        """vocab_size""": 4_9_4_0_8,
        """vision_layers""": 1_2,
        """vision_width""": 7_6_8,
        """vision_patch_size""": 1_6,
        """transformer_width""": 5_1_2,
        """transformer_heads""": 8,
        """transformer_layers""": 1_2,
    },
    """vit_l14""": {
        """embed_dim""": 7_6_8,
        """image_resolution""": 8_4_0,
        """context_length""": 1_6,
        """vocab_size""": 4_9_4_0_8,
        """vision_layers""": 2_4,
        """vision_width""": 1_0_2_4,
        """vision_patch_size""": 1_4,
        """transformer_width""": 7_6_8,
        """transformer_heads""": 1_2,
        """transformer_layers""": 1_2,
    },
}






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Dict  ,	__lowerCAmelCase			:		Optional[Any]=""  ,	__lowerCAmelCase			:		Optional[Any]="/"						)    ->       List[Any]:
 _UpperCamelCase      :	Any					      =  []

 for k, v in params.items():
  _UpperCamelCase      :	Tuple					      =  parent_key + sep + k if parent_key else k

  if isinstance(__lowerCamelCase  ,	collections.MutableMapping						):
   items.extend(flatten_nested_dict(__lowerCamelCase  ,	__lowerCamelCase  ,	sep=__lowerCamelCase						).items()						)
  else:
   items.append((new_key, v)						)
 return dict(__lowerCamelCase						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Union[str, Any]						)    ->       Any:
 return jax.tree_util.tree_map(lambda __lowerCAmelCase						: x.astype(jnp.floataa						) if x.dtype == jnp.bfloataa else x  ,	__lowerCamelCase						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		List[Any]  ,	__lowerCAmelCase			:		Union[str, Any]						)    ->       int:
 _UpperCamelCase      ,	_UpperCamelCase      ,	_UpperCamelCase      :	Dict					      =  pt_attn_layer.in_proj_weight.chunk(3  ,	dim=0						)
 _UpperCamelCase      ,	_UpperCamelCase      ,	_UpperCamelCase      :	int					      =  pt_attn_layer.in_proj_bias.chunk(3  ,	dim=0						)

 _UpperCamelCase      :	Dict					      =  pt_attn_layer.out_proj.weight
 _UpperCamelCase      :	List[str]					      =  pt_attn_layer.out_proj.bias

 _UpperCamelCase      :	Union[str, Any]					      =  q_proj
 _UpperCamelCase      :	List[str]					      =  q_proj_bias

 _UpperCamelCase      :	Optional[int]					      =  k_proj
 _UpperCamelCase      :	Any					      =  k_proj_bias

 _UpperCamelCase      :	Optional[Any]					      =  v_proj
 _UpperCamelCase      :	int					      =  v_proj_bias

 _UpperCamelCase      :	List[str]					      =  out_proj_weights
 _UpperCamelCase      :	str					      =  out_proj_bias






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[int]  ,	__lowerCAmelCase			:		Optional[int]						)    ->       Any:
 copy_linear(hf_mlp.fca  ,	pt_mlp.c_fc						)
 copy_linear(hf_mlp.fca  ,	pt_mlp.c_proj						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Union[str, Any]  ,	__lowerCAmelCase			:		Dict						)    ->       Optional[Any]:
 _UpperCamelCase      :	Tuple					      =  pt_linear.weight
 _UpperCamelCase      :	Any					      =  pt_linear.bias






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		str  ,	__lowerCAmelCase			:		Any						)    ->       List[str]:
 # copy layer norms
 copy_linear(hf_layer.layer_norma  ,	pt_layer.ln_a						)
 copy_linear(hf_layer.layer_norma  ,	pt_layer.ln_a						)

 # copy MLP
 copy_mlp(hf_layer.mlp  ,	pt_layer.mlp						)

 # copy attn
 copy_attn_layer(hf_layer.self_attn  ,	pt_layer.attn						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		Dict						)    ->       Optional[Any]:
 for hf_layer, pt_layer in zip(__lowerCamelCase  ,	__lowerCamelCase						):
  copy_layer(__lowerCamelCase  ,	__lowerCamelCase						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[int]  ,	__lowerCAmelCase			:		Union[str, Any]						)    ->       str:
 # copy  embeds
 _UpperCamelCase      :	List[str]					      =  pt_model.token_embedding.weight
 _UpperCamelCase      :	Dict					      =  pt_model.positional_embedding

 # copy layer norm
 copy_linear(hf_encoder.final_layer_norm  ,	pt_model.ln_final						)

 # copy hidden layers
 copy_layers(hf_encoder.encoder.layers  ,	pt_model.transformer.resblocks						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Union[str, Any]  ,	__lowerCAmelCase			:		Any						)    ->       Dict:
 # copy projection
 _UpperCamelCase      :	Optional[Any]					      =  pt_model.text_projection.data.T

 # copy text encoder
 copy_encoder(hf_model.text_model  ,	__lowerCamelCase						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		List[str]  ,	__lowerCAmelCase			:		Any						)    ->       Optional[Any]:
 # copy projection
 _UpperCamelCase      :	int					      =  pt_model.visual.proj.data.T

 # copy layer norms
 copy_linear(hf_model.vision_model.pre_layernorm  ,	pt_model.visual.ln_pre						)
 copy_linear(hf_model.vision_model.post_layernorm  ,	pt_model.visual.ln_post						)

 # copy embeds
 _UpperCamelCase      :	int					      =  pt_model.visual.conva.weight.data
 _UpperCamelCase      :	Tuple					      =  pt_model.visual.class_embedding
 _UpperCamelCase      :	Optional[int]					      =  pt_model.visual.positional_embedding.data

 # copy encoder
 copy_layers(hf_model.vision_model.encoder.layers  ,	pt_model.visual.transformer.resblocks						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		List[str]  ,	__lowerCAmelCase			:		Any						)    ->       Optional[Any]:
 _UpperCamelCase      :	str					      =  flatten_nested_dict(flax_params["backbone"]["merged_class_token"]						)

 _UpperCamelCase      :	Dict					      =  torch.from_numpy(flax_class_token_params["scale"]						)
 _UpperCamelCase      :	List[Any]					      =  torch.from_numpy(flax_class_token_params["bias"]						)
 _UpperCamelCase      :	Dict					      =  nn.Parameter(__lowerCamelCase						)
 _UpperCamelCase      :	List[str]					      =  nn.Parameter(__lowerCamelCase						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		List[Any]  ,	__lowerCAmelCase			:		str						)    ->       Tuple:
 _UpperCamelCase      :	str					      =  hf_model.state_dict()
 _UpperCamelCase      :	int					      =  {}

 # Rename class prediction head flax params to pytorch HF
 _UpperCamelCase      :	Union[str, Any]					      =  flatten_nested_dict(flax_params["class_head"]						)

 for flax_key, v in flax_class_params.items():
  _UpperCamelCase      :	Any					      =  flax_key.replace("/"  ,	"."						)
  _UpperCamelCase      :	List[Any]					      =  torch_key.replace(".kernel"  ,	".weight"						)
  _UpperCamelCase      :	Union[str, Any]					      =  torch_key.replace("Dense_0"  ,	"dense0"						)
  _UpperCamelCase      :	int					      =  "class_head." + torch_key

  if "weight" in torch_key and v.ndim == 2:
   _UpperCamelCase      :	Tuple					      =  v.T

  _UpperCamelCase      :	str					      =  nn.Parameter(torch.from_numpy(__lowerCamelCase						)						)

 # Rename box prediction box flax params to pytorch HF
 _UpperCamelCase      :	Dict					      =  flatten_nested_dict(flax_params["obj_box_head"]						)

 for flax_key, v in flax_box_params.items():
  _UpperCamelCase      :	Tuple					      =  flax_key.replace("/"  ,	"."						)
  _UpperCamelCase      :	Tuple					      =  torch_key.replace(".kernel"  ,	".weight"						)
  _UpperCamelCase      :	Optional[int]					      =  torch_key.replace("_"  ,	""						).lower()
  _UpperCamelCase      :	Optional[Any]					      =  "box_head." + torch_key

  if "weight" in torch_key and v.ndim == 2:
   _UpperCamelCase      :	List[Any]					      =  v.T

  _UpperCamelCase      :	Any					      =  nn.Parameter(torch.from_numpy(__lowerCamelCase						)						)

 # Copy flax params to PyTorch params
 for name, param in new_params.items():
  if name in pt_params.keys():
   pt_params[name].copy_(__lowerCamelCase						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		List[Any]  ,	__lowerCAmelCase			:		List[str]						)    ->       Any:
 for k, v in flax_attn_params.items():
  if k.startswith("transformer"						):
   _UpperCamelCase      :	List[str]					      =  k.replace("transformer.resblocks"  ,	"text_model.encoder.layers"						)
  else:
   _UpperCamelCase      :	Optional[int]					      =  k.replace("visual.transformer.resblocks"  ,	"vision_model.encoder.layers"						)

  _UpperCamelCase      :	str					      =  torch_key.replace("attn"  ,	"self_attn"						)
  _UpperCamelCase      :	List[Any]					      =  torch_key.replace("key"  ,	"k_proj"						)
  _UpperCamelCase      :	Dict					      =  torch_key.replace("value"  ,	"v_proj"						)
  _UpperCamelCase      :	Optional[int]					      =  torch_key.replace("query"  ,	"q_proj"						)
  _UpperCamelCase      :	int					      =  torch_key.replace("out"  ,	"out_proj"						)

  if "bias" in torch_key and v.ndim == 2:
   _UpperCamelCase      :	Any					      =  v.shape[0] * v.shape[1]
   _UpperCamelCase      :	Union[str, Any]					      =  v.reshape(__lowerCamelCase						)

  if "weight" in torch_key and "out" in torch_key:
   _UpperCamelCase      :	List[str]					      =  (v.shape[0] * v.shape[1], v.shape[2])
   _UpperCamelCase      :	int					      =  v.reshape(__lowerCamelCase						).T

  if "weight" in torch_key and "out" not in torch_key:
   _UpperCamelCase      :	List[Any]					      =  (v.shape[0], v.shape[1] * v.shape[2])
   _UpperCamelCase      :	Optional[Any]					      =  v.reshape(__lowerCamelCase						).T

  # Copy flax CLIP attn params to HF PyTorch params
  _UpperCamelCase      :	int					      =  torch.from_numpy(__lowerCamelCase						)
  hf_backbone.state_dict()[torch_key].copy_(__lowerCamelCase						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		List[str]						)    ->       Tuple:
 _UpperCamelCase      :	Dict					      =  {}
 _UpperCamelCase      :	Dict					      =  []

 for k, v in params.items():
  if "attn." in k:
   _UpperCamelCase      :	Dict					      =  k[: k.rindex("attn."						) + 5]
   if base in processed_attn_layers:
    continue

   processed_attn_layers.append(__lowerCamelCase						)
   _UpperCamelCase      :	Optional[Any]					      =  params[base + "out.weight"].shape[-1]
   _UpperCamelCase      :	int					      =  params[base + "out.weight"].reshape(__lowerCamelCase  ,	__lowerCamelCase						).T
   _UpperCamelCase      :	Optional[int]					      =  params[base + "out.bias"]
  else:
   _UpperCamelCase      :	int					      =  v
 return new_params






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Any  ,	__lowerCAmelCase			:		List[str]						)    ->       List[str]:
 _UpperCamelCase      :	Optional[int]					      =  CLIP(**__lowerCamelCase						)
 torch_model.eval()
 _UpperCamelCase      :	Tuple					      =  torch_model.state_dict()

 _UpperCamelCase      :	Dict					      =  flatten_nested_dict(flax_params["backbone"]["clip"]						)
 _UpperCamelCase      :	Union[str, Any]					      =  {}

 for flax_key, v in flax_clip_params.items():
  _UpperCamelCase      :	Optional[int]					      =  flax_key.replace("/"  ,	"."						)
  _UpperCamelCase      :	Optional[int]					      =  torch_key.replace("text.token_embedding.embedding"  ,	"token_embedding.kernel"						)

  if (
      torch_key.startswith("text.transformer"						)
      or torch_key.startswith("text.text_projection"						)
      or torch_key.startswith("text.ln_final"						)
      or torch_key.startswith("text.positional_embedding"						)
  ):
   _UpperCamelCase      :	List[str]					      =  torch_key[5:]

  _UpperCamelCase      :	int					      =  torch_key.replace("text_projection.kernel"  ,	"text_projection"						)
  _UpperCamelCase      :	Optional[Any]					      =  torch_key.replace("visual.proj.kernel"  ,	"visual.proj"						)
  _UpperCamelCase      :	Optional[int]					      =  torch_key.replace(".scale"  ,	".weight"						)
  _UpperCamelCase      :	Union[str, Any]					      =  torch_key.replace(".kernel"  ,	".weight"						)

  if "conv" in torch_key or "downsample.0.weight" in torch_key:
   _UpperCamelCase      :	Any					      =  v.transpose(3  ,	2  ,	0  ,	1						)

  elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
   # Fully connected layers are transposed, embeddings are not
   _UpperCamelCase      :	Any					      =  v.T

  _UpperCamelCase      :	Optional[Any]					      =  v

 _UpperCamelCase      :	Tuple					      =  _convert_attn_layers(__lowerCamelCase						)
 new_torch_params.update(__lowerCamelCase						)
 _UpperCamelCase      :	Any					      =  {}

 # Copy flax CLIP backbone params to PyTorch params
 for name, param in new_torch_params.items():
  if name in torch_clip_params.keys():
   _UpperCamelCase      :	List[str]					      =  torch.from_numpy(new_torch_params[name]						)
   torch_clip_params[name].copy_(__lowerCamelCase						)
  else:
   _UpperCamelCase      :	int					      =  param

 return torch_clip_params, torch_model, attn_params






@torch.no_grad()
def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[int]  ,	__lowerCAmelCase			:		Union[str, Any]  ,	__lowerCAmelCase			:		Dict  ,	__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		Optional[Any]=None						)    ->       str:
 _UpperCamelCase      :	Union[str, Any]					      =  Repository(__lowerCamelCase  ,	clone_from=f"google/{pytorch_dump_folder_path}"						)
 repo.git_pull()

 if config_path is not None:
  _UpperCamelCase      :	Dict					      =  OwlViTConfig.from_pretrained(__lowerCamelCase						)
 else:
  _UpperCamelCase      :	Optional[int]					      =  OwlViTConfig()

 _UpperCamelCase      :	Optional[int]					      =  OwlViTModel(__lowerCamelCase						).eval()
 _UpperCamelCase      :	List[Any]					      =  OwlViTForObjectDetection(__lowerCamelCase						).eval()

 copy_text_model_and_projection(__lowerCamelCase  ,	__lowerCamelCase						)
 copy_vision_model_and_projection(__lowerCamelCase  ,	__lowerCamelCase						)
 _UpperCamelCase      :	List[Any]					      =  pt_backbone.logit_scale
 copy_flax_attn_params(__lowerCamelCase  ,	__lowerCamelCase						)

 _UpperCamelCase      :	Dict					      =  hf_backbone
 copy_class_merge_token(__lowerCamelCase  ,	__lowerCamelCase						)
 copy_class_box_heads(__lowerCamelCase  ,	__lowerCamelCase						)

 # Save HF model
 hf_model.save_pretrained(repo.local_dir						)

 # Initialize image processor
 _UpperCamelCase      :	Optional[Any]					      =  OwlViTImageProcessor(
     size=config.vision_config.image_size  ,	crop_size=config.vision_config.image_size						)
 # Initialize tokenizer
 _UpperCamelCase      :	str					      =  CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"  ,	pad_token="!"  ,	model_max_length=16						)

 # Initialize processor
 _UpperCamelCase      :	str					      =  OwlViTProcessor(image_processor=__lowerCamelCase  ,	tokenizer=__lowerCamelCase						)
 image_processor.save_pretrained(repo.local_dir						)
 processor.save_pretrained(repo.local_dir						)

 repo.git_add()
 repo.git_commit("Upload model and processor"						)
 repo.git_push()


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE						 =     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--owlvit_version""",
        default=None,
        type=str,
        required=True,
        help="""OWL-ViT model name [clip_b16, clip_b32, clip_l14].""",
    )
    parser.add_argument(
        """--owlvit_checkpoint""", default=None, type=str, required=True, help="""Path to flax model checkpoint."""
    )
    parser.add_argument("""--hf_config""", default=None, type=str, required=True, help="""Path to HF model config.""")
    parser.add_argument(
        """--pytorch_dump_folder_path""", default="""hf_model""", type=str, help="""Path to the output PyTorch model."""
    )
    _SCREAMING_SNAKE_CASE						 =     parser.parse_args()

    # Initialize PyToch clip model
    _SCREAMING_SNAKE_CASE						 =     args.owlvit_version
    if model_name == "clip_b16":
        _SCREAMING_SNAKE_CASE						 =     CONFIGS["""vit_b16"""]
    elif model_name == "clip_b32":
        _SCREAMING_SNAKE_CASE						 =     CONFIGS["""vit_b32"""]
    elif model_name == "clip_l14":
        _SCREAMING_SNAKE_CASE						 =     CONFIGS["""vit_l14"""]

    # Load from checkpoint and convert params to float-32
    _SCREAMING_SNAKE_CASE						 =     checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["""optimizer"""]["""target"""]
    _SCREAMING_SNAKE_CASE						 =     jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
    del variables

    # Convert CLIP backbone
    _SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE						 =     convert_clip_backbone(flax_params, torch_config)

    convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)





import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
   import torch


__UpperCAmelCase   						=     logging.get_logger(__name__)





class 				UpperCamelCase__		:






       """simple docstring"""


       def __init__(							self ,     _A ,     _A=21 ,     _A=[] ,     _A=None	)				->				List[Any]:
          SCREAMING_SNAKE_CASE_	=      model
          SCREAMING_SNAKE_CASE_	=      trace_batch_nums
          SCREAMING_SNAKE_CASE_	=      abort_after_batch_num

          # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
          SCREAMING_SNAKE_CASE_	=      collections.deque([] ,     _A	)
          SCREAMING_SNAKE_CASE_	=      []
          SCREAMING_SNAKE_CASE_	=      0
          SCREAMING_SNAKE_CASE_	=      0
          SCREAMING_SNAKE_CASE_	=      False
          SCREAMING_SNAKE_CASE_	=      '''                 '''

          self.analyse_model()

          self.register_forward_hook()



       def     _UpperCamelCase	(							self ,     _A=None	)				->				Any:
          if frame is not None:
             self.expand_frame(_A	)
          self.frames.append('''\n'''.join(self.frame	)	)
          SCREAMING_SNAKE_CASE_	=      []  # start a new frame



       def     _UpperCamelCase	(							self ,     _A	)				->				List[Any]:
          self.frame.append(_A	)



       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          print('''\n'''.join(self.frames	)	)
          SCREAMING_SNAKE_CASE_	=      []



       def     _UpperCamelCase	(							self	)				->				int:
          SCREAMING_SNAKE_CASE_	=      []



       def     _UpperCamelCase	(							self	)				->				List[str]:
          print(F'''\nDetected inf/nan during batch_number={self.batch_number}'''	)
          print(F'''Last {len(self.frames	)} forward frames:'''	)
          print(F'''{"abs min":8} {"abs max":8} metadata'''	)
          print('''\n'''.join(self.frames	)	)
          print('''\n\n'''	)
          SCREAMING_SNAKE_CASE_	=      []



       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          # extract the fully qualified module names, to be able to report at run time. e.g.:
          # encoder.block.2.layer.0.SelfAttention.o
          #
          # for shared weights only the first shared module name will be registered
          SCREAMING_SNAKE_CASE_	=      {m: name for name, m in self.model.named_modules()}
          # self.longest_module_name = max(len(v) for v in self.module_names.values())



       def     _UpperCamelCase	(							self ,     _A ,     _A	)				->				Union[str, Any]:
          if torch.is_tensor(_A	):
             self.expand_frame(get_abs_min_max(_A ,     _A	)	)
             if detect_overflow(_A ,     _A	):
                SCREAMING_SNAKE_CASE_	=      True
          elif var is None:
             self.expand_frame(F'''{"None":>17} {ctx}'''	)
          else:
             self.expand_frame(F'''{"not a tensor":>17} {ctx}'''	)



       def     _UpperCamelCase	(							self	)				->				List[str]:
          self.expand_frame(F'''\n\n{self.prefix} *** Starting batch number={self.batch_number} ***'''	)
          self.expand_frame(F'''{"abs min":8} {"abs max":8} metadata'''	)



       def     _UpperCamelCase	(							self	)				->				List[Any]:
          self.expand_frame(F'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''	)



       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A	)				->				Union[str, Any]:
          self.expand_frame(F'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''	)

          # params
          for name, p in module.named_parameters(recurse=_A	):
             self.analyse_variable(_A ,     _A	)

          # inputs
          if isinstance(_A ,     _A	):
             for i, x in enumerate(_A	):
                self.analyse_variable(_A ,     F'''input[{i}]'''	)
          else:
             self.analyse_variable(_A ,     '''input'''	)

          # outputs
          if isinstance(_A ,     _A	):
             for i, x in enumerate(_A	):
                # possibly a tuple of tuples
                if isinstance(_A ,     _A	):
                   for j, y in enumerate(_A	):
                      self.analyse_variable(_A ,     F'''output[{i}][{j}]'''	)
                else:
                   self.analyse_variable(_A ,     F'''output[{i}]'''	)
          else:
             self.analyse_variable(_A ,     '''output'''	)

          self.save_frame()



       def     _UpperCamelCase	(							self	)				->				List[Any]:
          self.model.apply(self._register_forward_hook	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Tuple:
          module.register_forward_hook(self.forward_hook	)




       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A	)				->				Any:
          # - input is a tuple of packed inputs (could be non-Tensors)
          # - output could be a Tensor or a tuple of Tensors and non-Tensors

          SCREAMING_SNAKE_CASE_	=      False

          SCREAMING_SNAKE_CASE_	=      True if self.batch_number in self.trace_batch_nums else False
          if trace_mode:
             self.reset_saved_frames()

          if self.total_calls == 0:
             self.batch_start_frame()
          self.total_calls += 1

          # count batch numbers - the very first forward hook of the batch will be called when the
          # batch completes - i.e. it gets called very last - we know this batch has finished
          if module == self.model:
             self.batch_number += 1
             SCREAMING_SNAKE_CASE_	=      True

          self.create_frame(_A ,     _A ,     _A	)

          # if last_frame_of_batch:
          #     self.batch_end_frame()

          if trace_mode:
             self.trace_frames()

          if last_frame_of_batch:
             self.batch_start_frame()

          if self.detected_overflow and not trace_mode:
             self.dump_saved_frames()

             # now we can abort, as it's pointless to continue running
             raise ValueError(
                 '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
                 '''Please scroll up above this traceback to see the activation values prior to this event.'''	)

          # abort after certain batch if requested to do so
          if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
             raise ValueError(
                 F'''DebugUnderflowOverflow: aborting after {self.batch_number} batches due to'''
                 F''' `abort_after_batch_num={self.abort_after_batch_num}` arg'''	)







def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      var.abs()
   return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      False
   if torch.isnan(__lowerCamelCase						).any().item():
      SCREAMING_SNAKE_CASE_	=      True
      print(F'''{ctx} has nans'''						)
   if torch.isinf(__lowerCamelCase						).any().item():
      SCREAMING_SNAKE_CASE_	=      True
      print(F'''{ctx} has infs'''						)

   # if needed to monitor large elements can enable the following
   if 0:  # and detected:
      SCREAMING_SNAKE_CASE_	=      var[torch.ge(var.abs(),  1_00						)]
      if naaa.numel() > 0:
         print(F'''{ctx}:  n100={naaa.numel()}'''						)
      SCREAMING_SNAKE_CASE_	=      var[torch.ge(var.abs(),  10_00						)]
      if naaaa.numel() > 0:
         print(F'''{ctx}: n1000={naaaa.numel()}'''						)
      SCREAMING_SNAKE_CASE_	=      var[torch.ge(var.abs(),  1_00_00						)]
      if naaaaa.numel() > 0:
         print(F'''{ctx}: n10000={naaaaa.numel()}'''						)

   if 0:
      print(F'''min={var.min():9.2e} max={var.max():9.2e}'''						)

   if 0:
      print(F'''min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})'''						)

   return detected





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       ="underflow_overflow"
       UpperCAmelCase_				       ="tpu_metrics_debug"


