'''simple docstring'''

import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
A_       						=							logging.get_logger(__name__)





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					SwinConfig.from_pretrained(
      "microsoft/swin-tiny-patch4-window7-224"      ,       out_features=["stage1", "stage2", "stage3", "stage4"] )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					MaskFormerConfig(backbone_config=lowerCAmelCase__ )

  SCREAMING_SNAKE_CASE:Any						      =					"huggingface/label-files"
  if "ade20k-full" in model_name:
    # this should be ok
    SCREAMING_SNAKE_CASE:int						      =					847
    SCREAMING_SNAKE_CASE:int						      =					"maskformer-ade20k-full-id2label.json"
  elif "ade" in model_name:
    # this should be ok
    SCREAMING_SNAKE_CASE:int						      =					150
    SCREAMING_SNAKE_CASE:str						      =					"ade20k-id2label.json"
  elif "coco-stuff" in model_name:
    # this should be ok
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					171
    SCREAMING_SNAKE_CASE:Any						      =					"maskformer-coco-stuff-id2label.json"
  elif "coco" in model_name:
    # TODO
    SCREAMING_SNAKE_CASE:List[str]						      =					133
    SCREAMING_SNAKE_CASE:Any						      =					"coco-panoptic-id2label.json"
  elif "cityscapes" in model_name:
    # this should be ok
    SCREAMING_SNAKE_CASE:int						      =					19
    SCREAMING_SNAKE_CASE:int						      =					"cityscapes-id2label.json"
  elif "vistas" in model_name:
    # this should be ok
    SCREAMING_SNAKE_CASE:Optional[int]						      =					65
    SCREAMING_SNAKE_CASE:List[str]						      =					"mapillary-vistas-id2label.json"

  SCREAMING_SNAKE_CASE:List[str]						      =					json.load(open(hf_hub_download(lowerCAmelCase__      ,       lowerCAmelCase__      ,       repo_type="dataset" )      ,       "r" ) )
  SCREAMING_SNAKE_CASE:int						      =					{int(lowerCAmelCase__ ): v for k, v in idalabel.items()}

  return config





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					[]
  # stem
  # fmt: off
  rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight") )
  rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias") )
  rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight") )
  rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias") )
  # stages
  for i in range(len(config.backbone_config.depths ) ):
    for j in range(config.backbone_config.depths[i] ):
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''') )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''') )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''') )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''') )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''') )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''') )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''') )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''') )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''') )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''') )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''') )
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''') )

    if i < 3:
      rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''') )
      rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''') )
      rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''') )
    rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''') )
    rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''') )

  # FPN
  rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight") )
  rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight") )
  rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias") )
  for source_index, target_index in zip(range(3      ,       0      ,       -1 )      ,       range(0      ,       3 ) ):
    rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''') )
    rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''') )
    rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''') )
    rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''') )
    rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''') )
    rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''') )
  rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight") )
  rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias") )

  # Transformer decoder
  for idx in range(config.decoder_config.decoder_layers ):
    # self-attention out projection
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''') )
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''') )
    # cross-attention out projection
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''') )
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''') )
    # MLP 1
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''') )
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''') )
    # MLP 2
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''') )
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''') )
    # layernorm 1 (self-attention layernorm)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''') )
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''') )
    # layernorm 2 (cross-attention layernorm)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''') )
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''') )
    # layernorm 3 (final layernorm)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''') )
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''') )

  rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight") )
  rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias") )

  # heads on top
  rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight") )

  rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight") )
  rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias") )

  rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight") )
  rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias") )

  for i in range(3 ):
    rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''') )
    rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''') )
  # fmt: on

  return rename_keys





def    A_  ( snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:str						      =					dct.pop(lowerCAmelCase__ )
  SCREAMING_SNAKE_CASE:List[str]						      =					val





def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:int						      =					[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
  for i in range(len(backbone_config.depths ) ):
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					num_features[i]
    for j in range(backbone_config.depths[i] ):
      # fmt: off
      # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
      SCREAMING_SNAKE_CASE:int						      =					state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight''' )
      SCREAMING_SNAKE_CASE:Any						      =					state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias''' )
      # next, add query, keys and values (in that order) to the state dict
      SCREAMING_SNAKE_CASE:Dict						      =					in_proj_weight[:dim, :]
      SCREAMING_SNAKE_CASE:Optional[Any]						      =					in_proj_bias[: dim]
      SCREAMING_SNAKE_CASE:Tuple						      =					in_proj_weight[
          dim : dim * 2, :
      ]
      SCREAMING_SNAKE_CASE:Any						      =					in_proj_bias[
          dim : dim * 2
      ]
      SCREAMING_SNAKE_CASE:Optional[int]						      =					in_proj_weight[
          -dim :, :
      ]
      SCREAMING_SNAKE_CASE:Dict						      =					in_proj_bias[-dim :]
      # fmt: on





def    A_  ( snake_case      ,       snake_case ):
  # fmt: off
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					config.decoder_config.hidden_size
  for idx in range(config.decoder_config.decoder_layers ):
    # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
    SCREAMING_SNAKE_CASE:List[str]						      =					state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight''' )
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias''' )
    # next, add query, keys and values (in that order) to the state dict
    SCREAMING_SNAKE_CASE:List[str]						      =					in_proj_weight[: hidden_size, :]
    SCREAMING_SNAKE_CASE:int						      =					in_proj_bias[:config.hidden_size]
    SCREAMING_SNAKE_CASE:Optional[int]						      =					in_proj_weight[hidden_size : hidden_size * 2, :]
    SCREAMING_SNAKE_CASE:Optional[int]						      =					in_proj_bias[hidden_size : hidden_size * 2]
    SCREAMING_SNAKE_CASE:Optional[int]						      =					in_proj_weight[-hidden_size :, :]
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					in_proj_bias[-hidden_size :]
    # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight''' )
    SCREAMING_SNAKE_CASE:List[Any]						      =					state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias''' )
    # next, add query, keys and values (in that order) to the state dict
    SCREAMING_SNAKE_CASE:Optional[int]						      =					in_proj_weight[: hidden_size, :]
    SCREAMING_SNAKE_CASE:Tuple						      =					in_proj_bias[:config.hidden_size]
    SCREAMING_SNAKE_CASE:int						      =					in_proj_weight[hidden_size : hidden_size * 2, :]
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					in_proj_bias[hidden_size : hidden_size * 2]
    SCREAMING_SNAKE_CASE:Any						      =					in_proj_weight[-hidden_size :, :]
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					in_proj_bias[-hidden_size :]
  # fmt: on





def    A_  ( ):
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					"http://images.cocodataset.org/val2017/000000039769.jpg"
  SCREAMING_SNAKE_CASE:str						      =					Image.open(requests.get(lowerCAmelCase__      ,       stream=lowerCAmelCase__ ).raw )
  return im





@torch.no_grad()
def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case = False ):
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					get_maskformer_config(lowerCAmelCase__ )

  # load original state_dict
  with open(lowerCAmelCase__      ,       "rb" ) as f:
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					pickle.load(lowerCAmelCase__ )
  SCREAMING_SNAKE_CASE:Dict						      =					data["model"]

  # for name, param in state_dict.items():
  #     print(name, param.shape)

  # rename keys
  SCREAMING_SNAKE_CASE:str						      =					create_rename_keys(lowerCAmelCase__ )
  for src, dest in rename_keys:
    rename_key(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ )
  read_in_swin_q_k_v(lowerCAmelCase__      ,       config.backbone_config )
  read_in_decoder_q_k_v(lowerCAmelCase__      ,       lowerCAmelCase__ )

  # update to torch tensors
  for key, value in state_dict.items():
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.from_numpy(lowerCAmelCase__ )

  # load 🤗 model
  SCREAMING_SNAKE_CASE:Any						      =					MaskFormerForInstanceSegmentation(lowerCAmelCase__ )
  model.eval()

  for name, param in model.named_parameters():
    print(lowerCAmelCase__      ,       param.shape )

  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Any						      =					model.load_state_dict(lowerCAmelCase__      ,       strict=lowerCAmelCase__ )
  assert missing_keys == [
      "model.pixel_level_module.encoder.model.layernorm.weight",
      "model.pixel_level_module.encoder.model.layernorm.bias",
  ]
  assert len(lowerCAmelCase__ ) == 0, F'''Unexpected keys: {unexpected_keys}'''

  # verify results
  SCREAMING_SNAKE_CASE:Tuple						      =					prepare_img()
  if "vistas" in model_name:
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					65
  elif "cityscapes" in model_name:
    SCREAMING_SNAKE_CASE:Any						      =					65535
  else:
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					255
  SCREAMING_SNAKE_CASE:List[str]						      =					True if "ade" in model_name else False
  SCREAMING_SNAKE_CASE:Any						      =					MaskFormerImageProcessor(ignore_index=lowerCAmelCase__      ,       reduce_labels=lowerCAmelCase__ )

  SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_processor(lowerCAmelCase__      ,       return_tensors="pt" )

  SCREAMING_SNAKE_CASE:str						      =					model(**lowerCAmelCase__ )

  print("Logits:"      ,       outputs.class_queries_logits[0, :3, :3] )

  if model_name == "maskformer-swin-tiny-ade":
    SCREAMING_SNAKE_CASE:Optional[int]						      =					torch.tensor(
        [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]] )
  assert torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,       lowerCAmelCase__      ,       atol=1e-4 )
  print("Looks ok!" )

  if pytorch_dump_folder_path is not None:
    print(F'''Saving model and image processor to {pytorch_dump_folder_path}''' )
    Path(lowerCAmelCase__ ).mkdir(exist_ok=lowerCAmelCase__ )
    model.save_pretrained(lowerCAmelCase__ )
    image_processor.save_pretrained(lowerCAmelCase__ )

  if push_to_hub:
    print("Pushing model and image processor to the hub..." )
    model.push_to_hub(F'''nielsr/{model_name}''' )
    image_processor.push_to_hub(F'''nielsr/{model_name}''' )


if __name__ == "__main__":
    A_       						=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--model_name",
        default="maskformer-swin-tiny-ade",
        type=str,
        help=("Name of the MaskFormer model you'd like to convert",),
    )
    parser.add_argument(
        "--checkpoint_path",
        default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
        type=str,
        help="Path to the original state dict (.pth file).",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
    )

    A_       						=							parser.parse_args()
    convert_maskformer_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )









'''simple docstring'''

# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
#     ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
#     --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
#     --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
#    1. --tf32 0 --fp16 0
#    2. --tf32 0 --fp16 1
#    3. --tf32 0 --bf16 1
#    4. --tf32 1 --fp16 0
#    5. --tf32 1 --fp16 1
#    6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
#    --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
#    --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
#    1. --tf32 0
#    2. --tf32 0 --fp16
#    3. --tf32 0 --bf16
#    4. --tf32 1
#    5. --tf32 1 --fp16
#    6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
#    `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation       |     Train |   Diff |   Train |
# |                 |   samples |      % |    loss |
# |                 |       per |        |         |
# |                 |    second |        |         |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0        |    285.11 |      0 |    2.51 |
# | --tf32 1        |    342.09 |     20 |    2.51 |
# | --fp16 --tf32 0 |    423.49 |     49 |    2.51 |
# | --fp16 --tf32 1 |    423.13 |     48 |    2.51 |
# | --bf16 --tf32 0 |    416.80 |     46 |    2.52 |
# | --bf16 --tf32 1 |    415.87 |     46 |    2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
#    --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#

import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean

import pandas as pd
import torch
from tqdm import tqdm

import transformers


A_       						=							float("nan")





class 							_snake_case :
   def __init__( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					sys.stdout
     SCREAMING_SNAKE_CASE:List[Any]						      =					open(SCREAMING_SNAKE_CASE__     ,"a"							)
   def __getattr__( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     return getattr(self.stdout     ,SCREAMING_SNAKE_CASE__							)







   def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      str							):
     self.stdout.write(SCREAMING_SNAKE_CASE__							)
     # strip tqdm codes
     self.file.write(re.sub(R"^.*\r"     ,""     ,SCREAMING_SNAKE_CASE__     ,0     ,re.M							)							)





def    A_  ( snake_case=80      ,       snake_case=False ):
  SCREAMING_SNAKE_CASE:Tuple						      =					[]

  # deal with critical env vars
  SCREAMING_SNAKE_CASE:Optional[int]						      =					["CUDA_VISIBLE_DEVICES"]
  for key in env_keys:
    SCREAMING_SNAKE_CASE:Any						      =					os.environ.get(snake_case      ,       snake_case )
    if val is not None:
      cmd.append(F'''{key}={val}''' )

    # python executable (not always needed if the script is executable)
  SCREAMING_SNAKE_CASE:Optional[int]						      =					sys.executable if full_python_path else sys.executable.split("/" )[-1]
  cmd.append(snake_case )

  # now the normal args
  cmd += list(map(shlex.quote      ,       sys.argv ) )

  # split up into up to MAX_WIDTH lines with shell multi-line escapes
  SCREAMING_SNAKE_CASE:str						      =					[]
  SCREAMING_SNAKE_CASE:str						      =					""
  while len(snake_case ) > 0:
    current_line += F'''{cmd.pop(0 )} '''
    if len(snake_case ) == 0 or len(snake_case ) + len(cmd[0] ) + 1 > max_width - 1:
      lines.append(snake_case )
      SCREAMING_SNAKE_CASE:Tuple						      =					""
  return "\\\n".join(snake_case )





def    A_  ( snake_case      ,       snake_case ):
  # unwrap multi-line input
  SCREAMING_SNAKE_CASE:List[Any]						      =					re.sub(r"[\\\n]+"      ,       " "      ,       args.base_cmd )

  # remove --output_dir if any and set our own
  SCREAMING_SNAKE_CASE:int						      =					re.sub("--output_dir\s+[^\s]+"      ,       ""      ,       args.base_cmd )
  args.base_cmd += F''' --output_dir {output_dir}'''

  # ensure we have --overwrite_output_dir
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					re.sub("--overwrite_output_dir\s+"      ,       ""      ,       args.base_cmd )
  args.base_cmd += " --overwrite_output_dir"

  return [sys.executable] + shlex.split(args.base_cmd )





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case ):
  # Enable to debug everything but the run itself, to do it fast and see the progress.
  # This is useful for debugging the output formatting quickly - we can remove it later once
  # everybody is happy with the output
  if 0:
    import random
    from time import sleep

    sleep(0 )
    return dict(
        {k: random.uniform(0      ,       100 ) for k in metric_keys}      ,       **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.2222_2222] )}      ,       )

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					subprocess.run(snake_case      ,       capture_output=snake_case      ,       text=snake_case )

  if verbose:
    print("STDOUT"      ,       result.stdout )
    print("STDERR"      ,       result.stderr )

  # save the streams
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					variation.replace(" "      ,       "-" )
  with open(Path(snake_case ) / F'''log.{prefix}.stdout.txt'''      ,       "w" ) as f:
    f.write(result.stdout )
  with open(Path(snake_case ) / F'''log.{prefix}.stderr.txt'''      ,       "w" ) as f:
    f.write(result.stderr )

  if result.returncode != 0:
    if verbose:
      print("failed" )
    return {target_metric_key: nan}

  with io.open(F'''{output_dir}/all_results.json'''      ,       "r"      ,       encoding="utf-8" ) as f:
    SCREAMING_SNAKE_CASE:List[str]						      =					json.load(snake_case )

  # filter out just the keys we want
  return {k: v for k, v in metrics.items() if k in metric_keys}





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       ):
  SCREAMING_SNAKE_CASE:str						      =					[]
  SCREAMING_SNAKE_CASE:List[str]						      =					[]
  SCREAMING_SNAKE_CASE:Any						      =					F'''{id}: {variation:<{longest_variation_len}}'''
  SCREAMING_SNAKE_CASE:Tuple						      =					F'''{preamble}: '''
  SCREAMING_SNAKE_CASE:Any						      =					set(report_metric_keys + [target_metric_key] )
  for i in tqdm(range(snake_case )      ,       desc=snake_case      ,       leave=snake_case ):
    SCREAMING_SNAKE_CASE:Dict						      =					process_run_single(
        snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case )
    SCREAMING_SNAKE_CASE:Tuple						      =					single_run_metrics[target_metric_key]
    if not math.isnan(snake_case ):
      metrics.append(snake_case )
      results.append(snake_case )
      outcome += "✓"
    else:
      outcome += "✘"
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					F'''\33[2K\r{outcome}'''
  if len(snake_case ) > 0:
    SCREAMING_SNAKE_CASE:Dict						      =					{k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()}
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					round(mean_metrics[target_metric_key]      ,       2 )
    SCREAMING_SNAKE_CASE:int						      =					F'''{outcome} {mean_target}'''
    if len(snake_case ) > 1:
      results_str += F''' {tuple(round(snake_case      ,       2 ) for x in results )}'''
    print(snake_case )
    SCREAMING_SNAKE_CASE:List[str]						      =					variation
    return mean_metrics
  else:
    print(snake_case )
    return {variation_key: variation, target_metric_key: nan}





def    A_  ( ):
  SCREAMING_SNAKE_CASE:int						      =					torch.cuda.get_device_properties(torch.device("cuda" ) )
  return F'''
Datetime    : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" )}

Software:
transformers: {transformers.__version__}
torch       : {torch.__version__}
cuda        : {torch.version.cuda}
python      : {platform.python_version()}

Hardware:
{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**30:0.2f}GB
'''





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					pd.DataFrame(snake_case )
  SCREAMING_SNAKE_CASE:str						      =					"variation"
  SCREAMING_SNAKE_CASE:str						      =					"diff_%"

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					nan
  if base_variation is not None and len(df[df[variation_key] == base_variation] ):
    # this may still return nan
    SCREAMING_SNAKE_CASE:Any						      =					df.loc[df[variation_key] == base_variation][target_metric_key].item()
  if math.isnan(snake_case ):
    # as a fallback, use the minimal value as the sentinel
    SCREAMING_SNAKE_CASE:Any						      =					df.loc[df[target_metric_key] != nan][target_metric_key].min()

  # create diff column if possible
  if not math.isnan(snake_case ):
    SCREAMING_SNAKE_CASE:Tuple						      =					df.apply(
        lambda snake_case : round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value )
        if not math.isnan(r[target_metric_key] )
        else 0      ,       axis="columns"      ,       )

  # re-order columns
  SCREAMING_SNAKE_CASE:Tuple						      =					[variation_key, target_metric_key, diff_key, *report_metric_keys]
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					df.reindex(snake_case      ,       axis="columns" )  # reorder cols

  # capitalize
  SCREAMING_SNAKE_CASE:str						      =					df.rename(str.capitalize      ,       axis="columns" )

  # make the cols as narrow as possible
  SCREAMING_SNAKE_CASE:int						      =					df.rename(lambda snake_case : c.replace("_"      ,       "<br>" )      ,       axis="columns" )
  SCREAMING_SNAKE_CASE:Dict						      =					df.rename(lambda snake_case : c.replace("_"      ,       "\n" )      ,       axis="columns" )

  SCREAMING_SNAKE_CASE:List[Any]						      =					["", "Copy between the cut-here-lines and paste as is to github or a forum"]
  report += ["----------8<-----------------8<--------"]
  report += ["*** Results:", df_github.to_markdown(index=snake_case      ,       floatfmt=".2f" )]
  report += ["```"]
  report += ["*** Setup:", get_versions()]
  report += ["*** The benchmark command line was:", get_original_command()]
  report += ["```"]
  report += ["----------8<-----------------8<--------"]
  report += ["*** Results (console):", df_console.to_markdown(index=snake_case      ,       floatfmt=".2f" )]

  print("\n\n".join(snake_case ) )





def    A_  ( ):
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					argparse.ArgumentParser()
  parser.add_argument(
      "--base-cmd"      ,       default=snake_case      ,       type=snake_case      ,       required=snake_case      ,       help="Base cmd"      ,       )
  parser.add_argument(
      "--variations"      ,       default=snake_case      ,       type=snake_case      ,       nargs="+"      ,       required=snake_case      ,       help="Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'"      ,       )
  parser.add_argument(
      "--base-variation"      ,       default=snake_case      ,       type=snake_case      ,       help="Baseline variation to compare to. if None the minimal target value will be used to compare against"      ,       )
  parser.add_argument(
      "--target-metric-key"      ,       default=snake_case      ,       type=snake_case      ,       required=snake_case      ,       help="Target metric key in output_dir/all_results.json, e.g., train_samples_per_second"      ,       )
  parser.add_argument(
      "--report-metric-keys"      ,       default=""      ,       type=snake_case      ,       help="Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., 'train_loss train_samples"      ,       )
  parser.add_argument(
      "--repeat-times"      ,       default=1      ,       type=snake_case      ,       help="How many times to re-run each variation - an average will be reported"      ,       )
  parser.add_argument(
      "--output_dir"      ,       default="output_benchmark"      ,       type=snake_case      ,       help="The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked"      ,       )
  parser.add_argument(
      "--verbose"      ,       default=snake_case      ,       action="store_true"      ,       help="Whether to show the outputs of each run or just the benchmark progress"      ,       )
  SCREAMING_SNAKE_CASE:int						      =					parser.parse_args()

  SCREAMING_SNAKE_CASE:int						      =					args.output_dir
  Path(snake_case ).mkdir(exist_ok=snake_case )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					get_base_command(snake_case      ,       snake_case )

  # split each dimension into its --foo variations
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[list(map(str.strip      ,       re.split(r"\|"      ,       snake_case ) ) ) for x in args.variations]
  # build a cartesian product of dimensions and convert those back into cmd-line arg strings,
  # while stripping white space for inputs that were empty
  SCREAMING_SNAKE_CASE:str						      =					list(map(str.strip      ,       map(" ".join      ,       itertools.product(*snake_case ) ) ) )
  SCREAMING_SNAKE_CASE:Dict						      =					max(len(snake_case ) for x in variations )

  # split wanted keys
  SCREAMING_SNAKE_CASE:Any						      =					args.report_metric_keys.split()

  # capture prints into a log file for convenience
  SCREAMING_SNAKE_CASE:Any						      =					F'''benchmark-report-{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S" )}.txt'''
  print(F'''\nNote: each run\'s output is also logged under {output_dir}/log.*.std*.txt''' )
  print(F'''and this script\'s output is also piped into {report_fn}''' )

  SCREAMING_SNAKE_CASE:Any						      =					Tee(snake_case )

  print(F'''\n*** Running {len(snake_case )} benchmarks:''' )
  print(F'''Base command: {" ".join(snake_case )}''' )

  SCREAMING_SNAKE_CASE:str						      =					"variation"
  SCREAMING_SNAKE_CASE:Tuple						      =					[]
  for id, variation in enumerate(tqdm(snake_case      ,       desc="Total completion: "      ,       leave=snake_case ) ):
    SCREAMING_SNAKE_CASE:List[str]						      =					base_cmd + variation.split()
    results.append(
        process_run(
            id + 1      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       args.target_metric_key      ,       snake_case      ,       args.repeat_times      ,       snake_case      ,       args.verbose      ,       ) )

  process_results(snake_case      ,       args.target_metric_key      ,       snake_case      ,       args.base_variation      ,       snake_case )


if __name__ == "__main__":
    main()






