



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 		snake_case_			(		_UpperCamelCase			: str							):
   __lowerCamelCase													=    SwinConfig.from_pretrained(
       '''microsoft/swin-tiny-patch4-window7-224''' ,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							)
   __lowerCamelCase													=    MaskFormerConfig(backbone_config=_UpperCamelCase							)

   __lowerCamelCase													=    '''huggingface/label-files'''
   if "ade20k-full" in model_name:
      # this should be ok
      __lowerCamelCase													=    8_47
      __lowerCamelCase													=    '''maskformer-ade20k-full-id2label.json'''
   elif "ade" in model_name:
      # this should be ok
      __lowerCamelCase													=    1_50
      __lowerCamelCase													=    '''ade20k-id2label.json'''
   elif "coco-stuff" in model_name:
      # this should be ok
      __lowerCamelCase													=    1_71
      __lowerCamelCase													=    '''maskformer-coco-stuff-id2label.json'''
   elif "coco" in model_name:
      # TODO
      __lowerCamelCase													=    1_33
      __lowerCamelCase													=    '''coco-panoptic-id2label.json'''
   elif "cityscapes" in model_name:
      # this should be ok
      __lowerCamelCase													=    19
      __lowerCamelCase													=    '''cityscapes-id2label.json'''
   elif "vistas" in model_name:
      # this should be ok
      __lowerCamelCase													=    65
      __lowerCamelCase													=    '''mapillary-vistas-id2label.json'''

   __lowerCamelCase													=    json.load(open(hf_hub_download(_UpperCamelCase ,_UpperCamelCase ,repo_type='''dataset'''							) ,'''r'''							)							)
   __lowerCamelCase													=    {int(_UpperCamelCase							): v for k, v in idalabel.items()}

   return config





def 		snake_case_			(		_UpperCamelCase			: Optional[int]							):
   __lowerCamelCase													=    []
   # 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 		snake_case_			(		_UpperCamelCase			: List[str] ,_UpperCamelCase			: Optional[int] ,_UpperCamelCase			: Dict							):
   __lowerCamelCase													=    dct.pop(_UpperCamelCase							)
   __lowerCamelCase													=    val





def 		snake_case_			(		_UpperCamelCase			: int ,_UpperCamelCase			: Optional[int]							):
   __lowerCamelCase													=    [int(backbone_config.embed_dim * 2**i							) for i in range(len(backbone_config.depths							)							)]
   for i in range(len(backbone_config.depths							)							):
      __lowerCamelCase													=    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)
         __lowerCamelCase													=    state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""							)
         __lowerCamelCase													=    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
         __lowerCamelCase													=    in_proj_weight[:dim, :]
         __lowerCamelCase													=    in_proj_bias[: dim]
         __lowerCamelCase													=    in_proj_weight[
             dim : dim * 2, :
         ]
         __lowerCamelCase													=    in_proj_bias[
             dim : dim * 2
         ]
         __lowerCamelCase													=    in_proj_weight[
             -dim :, :
         ]
         __lowerCamelCase													=    in_proj_bias[-dim :]
         # fmt: on





def 		snake_case_			(		_UpperCamelCase			: Optional[Any] ,_UpperCamelCase			: List[str]							):
   # fmt: off
   __lowerCamelCase													=    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)
      __lowerCamelCase													=    state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""							)
      __lowerCamelCase													=    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
      __lowerCamelCase													=    in_proj_weight[: hidden_size, :]
      __lowerCamelCase													=    in_proj_bias[:config.hidden_size]
      __lowerCamelCase													=    in_proj_weight[hidden_size : hidden_size * 2, :]
      __lowerCamelCase													=    in_proj_bias[hidden_size : hidden_size * 2]
      __lowerCamelCase													=    in_proj_weight[-hidden_size :, :]
      __lowerCamelCase													=    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)
      __lowerCamelCase													=    state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""							)
      __lowerCamelCase													=    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
      __lowerCamelCase													=    in_proj_weight[: hidden_size, :]
      __lowerCamelCase													=    in_proj_bias[:config.hidden_size]
      __lowerCamelCase													=    in_proj_weight[hidden_size : hidden_size * 2, :]
      __lowerCamelCase													=    in_proj_bias[hidden_size : hidden_size * 2]
      __lowerCamelCase													=    in_proj_weight[-hidden_size :, :]
      __lowerCamelCase													=    in_proj_bias[-hidden_size :]
   # fmt: on





def 		snake_case_			(		):
   __lowerCamelCase													=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   __lowerCamelCase													=    Image.open(requests.get(_UpperCamelCase ,stream=_UpperCamelCase							).raw							)
   return im





@torch.no_grad()
def 		snake_case_			(		_UpperCamelCase			: str ,_UpperCamelCase			: str ,_UpperCamelCase			: str ,_UpperCamelCase			: bool = False							):
   __lowerCamelCase													=    get_maskformer_config(_UpperCamelCase							)

   # load original state_dict
   with open(_UpperCamelCase ,'''rb'''							) as f:
      __lowerCamelCase													=    pickle.load(_UpperCamelCase							)
   __lowerCamelCase													=    data['''model''']

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

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

   # update to torch tensors
   for key, value in state_dict.items():
      __lowerCamelCase													=    torch.from_numpy(_UpperCamelCase							)

   # load 🤗 model
   __lowerCamelCase													=    MaskFormerForInstanceSegmentation(_UpperCamelCase							)
   model.eval()

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

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

   # verify results
   __lowerCamelCase													=    prepare_img()
   if "vistas" in model_name:
      __lowerCamelCase													=    65
   elif "cityscapes" in model_name:
      __lowerCamelCase													=    6_55_35
   else:
      __lowerCamelCase													=    2_55
   __lowerCamelCase													=    True if '''ade''' in model_name else False
   __lowerCamelCase													=    MaskFormerImageProcessor(ignore_index=_UpperCamelCase ,reduce_labels=_UpperCamelCase							)

   __lowerCamelCase													=    image_processor(_UpperCamelCase ,return_tensors='''pt'''							)

   __lowerCamelCase													=    model(**_UpperCamelCase							)

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

   if model_name == "maskformer-swin-tiny-ade":
      __lowerCamelCase													=    torch.tensor(
          [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]							)
   assert torch.allclose(outputs.class_queries_logits[0, :3, :3] ,_UpperCamelCase ,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(_UpperCamelCase							).mkdir(exist_ok=_UpperCamelCase							)
      model.save_pretrained(_UpperCamelCase							)
      image_processor.save_pretrained(_UpperCamelCase							)

   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
  )











import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a_			=					"""true"""





def 		a__			(		_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: List[str]=82 ,_UpperCamelCase			: Optional[Any]=16							):
   set_seed(42							)
   __lowerCamelCase													=    RegressionModel()
   __lowerCamelCase													=    deepcopy(_UpperCamelCase							)
   __lowerCamelCase													=    RegressionDataset(length=_UpperCamelCase							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,batch_size=_UpperCamelCase							)
   model.to(accelerator.device							)
   __lowerCamelCase							,__lowerCamelCase													=    accelerator.prepare(_UpperCamelCase ,_UpperCamelCase							)
   return model, ddp_model, dataloader





def 		a__			(		_UpperCamelCase			: Accelerator ,_UpperCamelCase			: str=False							):
   __lowerCamelCase													=    AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''							)
   __lowerCamelCase													=    load_dataset('''glue''' ,'''mrpc''' ,split='''validation'''							)

   def tokenize_function(_UpperCamelCase			: int							):
      __lowerCamelCase													=    tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase							)
      return outputs

   with accelerator.main_process_first():
      __lowerCamelCase													=    dataset.map(
          _UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)

   __lowerCamelCase													=    tokenized_datasets.rename_column('''label''' ,'''labels'''							)

   def collate_fn(_UpperCamelCase			: Any							):
      if use_longest:
         return tokenizer.pad(_UpperCamelCase ,padding='''longest''' ,return_tensors='''pt'''							)
      return tokenizer.pad(_UpperCamelCase ,padding='''max_length''' ,max_length=1_28 ,return_tensors='''pt'''							)

   return DataLoader(_UpperCamelCase ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=16							)





def 		a__			(		_UpperCamelCase			: Dict ,_UpperCamelCase			: List[str]							):
   __lowerCamelCase													=    Accelerator(dispatch_batches=_UpperCamelCase ,split_batches=_UpperCamelCase							)
   __lowerCamelCase													=    get_dataloader(_UpperCamelCase ,not dispatch_batches							)
   __lowerCamelCase													=    AutoModelForSequenceClassification.from_pretrained(
       '''hf-internal-testing/mrpc-bert-base-cased''' ,return_dict=_UpperCamelCase							)
   __lowerCamelCase							,__lowerCamelCase													=    accelerator.prepare(_UpperCamelCase ,_UpperCamelCase							)
   return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def 		a__			(		_UpperCamelCase			: Dict ,_UpperCamelCase			: Optional[Any] ,_UpperCamelCase			: Union[str, Any]							):
   __lowerCamelCase													=    []
   for batch in dataloader:
      __lowerCamelCase							,__lowerCamelCase													=    batch.values()
      with torch.no_grad():
         __lowerCamelCase													=    model(_UpperCamelCase							)
         __lowerCamelCase							,__lowerCamelCase													=    accelerator.gather_for_metrics((logit, target)							)
         logits_and_targets.append((logit, target)							)
   __lowerCamelCase							,__lowerCamelCase													=    [], []
   for logit, targ in logits_and_targets:
      logits.append(_UpperCamelCase							)
      targs.append(_UpperCamelCase							)
   __lowerCamelCase							,__lowerCamelCase													=    torch.cat(_UpperCamelCase							), torch.cat(_UpperCamelCase							)
   return logits, targs





def 		a__			(		_UpperCamelCase			: Accelerator ,_UpperCamelCase			: List[Any]=82 ,_UpperCamelCase			: str=False ,_UpperCamelCase			: List[str]=False ,_UpperCamelCase			: Optional[int]=16							):
   __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    get_basic_setup(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase							)
   __lowerCamelCase							,__lowerCamelCase													=    generate_predictions(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase							)
   assert (
       len(_UpperCamelCase							) == num_samples
   ), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCamelCase							)}"""





def 		a__			(		_UpperCamelCase			: bool = False ,_UpperCamelCase			: bool = False							):
   __lowerCamelCase													=    evaluate.load('''glue''' ,'''mrpc'''							)
   __lowerCamelCase							,__lowerCamelCase													=    get_mrpc_setup(_UpperCamelCase ,_UpperCamelCase							)
   # First do baseline
   __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    setup['''no''']
   model.to(_UpperCamelCase							)
   model.eval()
   for batch in dataloader:
      batch.to(_UpperCamelCase							)
      with torch.inference_mode():
         __lowerCamelCase													=    model(**_UpperCamelCase							)
      __lowerCamelCase													=    outputs.logits.argmax(dim=-1							)
      metric.add_batch(predictions=_UpperCamelCase ,references=batch['''labels''']							)
   __lowerCamelCase													=    metric.compute()

   # Then do distributed
   __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    setup['''ddp''']
   model.eval()
   for batch in dataloader:
      with torch.inference_mode():
         __lowerCamelCase													=    model(**_UpperCamelCase							)
      __lowerCamelCase													=    outputs.logits.argmax(dim=-1							)
      __lowerCamelCase													=    batch['''labels''']
      __lowerCamelCase							,__lowerCamelCase													=    accelerator.gather_for_metrics((preds, references)							)
      metric.add_batch(predictions=_UpperCamelCase ,references=_UpperCamelCase							)
   __lowerCamelCase													=    metric.compute()

   for key in "accuracy f1".split():
      assert math.isclose(
          baseline[key] ,distributed[key]							), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""





def 		a__			(		):
   __lowerCamelCase													=    Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase							)
   if accelerator.is_local_main_process:
      datasets.utils.logging.set_verbosity_warning()
      transformers.utils.logging.set_verbosity_warning()
   else:
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
   # These are a bit slower so they should only be ran on the GPU or TPU
   if torch.cuda.is_available() or is_tpu_available():
      if accelerator.is_local_main_process:
         print('''**Testing gather_for_metrics**'''							)
      for split_batches in [True, False]:
         for dispatch_batches in [True, False]:
            if accelerator.is_local_main_process:
               print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"""							)
            test_mrpc(_UpperCamelCase ,_UpperCamelCase							)
            accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print('''**Test torch metrics**'''							)
   for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
         __lowerCamelCase													=    Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase							)
         if accelerator.is_local_main_process:
            print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""							)
         test_torch_metrics(_UpperCamelCase ,99							)
         accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print('''**Test last batch is not dropped when perfectly divisible**'''							)
   __lowerCamelCase													=    Accelerator()
   test_torch_metrics(_UpperCamelCase ,5_12							)
   accelerator.state._reset_state()





def 		a__			(		_UpperCamelCase			: Optional[int]							):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()






