



import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		XCLIPTextConfig()

      # derive patch size from model name
      lowerCamelCase								=		model_name.find("patch" )
      lowerCamelCase								=		int(model_name[start_idx + len("patch" ) : start_idx + len("patch" ) + 2] )
      lowerCamelCase								=		XCLIPVisionConfig(patch_size=UpperCAmelCase__   ,   num_frames=UpperCAmelCase__ )

      if "large" in model_name:
            lowerCamelCase								=		768
            lowerCamelCase								=		3072
            lowerCamelCase								=		12

            lowerCamelCase								=		1024
            lowerCamelCase								=		4096
            lowerCamelCase								=		16
            lowerCamelCase								=		24
            lowerCamelCase								=		768
            lowerCamelCase								=		3072

      if model_name == "xclip-large-patch14-16-frames":
            lowerCamelCase								=		336

      lowerCamelCase								=		XCLIPConfig.from_text_vision_configs(UpperCAmelCase__   ,   UpperCAmelCase__ )

      if "large" in model_name:
            lowerCamelCase								=		768

      return config

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      if name == "token_embedding.weight":
            lowerCamelCase								=		name.replace("token_embedding.weight"   ,   "text_model.embeddings.token_embedding.weight" )
      if name == "positional_embedding":
            lowerCamelCase								=		name.replace("positional_embedding"   ,   "text_model.embeddings.position_embedding.weight" )
      if "ln_1" in name:
            lowerCamelCase								=		name.replace("ln_1"   ,   "layer_norm1" )
      if "ln_2" in name:
            lowerCamelCase								=		name.replace("ln_2"   ,   "layer_norm2" )
      if "c_fc" in name:
            lowerCamelCase								=		name.replace("c_fc"   ,   "fc1" )
      if "c_proj" in name:
            lowerCamelCase								=		name.replace("c_proj"   ,   "fc2" )
      if name.startswith("transformer.resblocks" ):
            lowerCamelCase								=		name.replace("transformer.resblocks"   ,   "text_model.encoder.layers" )
      if "attn.out_proj" in name and "message" not in name:
            lowerCamelCase								=		name.replace("attn.out_proj"   ,   "self_attn.out_proj" )
      if "ln_final" in name:
            lowerCamelCase								=		name.replace("ln_final"   ,   "text_model.final_layer_norm" )
      # visual encoder
      if name == "visual.class_embedding":
            lowerCamelCase								=		name.replace("visual.class_embedding"   ,   "vision_model.embeddings.class_embedding" )
      if name == "visual.positional_embedding":
            lowerCamelCase								=		name.replace("visual.positional_embedding"   ,   "vision_model.embeddings.position_embedding.weight" )
      if name.startswith("visual.transformer.resblocks" ):
            lowerCamelCase								=		name.replace("visual.transformer.resblocks"   ,   "vision_model.encoder.layers" )
      if "visual.conv1" in name:
            lowerCamelCase								=		name.replace("visual.conv1"   ,   "vision_model.embeddings.patch_embedding" )
      if "visual.ln_pre" in name:
            lowerCamelCase								=		name.replace("visual.ln_pre"   ,   "vision_model.pre_layernorm" )
      if "visual.ln_post" in name:
            lowerCamelCase								=		name.replace("visual.ln_post"   ,   "vision_model.post_layernorm" )
      if "visual.proj" in name:
            lowerCamelCase								=		name.replace("visual.proj"   ,   "visual_projection.weight" )
      if "text_projection" in name:
            lowerCamelCase								=		name.replace("text_projection"   ,   "text_projection.weight" )
      # things on top
      if "prompts_visual_proj" in name:
            lowerCamelCase								=		name.replace("prompts_visual_proj"   ,   "prompts_visual_projection" )
      if "prompts_visual_ln" in name:
            lowerCamelCase								=		name.replace("prompts_visual_ln"   ,   "prompts_visual_layernorm" )
      # mit
      if name == "mit.positional_embedding":
            lowerCamelCase								=		name.replace("positional"   ,   "position" )
      if name.startswith("mit.resblocks" ):
            lowerCamelCase								=		name.replace("mit.resblocks"   ,   "mit.encoder.layers" )
      # prompts generator
      if name.startswith("prompts_generator.norm" ):
            lowerCamelCase								=		name.replace("prompts_generator.norm"   ,   "prompts_generator.layernorm" )

      return name

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      for key in orig_state_dict.copy().keys():
            lowerCamelCase								=		orig_state_dict.pop(UpperCAmelCase__ )

            if "attn.in_proj" in key:
                  lowerCamelCase								=		key.split("." )
                  if key.startswith("visual" ):
                        lowerCamelCase								=		key_split[3]
                        lowerCamelCase								=		config.vision_config.hidden_size
                        if "message_attn" in key:
                              if "weight" in key:
                                    lowerCamelCase								=		val[
                                        :dim, :
                                    ]
                                    lowerCamelCase								=		val[
                                        dim : dim * 2, :
                                    ]
                                    lowerCamelCase								=		val[
                                        -dim:, :
                                    ]
                              else:
                                    lowerCamelCase								=		val[
                                        :dim
                                    ]
                                    lowerCamelCase								=		val[
                                        dim : dim * 2
                                    ]
                                    lowerCamelCase								=		val[
                                        -dim:
                                    ]
                        else:
                              if "weight" in key:
                                    lowerCamelCase								=		val[
                                        :dim, :
                                    ]
                                    lowerCamelCase								=		val[
                                        dim : dim * 2, :
                                    ]
                                    lowerCamelCase								=		val[
                                        -dim:, :
                                    ]
                              else:
                                    lowerCamelCase								=		val[:dim]
                                    lowerCamelCase								=		val[
                                        dim : dim * 2
                                    ]
                                    lowerCamelCase								=		val[-dim:]
                  elif key.startswith("mit" ):
                        lowerCamelCase								=		key_split[2]
                        lowerCamelCase								=		config.vision_config.mit_hidden_size
                        if "weight" in key:
                              lowerCamelCase								=		val[:dim, :]
                              lowerCamelCase								=		val[dim : dim * 2, :]
                              lowerCamelCase								=		val[-dim:, :]
                        else:
                              lowerCamelCase								=		val[:dim]
                              lowerCamelCase								=		val[dim : dim * 2]
                              lowerCamelCase								=		val[-dim:]
                  else:
                        lowerCamelCase								=		key_split[2]
                        lowerCamelCase								=		config.text_config.hidden_size
                        if "weight" in key:
                              lowerCamelCase								=		val[:dim, :]
                              lowerCamelCase								=		val[
                                  dim : dim * 2, :
                              ]
                              lowerCamelCase								=		val[-dim:, :]
                        else:
                              lowerCamelCase								=		val[:dim]
                              lowerCamelCase								=		val[
                                  dim : dim * 2
                              ]
                              lowerCamelCase								=		val[-dim:]
            else:
                  lowerCamelCase								=		rename_key(UpperCAmelCase__ )
                  if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                        lowerCamelCase								=		val.T
                  lowerCamelCase								=		val

      return orig_state_dict

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      if num_frames == 8:
            lowerCamelCase								=		"eating_spaghetti_8_frames.npy"
      elif num_frames == 16:
            lowerCamelCase								=		"eating_spaghetti.npy"
      elif num_frames == 32:
            lowerCamelCase								=		"eating_spaghetti_32_frames.npy"
      lowerCamelCase								=		hf_hub_download(
          repo_id="hf-internal-testing/spaghetti-video"   ,   filename=UpperCAmelCase__   ,   repo_type="dataset"   ,   )
      lowerCamelCase								=		np.load(UpperCAmelCase__ )
      return list(UpperCAmelCase__ )

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=None   ,   UpperCAmelCase__=False ):


      """simple docstring"""



      lowerCamelCase								=		{
          # fully supervised kinetics-400 checkpoints
          "xclip-base-patch32": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth",
          "xclip-base-patch32-16-frames": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"
          ),
          "xclip-base-patch16": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth",
          "xclip-base-patch16-16-frames": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"
          ),
          "xclip-large-patch14": "https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb",
          "xclip-large-patch14-16-frames": "https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f",
          # fully supervised kinetics-600 checkpoints
          "xclip-base-patch16-kinetics-600": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"
          ),
          "xclip-base-patch16-kinetics-600-16-frames": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"
          ),
          "xclip-large-patch14-kinetics-600": "https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be",
          # few shot
          "xclip-base-patch16-hmdb-2-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"
          ),
          "xclip-base-patch16-hmdb-4-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"
          ),
          "xclip-base-patch16-hmdb-8-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"
          ),
          "xclip-base-patch16-hmdb-16-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"
          ),
          "xclip-base-patch16-ucf-2-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"
          ),
          "xclip-base-patch16-ucf-4-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"
          ),
          "xclip-base-patch16-ucf-8-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"
          ),
          "xclip-base-patch16-ucf-16-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"
          ),
          # zero shot
          "xclip-base-patch16-zero-shot": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth",
      }

      lowerCamelCase								=		model_to_url[model_name]
      lowerCamelCase								=		8
      if "16-frames" in model_name:
            lowerCamelCase								=		16
      elif "shot" in model_name:
            lowerCamelCase								=		32

      lowerCamelCase								=		get_xclip_config(UpperCAmelCase__   ,   UpperCAmelCase__ )
      lowerCamelCase								=		XCLIPModel(UpperCAmelCase__ )
      model.eval()

      if "drive" in checkpoint_url:
            lowerCamelCase								=		"pytorch_model.bin"
            gdown.cached_download(UpperCAmelCase__   ,   UpperCAmelCase__   ,   quiet=UpperCAmelCase__ )
            lowerCamelCase								=		torch.load(UpperCAmelCase__   ,   map_location="cpu" )["model"]
      else:
            lowerCamelCase								=		torch.hub.load_state_dict_from_url(UpperCAmelCase__ )["model"]

      lowerCamelCase								=		convert_state_dict(UpperCAmelCase__   ,   UpperCAmelCase__ )

      lowerCamelCase								=		XCLIPModel(UpperCAmelCase__ )
      lowerCamelCase			,							lowerCamelCase								=		model.load_state_dict(UpperCAmelCase__   ,   strict=UpperCAmelCase__ )
      assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
      model.eval()

      lowerCamelCase								=		336 if model_name == "xclip-large-patch14-16-frames" else 224
      lowerCamelCase								=		VideoMAEImageProcessor(size=UpperCAmelCase__ )
      lowerCamelCase								=		CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32" )
      lowerCamelCase								=		CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32" )
      lowerCamelCase								=		XCLIPProcessor(image_processor=UpperCAmelCase__   ,   tokenizer=UpperCAmelCase__ )

      lowerCamelCase								=		prepare_video(UpperCAmelCase__ )
      lowerCamelCase								=		processor(
          text=["playing sports", "eating spaghetti", "go shopping"]   ,   videos=UpperCAmelCase__   ,   return_tensors="pt"   ,   padding=UpperCAmelCase__ )

      print("Shape of pixel values:"   ,   inputs.pixel_values.shape )

      with torch.no_grad():
            lowerCamelCase								=		model(**UpperCAmelCase__ )

      # Verify outputs
      lowerCamelCase								=		outputs.logits_per_video
      lowerCamelCase								=		logits_per_video.softmax(dim=1 )
      print("Probs:"   ,   UpperCAmelCase__ )
      # kinetics-400
      if model_name == "xclip-base-patch32":
            lowerCamelCase								=		torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]] )
      elif model_name == "xclip-base-patch32-16-frames":
            lowerCamelCase								=		torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]] )
      elif model_name == "xclip-base-patch16":
            lowerCamelCase								=		torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]] )
      elif model_name == "xclip-base-patch16-16-frames":
            lowerCamelCase								=		torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]] )
      elif model_name == "xclip-large-patch14":
            lowerCamelCase								=		torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]] )
      elif model_name == "xclip-large-patch14-16-frames":
            lowerCamelCase								=		torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]] )
      # kinetics-600
      elif model_name == "xclip-base-patch16-kinetics-600":
            lowerCamelCase								=		torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]] )
      elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
            lowerCamelCase								=		torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]] )
      elif model_name == "xclip-large-patch14-kinetics-600":
            lowerCamelCase								=		torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]] )
      # few shot
      elif model_name == "xclip-base-patch16-hmdb-2-shot":
            lowerCamelCase								=		torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]] )
      elif model_name == "xclip-base-patch16-hmdb-4-shot":
            lowerCamelCase								=		torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]] )
      elif model_name == "xclip-base-patch16-hmdb-8-shot":
            lowerCamelCase								=		torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]] )
      elif model_name == "xclip-base-patch16-hmdb-16-shot":
            lowerCamelCase								=		torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]] )
      elif model_name == "xclip-base-patch16-ucf-2-shot":
            lowerCamelCase								=		torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]] )
      elif model_name == "xclip-base-patch16-ucf-4-shot":
            lowerCamelCase								=		torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]] )
      elif model_name == "xclip-base-patch16-ucf-8-shot":
            lowerCamelCase								=		torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]] )
      elif model_name == "xclip-base-patch16-ucf-16-shot":
            lowerCamelCase								=		torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]] )
      # zero shot
      elif model_name == "xclip-base-patch16-zero-shot":
            lowerCamelCase								=		torch.tensor([[3.5082E-04, 9.9785E-01, 1.7966E-03]] )
      else:
            raise ValueError(F"""Model name {model_name} not supported""" )
      assert torch.allclose(UpperCAmelCase__   ,   UpperCAmelCase__   ,   atol=1E-3 )
      print("Looks ok!" )

      if pytorch_dump_folder_path is not None:
            print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
            model.save_pretrained(UpperCAmelCase__ )

      if push_to_hub:
            print("Pushing model, processor and slow tokenizer files to the hub..." )
            model.push_to_hub(UpperCAmelCase__   ,   organization="nielsr" )
            processor.push_to_hub(UpperCAmelCase__   ,   organization="nielsr" )
            slow_tokenizer.push_to_hub(UpperCAmelCase__   ,   organization="nielsr" )


if __name__ == "__main__":
     a_						:     Any	     =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='xclip-base-patch32',
         type=str,
         help='Name of the model.',
     )
     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_						:     List[str]	     =		parser.parse_args()
     convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a_						:     Any	     =		1_6
a_						:     Any	     =		3_2

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ = 16   ,   UpperCAmelCase__ = "bert-base-cased" ):


      """simple docstring"""



      lowerCamelCase								=		AutoTokenizer.from_pretrained(UpperCAmelCase__ )
      lowerCamelCase								=		load_dataset("glue"   ,   "mrpc" )

      def tokenize_function(UpperCAmelCase__ ):
            # max_length=None => use the model max length (it's actually the default)
            lowerCamelCase								=		tokenizer(examples["sentence1"]   ,   examples["sentence2"]   ,   truncation=UpperCAmelCase__   ,   max_length=UpperCAmelCase__ )
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      lowerCamelCase								=		datasets.map(
          UpperCAmelCase__   ,   batched=UpperCAmelCase__   ,   remove_columns=["idx", "sentence1", "sentence2"]   ,   load_from_cache_file=UpperCAmelCase__ )

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      lowerCamelCase								=		tokenized_datasets.rename_column("label"   ,   "labels" )

      def collate_fn(UpperCAmelCase__ ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(UpperCAmelCase__   ,   padding="max_length"   ,   max_length=128   ,   return_tensors="pt" )
            return tokenizer.pad(UpperCAmelCase__   ,   padding="longest"   ,   return_tensors="pt" )

      # Instantiate dataloaders.
      lowerCamelCase								=		DataLoader(
          tokenized_datasets["train"]   ,   shuffle=UpperCAmelCase__   ,   collate_fn=UpperCAmelCase__   ,   batch_size=UpperCAmelCase__ )
      lowerCamelCase								=		DataLoader(
          tokenized_datasets["validation"]   ,   shuffle=UpperCAmelCase__   ,   collate_fn=UpperCAmelCase__   ,   batch_size=UpperCAmelCase__ )

      return train_dataloader, eval_dataloader

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		Accelerator()

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCamelCase								=		config["lr"]
      lowerCamelCase								=		int(config["num_epochs"] )
      lowerCamelCase								=		int(config["seed"] )
      lowerCamelCase								=		int(config["batch_size"] )
      lowerCamelCase								=		args.model_name_or_path

      set_seed(UpperCAmelCase__ )
      lowerCamelCase			,							lowerCamelCase								=		get_dataloaders(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCamelCase								=		AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__   ,   return_dict=UpperCAmelCase__ )

      # Instantiate optimizer
      lowerCamelCase								=		(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      lowerCamelCase								=		optimizer_cls(params=model.parameters()   ,   lr=UpperCAmelCase__ )

      if accelerator.state.deepspeed_plugin is not None:
            lowerCamelCase								=		accelerator.state.deepspeed_plugin.deepspeed_config[
                "gradient_accumulation_steps"
            ]
      else:
            lowerCamelCase								=		1
      lowerCamelCase								=		(len(UpperCAmelCase__ ) * num_epochs) // gradient_accumulation_steps

      # Instantiate scheduler
      if (
          accelerator.state.deepspeed_plugin is None
          or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
      ):
            lowerCamelCase								=		get_linear_schedule_with_warmup(
                optimizer=UpperCAmelCase__   ,   num_warmup_steps=0   ,   num_training_steps=UpperCAmelCase__   ,   )
      else:
            lowerCamelCase								=		DummyScheduler(UpperCAmelCase__   ,   total_num_steps=UpperCAmelCase__   ,   warmup_num_steps=0 )

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		accelerator.prepare(
          UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )

      # We need to keep track of how many total steps we have iterated over
      lowerCamelCase								=		0
      # We also need to keep track of the stating epoch so files are named properly
      lowerCamelCase								=		0

      # Now we train the model
      lowerCamelCase								=		evaluate.load("glue"   ,   "mrpc" )
      lowerCamelCase								=		0
      lowerCamelCase								=		{}
      for epoch in range(UpperCAmelCase__   ,   UpperCAmelCase__ ):
            model.train()
            for step, batch in enumerate(UpperCAmelCase__ ):
                  lowerCamelCase								=		model(**UpperCAmelCase__ )
                  lowerCamelCase								=		outputs.loss
                  lowerCamelCase								=		loss / gradient_accumulation_steps
                  accelerator.backward(UpperCAmelCase__ )
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1

            model.eval()
            lowerCamelCase								=		0
            for step, batch in enumerate(UpperCAmelCase__ ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device )
                  with torch.no_grad():
                        lowerCamelCase								=		model(**UpperCAmelCase__ )
                  lowerCamelCase								=		outputs.logits.argmax(dim=-1 )
                  # It is slightly faster to call this once, than multiple times
                  lowerCamelCase			,							lowerCamelCase								=		accelerator.gather(
                      (predictions, batch["labels"]) )  # If we are in a multiprocess environment, the last batch has duplicates
                  if accelerator.use_distributed:
                        if step == len(UpperCAmelCase__ ) - 1:
                              lowerCamelCase								=		predictions[: len(eval_dataloader.dataset ) - samples_seen]
                              lowerCamelCase								=		references[: len(eval_dataloader.dataset ) - samples_seen]
                        else:
                              samples_seen += references.shape[0]
                  metric.add_batch(
                      predictions=UpperCAmelCase__   ,   references=UpperCAmelCase__   ,   )

            lowerCamelCase								=		metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"""epoch {epoch}:"""   ,   UpperCAmelCase__ )
            lowerCamelCase								=		eval_metric["accuracy"]

            if best_performance < eval_metric["accuracy"]:
                  lowerCamelCase								=		eval_metric["accuracy"]

      if args.performance_lower_bound is not None:
            assert (
                args.performance_lower_bound <= best_performance
            ), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
            with open(os.path.join(args.output_dir   ,   "all_results.json" )   ,   "w" ) as f:
                  json.dump(UpperCAmelCase__   ,   UpperCAmelCase__ )

def     __lowercase(	):


      """simple docstring"""



      lowerCamelCase								=		argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage." )
      parser.add_argument(
          "--model_name_or_path"   ,   type=UpperCAmelCase__   ,   default="bert-base-cased"   ,   help="Path to pretrained model or model identifier from huggingface.co/models."   ,   required=UpperCAmelCase__   ,   )
      parser.add_argument(
          "--output_dir"   ,   type=UpperCAmelCase__   ,   default="."   ,   help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."   ,   )
      parser.add_argument(
          "--performance_lower_bound"   ,   type=UpperCAmelCase__   ,   default=UpperCAmelCase__   ,   help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."   ,   )
      parser.add_argument(
          "--num_epochs"   ,   type=UpperCAmelCase__   ,   default=3   ,   help="Number of train epochs."   ,   )
      lowerCamelCase								=		parser.parse_args()
      lowerCamelCase								=		{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
      training_function(UpperCAmelCase__   ,   UpperCAmelCase__ )


if __name__ == "__main__":
     main()