






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 				snake_case__							(							UpperCAmelCase							:  Dict    ,			UpperCAmelCase							:  List[str]       ):
    lowerCAmelCase__   :Dict      							=				XCLIPTextConfig()

    # derive patch size from model name
    lowerCAmelCase__   :Union[str, Any]      							=				model_name.find("patch"       )
    lowerCAmelCase__   :Dict      							=				int(model_name[start_idx + len("patch"       ) : start_idx + len("patch"       ) + 2]       )
    lowerCAmelCase__   :Dict      							=				XCLIPVisionConfig(patch_size=UpperCAmelCase    ,			num_frames=UpperCAmelCase       )

    if "large" in model_name:
        lowerCAmelCase__   :str      							=				7_6_8
        lowerCAmelCase__   :Optional[Any]      							=				3_0_7_2
        lowerCAmelCase__   :List[Any]      							=				1_2

        lowerCAmelCase__   :str      							=				1_0_2_4
        lowerCAmelCase__   :Union[str, Any]      							=				4_0_9_6
        lowerCAmelCase__   :Dict      							=				1_6
        lowerCAmelCase__   :str      							=				2_4
        lowerCAmelCase__   :Optional[int]      							=				7_6_8
        lowerCAmelCase__   :str      							=				3_0_7_2

    if model_name == "xclip-large-patch14-16-frames":
        lowerCAmelCase__   :str      							=				3_3_6

    lowerCAmelCase__   :Dict      							=				XCLIPConfig.from_text_vision_configs(UpperCAmelCase    ,			UpperCAmelCase       )

    if "large" in model_name:
        lowerCAmelCase__   :Optional[int]      							=				7_6_8

    return config


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

    return name


def 				snake_case__							(							UpperCAmelCase							:  str    ,			UpperCAmelCase							:  Union[str, Any]       ):
    for key in orig_state_dict.copy().keys():
        lowerCAmelCase__   :str      							=				orig_state_dict.pop(UpperCAmelCase       )

        if "attn.in_proj" in key:
            lowerCAmelCase__   :Optional[int]      							=				key.split("."       )
            if key.startswith("visual"       ):
                lowerCAmelCase__   :int      							=				key_split[3]
                lowerCAmelCase__   :Any      							=				config.vision_config.hidden_size
                if "message_attn" in key:
                    if "weight" in key:
                        lowerCAmelCase__   :Optional[Any]      							=				val[
                            :dim, :
                        ]
                        lowerCAmelCase__   :List[Any]      							=				val[
                            dim : dim * 2, :
                        ]
                        lowerCAmelCase__   :Tuple      							=				val[
                            -dim:, :
                        ]
                    else:
                        lowerCAmelCase__   :Union[str, Any]      							=				val[
                            :dim
                        ]
                        lowerCAmelCase__   :Optional[Any]      							=				val[
                            dim : dim * 2
                        ]
                        lowerCAmelCase__   :Optional[int]      							=				val[
                            -dim:
                        ]
                else:
                    if "weight" in key:
                        lowerCAmelCase__   :Tuple      							=				val[
                            :dim, :
                        ]
                        lowerCAmelCase__   :Tuple      							=				val[
                            dim : dim * 2, :
                        ]
                        lowerCAmelCase__   :int      							=				val[
                            -dim:, :
                        ]
                    else:
                        lowerCAmelCase__   :Any      							=				val[:dim]
                        lowerCAmelCase__   :Optional[Any]      							=				val[
                            dim : dim * 2
                        ]
                        lowerCAmelCase__   :List[str]      							=				val[-dim:]
            elif key.startswith("mit"       ):
                lowerCAmelCase__   :List[str]      							=				key_split[2]
                lowerCAmelCase__   :int      							=				config.vision_config.mit_hidden_size
                if "weight" in key:
                    lowerCAmelCase__   :Dict      							=				val[:dim, :]
                    lowerCAmelCase__   :int      							=				val[dim : dim * 2, :]
                    lowerCAmelCase__   :int      							=				val[-dim:, :]
                else:
                    lowerCAmelCase__   :Optional[Any]      							=				val[:dim]
                    lowerCAmelCase__   :List[str]      							=				val[dim : dim * 2]
                    lowerCAmelCase__   :Tuple      							=				val[-dim:]
            else:
                lowerCAmelCase__   :List[Any]      							=				key_split[2]
                lowerCAmelCase__   :Optional[int]      							=				config.text_config.hidden_size
                if "weight" in key:
                    lowerCAmelCase__   :Optional[int]      							=				val[:dim, :]
                    lowerCAmelCase__   :int      							=				val[
                        dim : dim * 2, :
                    ]
                    lowerCAmelCase__   :int      							=				val[-dim:, :]
                else:
                    lowerCAmelCase__   :List[str]      							=				val[:dim]
                    lowerCAmelCase__   :Optional[int]      							=				val[
                        dim : dim * 2
                    ]
                    lowerCAmelCase__   :int      							=				val[-dim:]
        else:
            lowerCAmelCase__   :str      							=				rename_key(UpperCAmelCase       )
            if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                lowerCAmelCase__   :str      							=				val.T
            lowerCAmelCase__   :int      							=				val

    return orig_state_dict


def 				snake_case__							(							UpperCAmelCase							:  str       ):
    if num_frames == 8:
        lowerCAmelCase__   :List[Any]      							=				"eating_spaghetti_8_frames.npy"
    elif num_frames == 1_6:
        lowerCAmelCase__   :Optional[int]      							=				"eating_spaghetti.npy"
    elif num_frames == 3_2:
        lowerCAmelCase__   :Optional[int]      							=				"eating_spaghetti_32_frames.npy"
    lowerCAmelCase__   :Any      							=				hf_hub_download(
        repo_id="hf-internal-testing/spaghetti-video"    ,			filename=UpperCAmelCase    ,			repo_type="dataset"    ,			)
    lowerCAmelCase__   :Union[str, Any]      							=				np.load(UpperCAmelCase       )
    return list(UpperCAmelCase       )


def 				snake_case__							(							UpperCAmelCase							:  Tuple    ,			UpperCAmelCase							:  List[str]=None    ,			UpperCAmelCase							:  List[Any]=False       ):
    lowerCAmelCase__   :List[Any]      							=				{
        # 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__   :Tuple      							=				model_to_url[model_name]
    lowerCAmelCase__   :Optional[int]      							=				8
    if "16-frames" in model_name:
        lowerCAmelCase__   :Tuple      							=				1_6
    elif "shot" in model_name:
        lowerCAmelCase__   :int      							=				3_2

    lowerCAmelCase__   :Dict      							=				get_xclip_config(UpperCAmelCase    ,			UpperCAmelCase       )
    lowerCAmelCase__   :List[str]      							=				XCLIPModel(UpperCAmelCase       )
    model.eval()

    if "drive" in checkpoint_url:
        lowerCAmelCase__   :Any      							=				"pytorch_model.bin"
        gdown.cached_download(UpperCAmelCase    ,			UpperCAmelCase    ,			quiet=UpperCAmelCase       )
        lowerCAmelCase__   :Dict      							=				torch.load(UpperCAmelCase    ,			map_location="cpu"       )["model"]
    else:
        lowerCAmelCase__   :Optional[int]      							=				torch.hub.load_state_dict_from_url(UpperCAmelCase       )["model"]

    lowerCAmelCase__   :List[Any]      							=				convert_state_dict(UpperCAmelCase    ,			UpperCAmelCase       )

    lowerCAmelCase__   :List[Any]      							=				XCLIPModel(UpperCAmelCase       )
    lowerCAmelCase__   :Any      							=				model.load_state_dict(UpperCAmelCase    ,			strict=UpperCAmelCase       )
    assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
    model.eval()

    lowerCAmelCase__   :Optional[int]      							=				3_3_6 if model_name == "xclip-large-patch14-16-frames" else 2_2_4
    lowerCAmelCase__   :List[Any]      							=				VideoMAEImageProcessor(size=UpperCAmelCase       )
    lowerCAmelCase__   :Optional[int]      							=				CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"       )
    lowerCAmelCase__   :List[Any]      							=				CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"       )
    lowerCAmelCase__   :Optional[Any]      							=				XCLIPProcessor(image_processor=UpperCAmelCase    ,			tokenizer=UpperCAmelCase       )

    lowerCAmelCase__   :Dict      							=				prepare_video(UpperCAmelCase       )
    lowerCAmelCase__   :Union[str, Any]      							=				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__   :Any      							=				model(**UpperCAmelCase       )

    # Verify outputs
    lowerCAmelCase__   :Optional[int]      							=				outputs.logits_per_video
    lowerCAmelCase__   :str      							=				logits_per_video.softmax(dim=1       )
    print("Probs:"    ,			UpperCAmelCase       )
    # kinetics-400
    if model_name == "xclip-base-patch32":
        lowerCAmelCase__   :Dict      							=				torch.tensor([[0.0019, 0.9951, 0.0030]]       )
    elif model_name == "xclip-base-patch32-16-frames":
        lowerCAmelCase__   :Union[str, Any]      							=				torch.tensor([[7.0_999E-04, 9.9_883E-01, 4.5_580E-04]]       )
    elif model_name == "xclip-base-patch16":
        lowerCAmelCase__   :Union[str, Any]      							=				torch.tensor([[0.0083, 0.9681, 0.0236]]       )
    elif model_name == "xclip-base-patch16-16-frames":
        lowerCAmelCase__   :str      							=				torch.tensor([[7.6_937E-04, 9.9_728E-01, 1.9_473E-03]]       )
    elif model_name == "xclip-large-patch14":
        lowerCAmelCase__   :Dict      							=				torch.tensor([[0.0062, 0.9864, 0.0075]]       )
    elif model_name == "xclip-large-patch14-16-frames":
        lowerCAmelCase__   :Tuple      							=				torch.tensor([[3.3_877E-04, 9.9_937E-01, 2.8_888E-04]]       )
    # kinetics-600
    elif model_name == "xclip-base-patch16-kinetics-600":
        lowerCAmelCase__   :Dict      							=				torch.tensor([[0.0555, 0.8914, 0.0531]]       )
    elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
        lowerCAmelCase__   :Dict      							=				torch.tensor([[3.8_554E-04, 9.9_929E-01, 3.2_754E-04]]       )
    elif model_name == "xclip-large-patch14-kinetics-600":
        lowerCAmelCase__   :Optional[int]      							=				torch.tensor([[0.0036, 0.9920, 0.0045]]       )
    # few shot
    elif model_name == "xclip-base-patch16-hmdb-2-shot":
        lowerCAmelCase__   :Tuple      							=				torch.tensor([[7.1_890E-06, 9.9_994E-01, 5.6_559E-05]]       )
    elif model_name == "xclip-base-patch16-hmdb-4-shot":
        lowerCAmelCase__   :Tuple      							=				torch.tensor([[1.0_320E-05, 9.9_993E-01, 6.2_435E-05]]       )
    elif model_name == "xclip-base-patch16-hmdb-8-shot":
        lowerCAmelCase__   :Optional[int]      							=				torch.tensor([[4.1_377E-06, 9.9_990E-01, 9.8_386E-05]]       )
    elif model_name == "xclip-base-patch16-hmdb-16-shot":
        lowerCAmelCase__   :Dict      							=				torch.tensor([[4.1_347E-05, 9.9_962E-01, 3.3_411E-04]]       )
    elif model_name == "xclip-base-patch16-ucf-2-shot":
        lowerCAmelCase__   :List[Any]      							=				torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]       )
    elif model_name == "xclip-base-patch16-ucf-4-shot":
        lowerCAmelCase__   :List[Any]      							=				torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]       )
    elif model_name == "xclip-base-patch16-ucf-8-shot":
        lowerCAmelCase__   :List[str]      							=				torch.tensor([[0.0027, 0.9904, 0.0070]]       )
    elif model_name == "xclip-base-patch16-ucf-16-shot":
        lowerCAmelCase__   :int      							=				torch.tensor([[9.8_219E-04, 9.9_593E-01, 3.0_863E-03]]       )
    # zero shot
    elif model_name == "xclip-base-patch16-zero-shot":
        lowerCAmelCase__   :Optional[int]      							=				torch.tensor([[3.5_082E-04, 9.9_785E-01, 1.7_966E-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    :   Optional[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    :   Optional[Any]        =			parser.parse_args()
 convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











import argparse
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.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_a    :   List[Any]        =			16
_a    :   Tuple        =			32


def 				snake_case__							(							UpperCAmelCase							:  Accelerator    ,			UpperCAmelCase							:  int = 1_6       ):
    lowerCAmelCase__   :Tuple      							=				AutoTokenizer.from_pretrained("bert-base-cased"       )
    lowerCAmelCase__   :Any      							=				load_dataset("glue"    ,			"mrpc"       )

    def tokenize_function(UpperCAmelCase							:  Dict       ):
        # max_length=None => use the model max length (it's actually the default)
        lowerCAmelCase__   :Any      							=				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
    # starting with the main process first:
    with accelerator.main_process_first():
        lowerCAmelCase__   :Any      							=				datasets.map(
            UpperCAmelCase    ,			batched=UpperCAmelCase    ,			remove_columns=["idx", "sentence1", "sentence2"]    ,			)

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

    def collate_fn(UpperCAmelCase							:  Any       ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        lowerCAmelCase__   :Optional[int]      							=				1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            lowerCAmelCase__   :int      							=				1_6
        elif accelerator.mixed_precision != "no":
            lowerCAmelCase__   :Optional[int]      							=				8
        else:
            lowerCAmelCase__   :List[Any]      							=				None

        return tokenizer.pad(
            UpperCAmelCase    ,			padding="longest"    ,			max_length=UpperCAmelCase    ,			pad_to_multiple_of=UpperCAmelCase    ,			return_tensors="pt"    ,			)

    # Instantiate dataloaders.
    lowerCAmelCase__   :Dict      							=				DataLoader(
        tokenized_datasets["train"]    ,			shuffle=UpperCAmelCase    ,			collate_fn=UpperCAmelCase    ,			batch_size=UpperCAmelCase       )
    lowerCAmelCase__   :Optional[Any]      							=				DataLoader(
        tokenized_datasets["validation"]    ,			shuffle=UpperCAmelCase    ,			collate_fn=UpperCAmelCase    ,			batch_size=UpperCAmelCase       )

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 _a    :   List[Any]        =			mocked_dataloaders  # noqa: F811


def 				snake_case__							(							UpperCAmelCase							:  Tuple    ,			UpperCAmelCase							:  str       ):
    # For testing only
    if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,			UpperCAmelCase       ) == "1":
        lowerCAmelCase__   :Tuple      							=				2
    # New Code #
    lowerCAmelCase__   :Tuple      							=				int(args.gradient_accumulation_steps       )
    lowerCAmelCase__   :str      							=				int(args.local_sgd_steps       )
    # Initialize accelerator
    lowerCAmelCase__   :Optional[Any]      							=				Accelerator(
        cpu=args.cpu    ,			mixed_precision=args.mixed_precision    ,			gradient_accumulation_steps=UpperCAmelCase       )
    if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
        raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"       )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    lowerCAmelCase__   :Union[str, Any]      							=				config["lr"]
    lowerCAmelCase__   :int      							=				int(config["num_epochs"]       )
    lowerCAmelCase__   :List[Any]      							=				int(config["seed"]       )
    lowerCAmelCase__   :int      							=				int(config["batch_size"]       )

    lowerCAmelCase__   :Tuple      							=				evaluate.load("glue"    ,			"mrpc"       )

    set_seed(UpperCAmelCase       )
    lowerCAmelCase__							,lowerCAmelCase__   :Any      							=				get_dataloaders(UpperCAmelCase    ,			UpperCAmelCase       )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowerCAmelCase__   :Dict      							=				AutoModelForSequenceClassification.from_pretrained("bert-base-cased"    ,			return_dict=UpperCAmelCase       )

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    lowerCAmelCase__   :Any      							=				model.to(accelerator.device       )

    # Instantiate optimizer
    lowerCAmelCase__   :Optional[int]      							=				AdamW(params=model.parameters()    ,			lr=UpperCAmelCase       )

    # Instantiate scheduler
    lowerCAmelCase__   :List[Any]      							=				get_linear_schedule_with_warmup(
        optimizer=UpperCAmelCase    ,			num_warmup_steps=1_0_0    ,			num_training_steps=(len(UpperCAmelCase       ) * num_epochs)    ,			)

    # 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__   :Tuple      							=				accelerator.prepare(
        UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase       )

    # Now we train the model
    for epoch in range(UpperCAmelCase       ):
        model.train()
        with LocalSGD(
            accelerator=UpperCAmelCase    ,			model=UpperCAmelCase    ,			local_sgd_steps=UpperCAmelCase    ,			enabled=local_sgd_steps is not None       ) as local_sgd:
            for step, batch in enumerate(UpperCAmelCase       ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device       )
                # New code #
                # We use the new `accumulate` context manager to perform gradient accumulation
                # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                with accelerator.accumulate(UpperCAmelCase       ):
                    lowerCAmelCase__   :List[str]      							=				model(**UpperCAmelCase       )
                    lowerCAmelCase__   :List[Any]      							=				output.loss
                    accelerator.backward(UpperCAmelCase       )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

        model.eval()
        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__   :Dict      							=				model(**UpperCAmelCase       )
            lowerCAmelCase__   :List[Any]      							=				outputs.logits.argmax(dim=-1       )
            lowerCAmelCase__							,lowerCAmelCase__   :List[Any]      							=				accelerator.gather_for_metrics((predictions, batch["labels"])       )
            metric.add_batch(
                predictions=UpperCAmelCase    ,			references=UpperCAmelCase    ,			)

        lowerCAmelCase__   :Optional[int]      							=				metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}:'''    ,			UpperCAmelCase       )


def 				snake_case__							(							):
    lowerCAmelCase__   :Union[str, Any]      							=				argparse.ArgumentParser(description="Simple example of training script."       )
    parser.add_argument(
        "--mixed_precision"    ,			type=UpperCAmelCase    ,			default=UpperCAmelCase    ,			choices=["no", "fp16", "bf16", "fp8"]    ,			help="Whether to use mixed precision. Choose"
        "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
        "and an Nvidia Ampere GPU."    ,			)
    # New Code #
    parser.add_argument(
        "--gradient_accumulation_steps"    ,			type=UpperCAmelCase    ,			default=1    ,			help="The number of minibatches to be ran before gradients are accumulated."    ,			)
    parser.add_argument(
        "--local_sgd_steps"    ,			type=UpperCAmelCase    ,			default=8    ,			help="Number of local SGD steps or None to disable local SGD"       )
    parser.add_argument("--cpu"    ,			action="store_true"    ,			help="If passed, will train on the CPU."       )
    lowerCAmelCase__   :Tuple      							=				parser.parse_args()
    lowerCAmelCase__   :Any      							=				{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
    training_function(UpperCAmelCase    ,			UpperCAmelCase       )


if __name__ == "__main__":
 main()



