"""simple docstring"""


import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__A       			=      logging.getLogger(__name__)

def 						UpperCamelCase				(	):
    __a			      =						argparse.ArgumentParser(description="""PyTorch Transformer Language Model"""						)
    parser.add_argument("""--model_name"""				, type=_lowerCAmelCase				, default="""transfo-xl-wt103"""				, help="""pretrained model name"""						)
    parser.add_argument(
        """--split"""				, type=_lowerCAmelCase				, default="""test"""				, choices=["""all""", """valid""", """test"""]				, help="""which split to evaluate"""						)
    parser.add_argument("""--batch_size"""				, type=_lowerCAmelCase				, default=10				, help="""batch size"""						)
    parser.add_argument("""--tgt_len"""				, type=_lowerCAmelCase				, default=128				, help="""number of tokens to predict"""						)
    parser.add_argument("""--ext_len"""				, type=_lowerCAmelCase				, default=0				, help="""length of the extended context"""						)
    parser.add_argument("""--mem_len"""				, type=_lowerCAmelCase				, default=1600				, help="""length of the retained previous heads"""						)
    parser.add_argument("""--clamp_len"""				, type=_lowerCAmelCase				, default=1000				, help="""max positional embedding index"""						)
    parser.add_argument("""--no_cuda"""				, action="""store_true"""				, help="""Do not use CUDA even though CUA is available"""						)
    parser.add_argument("""--work_dir"""				, type=_lowerCAmelCase				, required=_lowerCAmelCase				, help="""path to the work_dir"""						)
    parser.add_argument("""--no_log"""				, action="""store_true"""				, help="""do not log the eval result"""						)
    parser.add_argument("""--same_length"""				, action="""store_true"""				, help="""set same length attention with masking"""						)
    parser.add_argument("""--server_ip"""				, type=_lowerCAmelCase				, default=""""""				, help="""Can be used for distant debugging."""						)
    parser.add_argument("""--server_port"""				, type=_lowerCAmelCase				, default=""""""				, help="""Can be used for distant debugging."""						)
    __a			      =						parser.parse_args()
    assert args.ext_len >= 0, "extended context length must be non-negative"

    if args.server_ip and args.server_port:
        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
        import ptvsd

        print("""Waiting for debugger attach"""						)
        ptvsd.enable_attach(address=(args.server_ip, args.server_port)				, redirect_output=_lowerCAmelCase						)
        ptvsd.wait_for_attach()

    __a			      =						torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""						)
    logger.info("""device: {}""".format(_lowerCAmelCase						)						)

    # Load a pre-processed dataset
    # You can also build the corpus yourself using TransfoXLCorpus methods
    # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
    # and tokenizing the dataset
    # The pre-processed corpus is a convertion (using the conversion script )
    __a			      =						TransfoXLCorpus.from_pretrained(args.model_name						)

    __a			      =						corpus.get_iterator("""valid"""				, args.batch_size				, args.tgt_len				, device=_lowerCAmelCase				, ext_len=args.ext_len						)
    __a			      =						corpus.get_iterator("""test"""				, args.batch_size				, args.tgt_len				, device=_lowerCAmelCase				, ext_len=args.ext_len						)

    # Load a pre-trained model
    __a			      =						TransfoXLLMHeadModel.from_pretrained(args.model_name						)
    model.to(_lowerCAmelCase						)

    logger.info(
        """Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}""".format(
            args.batch_size				, args.tgt_len				, args.ext_len				, args.mem_len				, args.clamp_len						)						)

    model.reset_memory_length(args.mem_len						)
    if args.clamp_len > 0:
        __a			      =						args.clamp_len
    if args.same_length:
        __a			      =						True

    ###############################################################################
    # Evaluation code
    ###############################################################################
    def evaluate(_lowerCAmelCase   :							List[str]						):
        # Turn on evaluation mode which disables dropout.
        model.eval()
        __a      ,  __a			      =						0, 0.0
        __a			      =						time.time()
        with torch.no_grad():
            __a			      =						None
            for idx, (data, target, seq_len) in enumerate(_lowerCAmelCase						):
                __a			      =						model(_lowerCAmelCase				, lm_labels=_lowerCAmelCase				, mems=_lowerCAmelCase						)
                __a      ,  __a      ,  __a			      =						ret
                __a			      =						loss.mean()
                total_loss += seq_len * loss.item()
                total_len += seq_len
            __a			      =						time.time() - start_time
        logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(_lowerCAmelCase				, 1000 * total_time / (idx + 1)						)						)
        return total_loss / total_len

    # Run on test data.
    if args.split == "all":
        __a			      =						evaluate(_lowerCAmelCase						)
        __a			      =						evaluate(_lowerCAmelCase						)
    elif args.split == "valid":
        __a			      =						evaluate(_lowerCAmelCase						)
        __a			      =						None
    elif args.split == "test":
        __a			      =						evaluate(_lowerCAmelCase						)
        __a			      =						None

    def format_log(_lowerCAmelCase   :							int				, _lowerCAmelCase   :							Optional[Any]						):
        __a			      =						"""| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(_lowerCAmelCase				, _lowerCAmelCase				, math.exp(_lowerCAmelCase						)						)
        return log_str

    __a			      =						""""""
    if valid_loss is not None:
        log_str += format_log(_lowerCAmelCase				, """valid"""						)
    if test_loss is not None:
        log_str += format_log(_lowerCAmelCase				, """test"""						)

    logger.info("""=""" * 100						)
    logger.info(_lowerCAmelCase						)
    logger.info("""=""" * 100						)


if __name__ == "__main__":
 main()

"""simple docstring"""


import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)

def 						UpperCamelCase				(	_lowerCAmelCase   :							List[str]				, _lowerCAmelCase   :							Optional[int]=False				, _lowerCAmelCase   :							Union[str, Any]=False				, _lowerCAmelCase   :							Any="data2vec."						):
    __a			      =						"""backbone.""" if is_semantic else """"""

    __a			      =						[]
    for i in range(config.num_hidden_layers						):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append(
            (f"""{prefix}blocks.{i}.norm1.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.norm2.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")						)

    # projection layer + position embeddings
    rename_keys.extend(
        [
            (f"""{prefix}cls_token""", f"""{hf_prefix}embeddings.cls_token"""),
            (f"""{prefix}patch_embed.proj.weight""", f"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
            (f"""{prefix}patch_embed.proj.bias""", f"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
        ]						)

    if has_lm_head:
        # mask token + shared relative position bias + layernorm
        rename_keys.extend(
            [
                ("""mask_token""", f"""{hf_prefix}embeddings.mask_token"""),
                (
                    """rel_pos_bias.relative_position_bias_table""",
                    f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
                ),
                (
                    """rel_pos_bias.relative_position_index""",
                    f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
                ),
                ("""norm.weight""", """layernorm.weight"""),
                ("""norm.bias""", """layernorm.bias"""),
            ]						)
    elif is_semantic:
        # semantic segmentation classification heads
        rename_keys.extend(
            [
                ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
                ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
                ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
                ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
            ]						)
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ("""fc_norm.weight""", f"""{hf_prefix}pooler.layernorm.weight"""),
                ("""fc_norm.bias""", f"""{hf_prefix}pooler.layernorm.bias"""),
                ("""head.weight""", """classifier.weight"""),
                ("""head.bias""", """classifier.bias"""),
            ]						)

    return rename_keys

def 						UpperCamelCase				(	_lowerCAmelCase   :							str				, _lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							List[Any]=False				, _lowerCAmelCase   :							Optional[int]=False				, _lowerCAmelCase   :							Tuple="data2vec_vision."						):
    for i in range(config.num_hidden_layers						):
        __a			      =						"""backbone.""" if is_semantic else """"""
        # queries, keys and values
        __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""						)
        __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""						)
        __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""						)

        __a			      =						in_proj_weight[
            : config.hidden_size, :
        ]
        __a			      =						q_bias
        __a			      =						in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        __a			      =						in_proj_weight[
            -config.hidden_size :, :
        ]
        __a			      =						v_bias

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""						)
        __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""						)

        __a			      =						gamma_a
        __a			      =						gamma_a

        # relative_position bias table + index
        if not has_lm_head:
            # each layer has its own relative position bias
            __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""						)
            __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""						)

            __a			      =						table
            __a			      =						index

def 						UpperCamelCase				(	):
    __a			      =						argparse.ArgumentParser(
        """Convert Data2VecVision to HF for image classification and pretraining"""				, add_help=_lowerCAmelCase						)
    parser.add_argument("""--hf_checkpoint_name"""				, type=_lowerCAmelCase						)
    parser.add_argument("""--input_size"""				, default=224				, type=_lowerCAmelCase				, help="""images input size"""						)
    parser.add_argument("""--beit_checkpoint"""				, default=""""""				, help="""beit checkpoint"""						)

    return parser.parse_args()

def 						UpperCamelCase				(	_lowerCAmelCase   :							Optional[int]				, _lowerCAmelCase   :							Union[str, Any]				, _lowerCAmelCase   :							Union[str, Any]						):
    def load_state_dict(_lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							str				, _lowerCAmelCase   :							Union[str, Any]=""				, _lowerCAmelCase   :							Union[str, Any]="relative_position_index"						):
        __a			      =						[]
        __a			      =						[]
        __a			      =						[]
        # copy state_dict so _load_from_state_dict can modify it
        __a			      =						getattr(_lowerCAmelCase				, """_metadata"""				, _lowerCAmelCase						)
        __a			      =						state_dict.copy()
        if metadata is not None:
            __a			      =						metadata

        def load(_lowerCAmelCase   :							List[Any]				, _lowerCAmelCase   :							List[str]=""						):
            __a			      =						{} if metadata is None else metadata.get(prefix[:-1]				, {}						)
            module._load_from_state_dict(
                _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase						)
            for name, child in module._modules.items():
                if child is not None:
                    load(_lowerCAmelCase				, prefix + name + """."""						)

        load(_lowerCAmelCase				, prefix=_lowerCAmelCase						)

        __a			      =						[]
        __a			      =						[]
        for key in missing_keys:
            __a			      =						True
            for ignore_key in ignore_missing.split("""|"""						):
                if ignore_key in key:
                    __a			      =						False
                    break
            if keep_flag:
                warn_missing_keys.append(_lowerCAmelCase						)
            else:
                ignore_missing_keys.append(_lowerCAmelCase						)

        __a			      =						warn_missing_keys

        if len(_lowerCAmelCase						) > 0:
            print(
                """Weights of {} not initialized from pretrained model: {}""".format(
                    model.__class__.__name__				, _lowerCAmelCase						)						)
        if len(_lowerCAmelCase						) > 0:
            print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__				, _lowerCAmelCase						)						)
        if len(_lowerCAmelCase						) > 0:
            print(
                """Ignored weights of {} not initialized from pretrained model: {}""".format(
                    model.__class__.__name__				, _lowerCAmelCase						)						)
        if len(_lowerCAmelCase						) > 0:
            print("""\n""".join(_lowerCAmelCase						)						)

    __a			      =						{
        """pretrained""": False,
        """use_shared_rel_pos_bias""": True,
        """use_abs_pos_emb""": False,
        """init_values""": 0.1,
    }

    if is_finetuned:
        model_kwargs.update(
            {
                """num_classes""": 1000,
                """use_mean_pooling""": True,
                """init_scale""": 0.001,
                """use_rel_pos_bias""": True,
            }						)

    __a			      =						create_model(
        """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""				, **_lowerCAmelCase				, )
    __a			      =						model.patch_embed.patch_size
    __a			      =						(args.input_size // patch_size[0], args.input_size // patch_size[1])
    __a			      =						torch.load(args.beit_checkpoint				, map_location="""cpu"""						)

    print(f"""Load ckpt from {args.beit_checkpoint}"""						)
    __a			      =						None
    for model_key in ("model", "module"):
        if model_key in checkpoint:
            __a			      =						checkpoint[model_key]
            print(f"""Load state_dict by model_key = {model_key}"""						)
            break

    __a			      =						list(checkpoint_model.keys()						)
    for key in all_keys:
        if "relative_position_index" in key:
            checkpoint_model.pop(_lowerCAmelCase						)

        if "relative_position_bias_table" in key:
            __a			      =						checkpoint_model[key]
            __a      ,  __a			      =						rel_pos_bias.size()
            __a      ,  __a			      =						model.state_dict()[key].size()
            __a			      =						model.patch_embed.patch_shape
            if dst_patch_shape[0] != dst_patch_shape[1]:
                raise NotImplementedError()

    load_state_dict(_lowerCAmelCase				, _lowerCAmelCase				, prefix=""""""						)

    return model

def 						UpperCamelCase				(	):
    __a			      =						get_args()

    __a			      =						"""ft1k""" in args.hf_checkpoint_name
    __a			      =						"""large""" in args.hf_checkpoint_name

    if is_finetuned:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
        # into this folder.
        import modeling_finetune  # noqa: F401
    else:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
        # into this folder
        # IMPORTANT: Note that for now we've only converted the down-stream
        # model and not the full pretrained model. This means for the integration
        # test you need to add a `return x` after the following line:
        # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
        # to make the integration test pass.
        import modeling_cyclical  # noqa: F401

    # 1. Create model config
    __a			      =						DataaVecVisionConfig()
    if is_finetuned:
        __a			      =						True
        __a			      =						False
        __a			      =						True
        __a			      =						1000

        __a			      =						"""huggingface/label-files"""
        __a			      =						"""imagenet-1k-id2label.json"""
        __a			      =						json.load(open(hf_hub_download(_lowerCAmelCase				, _lowerCAmelCase				, repo_type="""dataset"""						)				, """r"""						)						)
        __a			      =						{int(_lowerCAmelCase						): v for k, v in idalabel.items()}
        __a			      =						idalabel
        __a			      =						{v: k for k, v in idalabel.items()}
    else:
        __a			      =						False
        __a			      =						True
        __a			      =						False

    if is_large:
        __a			      =						1024
        __a			      =						4096
        __a			      =						24
        __a			      =						16

    # 2. Load Beit model
    __a			      =						load_beit_model(_lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase						)
    orig_model.eval()

    # 3. Forward Beit model
    __a			      =						BeitImageProcessor(size=config.image_size				, do_center_crop=_lowerCAmelCase						)
    __a			      =						Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""						)
    __a			      =						image_processor(images=_lowerCAmelCase				, return_tensors="""pt"""						)
    __a			      =						encoding["""pixel_values"""]

    __a			      =						(pixel_values,) if is_finetuned else (pixel_values, None)
    with torch.no_grad():
        __a			      =						orig_model(*_lowerCAmelCase						)

    # 4. Load HF Data2VecVision model
    if is_finetuned:
        __a			      =						DataaVecVisionForImageClassification(_lowerCAmelCase						)
        hf_model.eval()
        __a			      =						False
        __a			      =						"""data2vec_vision."""
    else:
        __a			      =						DataaVecVisionModel(_lowerCAmelCase						)
        hf_model.eval()
        __a			      =						True
        __a			      =						""""""

    __a			      =						create_rename_keys(_lowerCAmelCase				, hf_prefix=_lowerCAmelCase				, has_lm_head=_lowerCAmelCase						)
    __a			      =						orig_model.state_dict()
    for src, dest in rename_keys:
        __a			      =						state_dict.pop(_lowerCAmelCase						)
        __a			      =						val

    read_in_q_k_v(_lowerCAmelCase				, _lowerCAmelCase				, hf_prefix=_lowerCAmelCase				, has_lm_head=_lowerCAmelCase						)
    __a      ,  __a			      =						hf_model.load_state_dict(_lowerCAmelCase				, strict=_lowerCAmelCase						)
    print("""HF missing"""				, _lowerCAmelCase						)
    print("""HF unexpected_keys"""				, _lowerCAmelCase						)

    # 5. Forward HF Data2VecVision model
    with torch.no_grad():
        __a			      =						hf_model(_lowerCAmelCase						)

    __a			      =						hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

    # 6. Compare
    __a			      =						torch.max(torch.abs(hf_output - orig_model_output						)						).item()

    print(f"""max_absolute_diff = {max_absolute_diff}"""						)
    __a			      =						torch.allclose(_lowerCAmelCase				, _lowerCAmelCase				, atol=1E-3						)
    print("""Do both models output the same tensors?"""				, """🔥""" if success else """💩"""						)
    if not success:
        raise Exception("""Something went wRoNg"""						)

    # 7. Save
    print(f"""Saving to {args.hf_checkpoint_name}"""						)
    hf_model.save_pretrained(args.hf_checkpoint_name						)
    image_processor.save_pretrained(args.hf_checkpoint_name						)


if __name__ == "__main__":
 main()
 # Run the following to convert checkpoints
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./pretrained_base.pt \
 #          --hf_checkpoint_name "./data2vec-vision-base"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./finetuned_base.pt \
 #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./pretrained_large.pt \
 #          --hf_checkpoint_name "./data2vec-vision-large"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./finetuned_large.pt \
 #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"
