
import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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


def 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : Tuple=False    ):
       snake_case       :   List[Any]							=      []

       # fmt: off
       # stem:
       rename_keys.append(("cls_token", "vit.embeddings.cls_token")    )
       rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")    )

       rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")    )
       rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")    )

       # backbone
       rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")    )
       rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")    )
       rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")    )

       for stage_idx in range(len(config.backbone_config.depths    )    ):
              for layer_idx in range(config.backbone_config.depths[stage_idx]    ):
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")    )
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")    )
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")    )
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")    )
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")    )
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")    )
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")    )
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")    )
                     rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")    )

              rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")    )
              rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")    )
              rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")    )

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

       if base_model:
              # layernorm + pooler
              rename_keys.extend(
                  [
                      ("norm.weight", "layernorm.weight"),
                      ("norm.bias", "layernorm.bias"),
                      ("pre_logits.fc.weight", "pooler.dense.weight"),
                      ("pre_logits.fc.bias", "pooler.dense.bias"),
                  ]    )

              # if just the base model, we should remove "vit" from all keys that start with "vit"
              snake_case       :   Dict							=      [(pair[0], pair[1][4:]) if pair[1].startswith("vit"    ) else pair for pair in rename_keys]
       else:
              # layernorm + classification head
              rename_keys.extend(
                  [
                      ("norm.weight", "vit.layernorm.weight"),
                      ("norm.bias", "vit.layernorm.bias"),
                      ("head.weight", "classifier.weight"),
                      ("head.bias", "classifier.bias"),
                  ]    )
       # fmt: on

       return rename_keys


def 			UpperCamelCase					(    __lowerCamelCase     : int				,					__lowerCamelCase     : int				,					__lowerCamelCase     : Optional[int]=False    ):
       for i in range(config.num_hidden_layers    ):
              if base_model:
                     snake_case       :   Dict							=      ""
              else:
                     snake_case       :   Optional[int]							=      "vit."
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              snake_case       :   Optional[Any]							=      state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""    )
              snake_case       :   Any							=      state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""    )
              # next, add query, keys and values (in that order) to the state dict
              snake_case       :   Any							=      in_proj_weight[
                  : config.hidden_size, :
              ]
              snake_case       :   Union[str, Any]							=      in_proj_bias[: config.hidden_size]
              snake_case       :   int							=      in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              snake_case       :   Optional[int]							=      in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              snake_case       :   int							=      in_proj_weight[
                  -config.hidden_size :, :
              ]
              snake_case       :   List[Any]							=      in_proj_bias[-config.hidden_size :]


def 			UpperCamelCase					(    __lowerCamelCase     : Optional[Any]    ):
       snake_case       :   Optional[int]							=      ["head.weight", "head.bias"]
       for k in ignore_keys:
              state_dict.pop(__lowerCamelCase				,					__lowerCamelCase    )


def 			UpperCamelCase					(    __lowerCamelCase     : Dict				,					__lowerCamelCase     : List[str]				,					__lowerCamelCase     : Tuple    ):
       snake_case       :   Optional[int]							=      dct.pop(__lowerCamelCase    )
       snake_case       :   List[Any]							=      val


def 			UpperCamelCase					(    ):
       snake_case       :   List[str]							=      "http://images.cocodataset.org/val2017/000000039769.jpg"
       snake_case       :   str							=      Image.open(requests.get(__lowerCamelCase				,					stream=__lowerCamelCase    ).raw    )
       return im


@torch.no_grad()
def 			UpperCamelCase					(    __lowerCamelCase     : int				,					__lowerCamelCase     : str				,					__lowerCamelCase     : Optional[Any]=False    ):
       snake_case       :   int							=      BitConfig(
           global_padding="same"				,					layer_type="bottleneck"				,					depths=(3, 4, 9)				,					out_features=["stage3"]				,					embedding_dynamic_padding=__lowerCamelCase				,					)
       snake_case       :   Optional[Any]							=      ViTHybridConfig(backbone_config=__lowerCamelCase				,					image_size=384				,					num_labels=1000    )
       snake_case       :   Optional[Any]							=      False

       # load original model from timm
       snake_case       :   Any							=      timm.create_model(__lowerCamelCase				,					pretrained=__lowerCamelCase    )
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       snake_case       :   Any							=      timm_model.state_dict()
       if base_model:
              remove_classification_head_(__lowerCamelCase    )
       snake_case       :   str							=      create_rename_keys(__lowerCamelCase				,					__lowerCamelCase    )
       for src, dest in rename_keys:
              rename_key(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )
       read_in_q_k_v(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )

       snake_case       :   Dict							=      "huggingface/label-files"
       snake_case       :   List[str]							=      "imagenet-1k-id2label.json"
       snake_case       :   str							=      json.load(open(hf_hub_download(__lowerCamelCase				,					__lowerCamelCase				,					repo_type="dataset"    )				,					"r"    )    )
       snake_case       :   int							=      {int(__lowerCamelCase    ): v for k, v in idalabel.items()}
       snake_case       :   Union[str, Any]							=      idalabel
       snake_case       :   int							=      {v: k for k, v in idalabel.items()}

       # load HuggingFace model
       if vit_name[-5:] == "in21k":
              snake_case       :   Dict							=      ViTHybridModel(__lowerCamelCase    ).eval()
       else:
              snake_case       :   int							=      ViTHybridForImageClassification(__lowerCamelCase    ).eval()
       model.load_state_dict(__lowerCamelCase    )

       # create image processor
       snake_case       :   Union[str, Any]							=      create_transform(**resolve_data_config({}				,					model=__lowerCamelCase    )    )
       snake_case       :   int							=      transform.transforms

       snake_case       :   int							=      {
           "bilinear": PILImageResampling.BILINEAR,
           "bicubic": PILImageResampling.BICUBIC,
           "nearest": PILImageResampling.NEAREST,
       }

       snake_case       :   Optional[Any]							=      ViTHybridImageProcessor(
           do_resize=__lowerCamelCase				,					size={"shortest_edge": timm_transforms[0].size}				,					resample=pillow_resamplings[timm_transforms[0].interpolation.value]				,					do_center_crop=__lowerCamelCase				,					crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}				,					do_normalize=__lowerCamelCase				,					image_mean=timm_transforms[-1].mean.tolist()				,					image_std=timm_transforms[-1].std.tolist()				,					)

       snake_case       :   int							=      prepare_img()
       snake_case       :   List[str]							=      transform(__lowerCamelCase    ).unsqueeze(0    )
       snake_case       :   Union[str, Any]							=      processor(__lowerCamelCase				,					return_tensors="pt"    ).pixel_values

       # verify pixel values
       assert torch.allclose(__lowerCamelCase				,					__lowerCamelCase    )

       # verify logits
       with torch.no_grad():
              snake_case       :   Union[str, Any]							=      model(__lowerCamelCase    )
              snake_case       :   Optional[Any]							=      outputs.logits

       print("Predicted class:"				,					logits.argmax(-1    ).item()    )
       if base_model:
              snake_case       :   List[str]							=      timm_model.forward_features(__lowerCamelCase    )
              assert timm_pooled_output.shape == outputs.pooler_output.shape
              assert torch.allclose(__lowerCamelCase				,					outputs.pooler_output				,					atol=1E-3    )
       else:
              snake_case       :   str							=      timm_model(__lowerCamelCase    )
              assert timm_logits.shape == outputs.logits.shape
              assert torch.allclose(__lowerCamelCase				,					outputs.logits				,					atol=1E-3    )
       print("Looks ok!"    )

       if pytorch_dump_folder_path is not None:
              Path(__lowerCamelCase    ).mkdir(exist_ok=__lowerCamelCase    )
              print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""    )
              model.save_pretrained(__lowerCamelCase    )
              print(f"""Saving processor to {pytorch_dump_folder_path}"""    )
              processor.save_pretrained(__lowerCamelCase    )

       if push_to_hub:
              print(f"""Pushing model and processor to the hub {vit_name}"""    )
              model.push_to_hub(f"""ybelkada/{vit_name}"""    )
              processor.push_to_hub(f"""ybelkada/{vit_name}"""    )


if __name__ == "__main__":
   __lowerCamelCase							=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--vit_name""",
       default="""vit_base_r50_s16_384""",
       type=str,
       help="""Name of the hybrid ViT timm model you'd like to convert.""",
   )
   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 to upload the model to the HuggingFace hub."""
   )

   __lowerCamelCase							=       parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)










from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


SCREAMING_SNAKE_CASE_:Any             =						[
    """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"""
    """ final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"""
    """ depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.""",
    """The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"""
    """ accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"""
    """ founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"""
    """ body.""",
    """Amnesty International releases its annual report on the death penalty. The report catalogs the use of"""
    """ state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"""
    """ world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"""
    """ punishment.""",
]

SCREAMING_SNAKE_CASE_:Optional[int]             =						[
    """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."""
    """ Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"""
    """ had informed his Lufthansa training school of an episode of severe depression, airline says .""",
    """Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."""
    """ Israel and the United States opposed the move, which could open the door to war crimes investigations against"""
    """ Israelis .""",
    """Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"""
    """ death . Organization claims that governments around the world are using the threat of terrorism to advance"""
    """ executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"""
    """ sentences up by 28% .""",
]




def 	__UpperCamelCase	(  )       ->  Optional[Any]:



    """simple docstring"""






    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     bootstrap_aggregation=_lowerCAmelCase      ,     rouge_keys=["""rouge2""", """rougeL"""]		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)
    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     bootstrap_aggregation=_lowerCAmelCase      ,     rouge_keys=["""rouge2"""]		)
    assert (
        pd.DataFrame(no_aggregation["""rouge2"""]		).fmeasure.mean()
        == pd.DataFrame(no_aggregation_just_ra["""rouge2"""]		).fmeasure.mean()
    )




def 	__UpperCamelCase	(  )       ->  List[Any]:



    """simple docstring"""






    A :       Dict          =   """rougeLsum"""
    A :       str          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=[k]		)[k]
    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=[k]		)[k]
    assert score > score_no_sep




def 	__UpperCamelCase	(  )       ->  Tuple:



    """simple docstring"""






    A :       Union[str, Any]          =   ["""rouge1""", """rouge2""", """rougeL"""]
    A :       Dict          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=_lowerCAmelCase		)
    A :       List[str]          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=_lowerCAmelCase		)
    assert score_sep == score_no_sep




def 	__UpperCamelCase	(  )       ->  Optional[Any]:



    """simple docstring"""






    A :       Optional[Any]          =   [
        """Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.""",
        """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .""",
    ]
    A :       Optional[Any]          =   [
        """Margot Frank, died in 1945, a month earlier than previously thought.""",
        """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"""
        """ the final seconds on board Flight 9525.""",
    ]
    assert calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase		) == calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase		)




def 	__UpperCamelCase	(  )       ->  List[Any]:



    """simple docstring"""






    A :       Tuple          =   [
        """\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" """
    ]
    A :       Union[str, Any]          =   [
        """ Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."""
    ]

    A :       int          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     rouge_keys=["""rougeLsum"""]      ,     newline_sep=_lowerCAmelCase		)["""rougeLsum"""]
    A :       Optional[Any]          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     rouge_keys=["""rougeLsum"""]		)["""rougeLsum"""]
    assert new_score > prev_score




def 	__UpperCamelCase	(  )       ->  Any:



    """simple docstring"""






    A :       Tuple          =   Path("""examples/seq2seq/test_data/wmt_en_ro"""		)
    A :       Optional[Any]          =   calculate_rouge_path(data_dir.joinpath("""test.source"""		)      ,     data_dir.joinpath("""test.target"""		)		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)
    A :       List[Any]          =   calculate_rouge_path(
        data_dir.joinpath("""test.source"""		)      ,     data_dir.joinpath("""test.target"""		)      ,     bootstrap_aggregation=_lowerCAmelCase		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)


