import argparse
import json

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

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)




def        lowerCAmelCase_			(					__A							)					->    Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  VideoMAEConfig()

      set_architecture_configs(__A,  __A							)

      if "finetuned" not in model_name:
            UpperCAmelCase__								=  False

      if "finetuned" in model_name:
            UpperCAmelCase__								=  "huggingface/label-files"
            if "kinetics" in model_name:
                  UpperCAmelCase__								=  400
                  UpperCAmelCase__								=  "kinetics400-id2label.json"
            elif "ssv2" in model_name:
                  UpperCAmelCase__								=  174
                  UpperCAmelCase__								=  "something-something-v2-id2label.json"
            else:
                  raise ValueError("Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."							)
            UpperCAmelCase__								=  json.load(open(hf_hub_download(__A,  __A,  repo_type="dataset"							),  "r"							)							)
            UpperCAmelCase__								=  {int(__A							): v for k, v in idalabel.items()}
            UpperCAmelCase__								=  idalabel
            UpperCAmelCase__								=  {v: k for k, v in idalabel.items()}

      return config




def        lowerCAmelCase_			(					__A,  __A							)					->    Union[str, Any]:







      '''simple docstring'''


      if "small" in model_name:
            UpperCAmelCase__								=  384
            UpperCAmelCase__								=  1_536
            UpperCAmelCase__								=  12
            UpperCAmelCase__								=  16
            UpperCAmelCase__								=  12
            UpperCAmelCase__								=  3
            UpperCAmelCase__								=  192
            UpperCAmelCase__								=  768
      elif "large" in model_name:
            UpperCAmelCase__								=  1_024
            UpperCAmelCase__								=  4_096
            UpperCAmelCase__								=  24
            UpperCAmelCase__								=  16
            UpperCAmelCase__								=  12
            UpperCAmelCase__								=  8
            UpperCAmelCase__								=  512
            UpperCAmelCase__								=  2_048
      elif "huge" in model_name:
            UpperCAmelCase__								=  1_280
            UpperCAmelCase__								=  5_120
            UpperCAmelCase__								=  32
            UpperCAmelCase__								=  16
            UpperCAmelCase__								=  12
            UpperCAmelCase__								=  8
            UpperCAmelCase__								=  640
            UpperCAmelCase__								=  2_560
      elif "base" not in model_name:
            raise ValueError("Model name should include either \"small\", \"base\", \"large\", or \"huge\""							)




def        lowerCAmelCase_			(					__A							)					->    Dict:







      '''simple docstring'''


      if "encoder." in name:
            UpperCAmelCase__								=  name.replace("encoder.",  ""							)
      if "cls_token" in name:
            UpperCAmelCase__								=  name.replace("cls_token",  "videomae.embeddings.cls_token"							)
      if "decoder_pos_embed" in name:
            UpperCAmelCase__								=  name.replace("decoder_pos_embed",  "decoder.decoder_pos_embed"							)
      if "pos_embed" in name and "decoder" not in name:
            UpperCAmelCase__								=  name.replace("pos_embed",  "videomae.embeddings.position_embeddings"							)
      if "patch_embed.proj" in name:
            UpperCAmelCase__								=  name.replace("patch_embed.proj",  "videomae.embeddings.patch_embeddings.projection"							)
      if "patch_embed.norm" in name:
            UpperCAmelCase__								=  name.replace("patch_embed.norm",  "videomae.embeddings.norm"							)
      if "decoder.blocks" in name:
            UpperCAmelCase__								=  name.replace("decoder.blocks",  "decoder.decoder_layers"							)
      if "blocks" in name:
            UpperCAmelCase__								=  name.replace("blocks",  "videomae.encoder.layer"							)
      if "attn.proj" in name:
            UpperCAmelCase__								=  name.replace("attn.proj",  "attention.output.dense"							)
      if "attn" in name and "bias" not in name:
            UpperCAmelCase__								=  name.replace("attn",  "attention.self"							)
      if "attn" in name:
            UpperCAmelCase__								=  name.replace("attn",  "attention.attention"							)
      if "norm1" in name:
            UpperCAmelCase__								=  name.replace("norm1",  "layernorm_before"							)
      if "norm2" in name:
            UpperCAmelCase__								=  name.replace("norm2",  "layernorm_after"							)
      if "mlp.fc1" in name:
            UpperCAmelCase__								=  name.replace("mlp.fc1",  "intermediate.dense"							)
      if "mlp.fc2" in name:
            UpperCAmelCase__								=  name.replace("mlp.fc2",  "output.dense"							)
      if "decoder_embed" in name:
            UpperCAmelCase__								=  name.replace("decoder_embed",  "decoder.decoder_embed"							)
      if "decoder_norm" in name:
            UpperCAmelCase__								=  name.replace("decoder_norm",  "decoder.decoder_norm"							)
      if "decoder_pred" in name:
            UpperCAmelCase__								=  name.replace("decoder_pred",  "decoder.decoder_pred"							)
      if "norm.weight" in name and "decoder" not in name and "fc" not in name:
            UpperCAmelCase__								=  name.replace("norm.weight",  "videomae.layernorm.weight"							)
      if "norm.bias" in name and "decoder" not in name and "fc" not in name:
            UpperCAmelCase__								=  name.replace("norm.bias",  "videomae.layernorm.bias"							)
      if "head" in name and "decoder" not in name:
            UpperCAmelCase__								=  name.replace("head",  "classifier"							)

      return name




def        lowerCAmelCase_			(					__A,  __A							)					->    Any:







      '''simple docstring'''


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

            if key.startswith("encoder."							):
                  UpperCAmelCase__								=  key.replace("encoder.",  ""							)

            if "qkv" in key:
                  UpperCAmelCase__								=  key.split("."							)
                  if key.startswith("decoder.blocks"							):
                        UpperCAmelCase__								=  config.decoder_hidden_size
                        UpperCAmelCase__								=  int(key_split[2]							)
                        UpperCAmelCase__								=  "decoder.decoder_layers."
                        if "weight" in key:
                              UpperCAmelCase__								=  val[:dim, :]
                              UpperCAmelCase__								=  val[dim : dim * 2, :]
                              UpperCAmelCase__								=  val[-dim:, :]
                  else:
                        UpperCAmelCase__								=  config.hidden_size
                        UpperCAmelCase__								=  int(key_split[1]							)
                        UpperCAmelCase__								=  "videomae.encoder.layer."
                        if "weight" in key:
                              UpperCAmelCase__								=  val[:dim, :]
                              UpperCAmelCase__								=  val[dim : dim * 2, :]
                              UpperCAmelCase__								=  val[-dim:, :]
            else:
                  UpperCAmelCase__								=  val

      return orig_state_dict




def        lowerCAmelCase_			(					)					->    Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  hf_hub_download(
          repo_id="hf-internal-testing/spaghetti-video",  filename="eating_spaghetti.npy",  repo_type="dataset"							)
      UpperCAmelCase__								=  np.load(__A							)
      return list(__A							)




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A							)					->    Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  get_videomae_config(__A							)

      if "finetuned" in model_name:
            UpperCAmelCase__								=  VideoMAEForVideoClassification(__A							)
      else:
            UpperCAmelCase__								=  VideoMAEForPreTraining(__A							)

      # download original checkpoint, hosted on Google Drive
      UpperCAmelCase__								=  "pytorch_model.bin"
      gdown.cached_download(__A,  __A,  quiet=__A							)
      UpperCAmelCase__								=  torch.load(__A,  map_location="cpu"							)
      if "model" in files:
            UpperCAmelCase__								=  files["model"]
      else:
            UpperCAmelCase__								=  files["module"]
      UpperCAmelCase__								=  convert_state_dict(__A,  __A							)

      model.load_state_dict(__A							)
      model.eval()

      # verify model on basic input
      UpperCAmelCase__								=  VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5],  image_std=[0.5, 0.5, 0.5]							)
      UpperCAmelCase__								=  prepare_video()
      UpperCAmelCase__								=  image_processor(__A,  return_tensors="pt"							)

      if "finetuned" not in model_name:
            UpperCAmelCase__								=  hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos",  filename="bool_masked_pos.pt"							)
            UpperCAmelCase__								=  torch.load(__A							)

      UpperCAmelCase__								=  model(**__A							)
      UpperCAmelCase__								=  outputs.logits

      UpperCAmelCase__								=  [
          "videomae-small-finetuned-kinetics",
          "videomae-small-finetuned-ssv2",
          # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
          "videomae-base-short",
          "videomae-base-short-finetuned-kinetics",
          "videomae-base",
          "videomae-base-finetuned-kinetics",
          "videomae-large",
          "videomae-large-finetuned-kinetics",
          "videomae-huge-finetuned-kinetics",
          # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
          "videomae-base-short-ssv2",
          "videomae-base-short-finetuned-ssv2",
          "videomae-base-ssv2",
          "videomae-base-finetuned-ssv2",
      ]

      # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
      if model_name == "videomae-small-finetuned-kinetics":
            UpperCAmelCase__								=  torch.Size([1, 400]							)
            UpperCAmelCase__								=  torch.tensor([-0.9291, -0.4061, -0.9307]							)
      elif model_name == "videomae-small-finetuned-ssv2":
            UpperCAmelCase__								=  torch.Size([1, 174]							)
            UpperCAmelCase__								=  torch.tensor([0.2671, -0.4689, -0.8235]							)
      elif model_name == "videomae-base":
            UpperCAmelCase__								=  torch.Size([1, 1_408, 1_536]							)
            UpperCAmelCase__								=  torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]]							)
      elif model_name == "videomae-base-short":
            UpperCAmelCase__								=  torch.Size([1, 1_408, 1_536]							)
            UpperCAmelCase__								=  torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]							)
            # we verified the loss both for normalized and unnormalized targets for this one
            UpperCAmelCase__								=  torch.tensor([0.5142]							) if config.norm_pix_loss else torch.tensor([0.6469]							)
      elif model_name == "videomae-large":
            UpperCAmelCase__								=  torch.Size([1, 1_408, 1_536]							)
            UpperCAmelCase__								=  torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]]							)
      elif model_name == "videomae-large-finetuned-kinetics":
            UpperCAmelCase__								=  torch.Size([1, 400]							)
            UpperCAmelCase__								=  torch.tensor([0.0771, 0.0011, -0.3625]							)
      elif model_name == "videomae-huge-finetuned-kinetics":
            UpperCAmelCase__								=  torch.Size([1, 400]							)
            UpperCAmelCase__								=  torch.tensor([0.2433, 0.1632, -0.4894]							)
      elif model_name == "videomae-base-short-finetuned-kinetics":
            UpperCAmelCase__								=  torch.Size([1, 400]							)
            UpperCAmelCase__								=  torch.tensor([0.6588, 0.0990, -0.2493]							)
      elif model_name == "videomae-base-finetuned-kinetics":
            UpperCAmelCase__								=  torch.Size([1, 400]							)
            UpperCAmelCase__								=  torch.tensor([0.3669, -0.0688, -0.2421]							)
      elif model_name == "videomae-base-short-ssv2":
            UpperCAmelCase__								=  torch.Size([1, 1_408, 1_536]							)
            UpperCAmelCase__								=  torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]]							)
      elif model_name == "videomae-base-short-finetuned-ssv2":
            UpperCAmelCase__								=  torch.Size([1, 174]							)
            UpperCAmelCase__								=  torch.tensor([-0.0537, -0.1539, -0.3266]							)
      elif model_name == "videomae-base-ssv2":
            UpperCAmelCase__								=  torch.Size([1, 1_408, 1_536]							)
            UpperCAmelCase__								=  torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]]							)
      elif model_name == "videomae-base-finetuned-ssv2":
            UpperCAmelCase__								=  torch.Size([1, 174]							)
            UpperCAmelCase__								=  torch.tensor([0.1961, -0.8337, -0.6389]							)
      else:
            raise ValueError(f"""Model name not supported. Should be one of {model_names}"""							)

      # verify logits
      assert logits.shape == expected_shape
      if "finetuned" in model_name:
            assert torch.allclose(logits[0, :3],  __A,  atol=1e-4							)
      else:
            print("Logits:",  logits[0, :3, :3]							)
            assert torch.allclose(logits[0, :3, :3],  __A,  atol=1e-4							)
      print("Logits ok!"							)

      # verify loss, if applicable
      if model_name == "videomae-base-short":
            UpperCAmelCase__								=  outputs.loss
            assert torch.allclose(__A,  __A,  atol=1e-4							)
            print("Loss ok!"							)

      if pytorch_dump_folder_path is not None:
            print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""							)
            image_processor.save_pretrained(__A							)
            model.save_pretrained(__A							)

      if push_to_hub:
            print("Pushing to the hub..."							)
            model.push_to_hub(__A,  organization="nielsr"							)


if __name__ == "__main__":
      UpperCamelCase__    							= argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--checkpoint_url',
          default='https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4',
          type=str,
          help=(
              'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
              ' download link.'
          ),
      )
      parser.add_argument(
          '--pytorch_dump_folder_path',
          default='/Users/nielsrogge/Documents/VideoMAE/Test',
          type=str,
          help='Path to the output PyTorch model directory.',
      )
      parser.add_argument('--model_name', default='videomae-base', type=str, help='Name of the model.')
      parser.add_argument(
          '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
      )

      UpperCamelCase__    							= parser.parse_args()
      convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			List[Any]              =      'facebook/bart-large-mnli'
   __UpperCAmelCase			:			Optional[Any]              =      (
       'This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '
       'should be the text to classify, and `labels`, which should be the list of labels to use for classification. '
       'It returns the most likely label in the list of provided `labels` for the input text.'
   )
   __UpperCAmelCase			:			Optional[int]              =      'text_classifier'
   __UpperCAmelCase			:			int              =      AutoTokenizer
   __UpperCAmelCase			:			Dict              =      AutoModelForSequenceClassification

   __UpperCAmelCase			:			int              =      ['text', ['text']]
   __UpperCAmelCase			:			Optional[int]              =      ['text']


   def       lowercase_				(self					:						List[Any]		)						->    List[str]:
         """simple docstring"""



         super().setup()
         UpperCAmelCase__								=  self.model.config
         UpperCAmelCase__								=  -1
         for idx, label in config.idalabel.items():
               if label.lower().startswith("entail"		):
                     UpperCAmelCase__								=  int(__UpperCAmelCase		)
         if self.entailment_id == -1:
               raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."		)


   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						int		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  labels
         return self.pre_processor(
             [text] * len(__UpperCAmelCase		)	,  [f"""This example is {label}""" for label in labels]	,  return_tensors="pt"	,  padding="max_length"	,  )





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Tuple		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  outputs.logits
         UpperCAmelCase__								=  torch.argmax(logits[:, 2]		).item()
         return self._labels[label_id]



