
'''simple docstring'''

import argparse
import json

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

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  Union[str, Any]:
  _lowerCAmelCase    :					Optional[Any]							=   TimesformerConfig()

  if "large" in model_name:
    _lowerCAmelCase    :					Any							=   96

  if "hr" in model_name:
    _lowerCAmelCase    :					Union[str, Any]							=   16
    _lowerCAmelCase    :					Any							=   4_48

  _lowerCAmelCase    :					Optional[int]							=   """huggingface/label-files"""
  if "k400" in model_name:
    _lowerCAmelCase    :					Any							=   4_00
    _lowerCAmelCase    :					Any							=   """kinetics400-id2label.json"""
  elif "k600" in model_name:
    _lowerCAmelCase    :					Any							=   6_00
    _lowerCAmelCase    :					str							=   """kinetics600-id2label.json"""
  elif "ssv2" in model_name:
    _lowerCAmelCase    :					Dict							=   1_74
    _lowerCAmelCase    :					Union[str, Any]							=   """something-something-v2-id2label.json"""
  else:
    raise ValueError("""Model name should either contain 'k400', 'k600' or 'ssv2'."""						)
  _lowerCAmelCase    :					str							=   json.load(open(hf_hub_download(_lowerCamelCase					, _lowerCamelCase					, repo_type="""dataset"""						)					, """r"""						)						)
  _lowerCAmelCase    :					List[Any]							=   {int(_lowerCamelCase						): v for k, v in idalabel.items()}
  _lowerCAmelCase    :					Any							=   idalabel
  _lowerCAmelCase    :					Dict							=   {v: k for k, v in idalabel.items()}

  return config


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]						)      ->  Dict:
  if "encoder." in name:
    _lowerCAmelCase    :					Tuple							=   name.replace("""encoder."""					, """"""						)
  if "cls_token" in name:
    _lowerCAmelCase    :					Optional[int]							=   name.replace("""cls_token"""					, """timesformer.embeddings.cls_token"""						)
  if "pos_embed" in name:
    _lowerCAmelCase    :					Union[str, Any]							=   name.replace("""pos_embed"""					, """timesformer.embeddings.position_embeddings"""						)
  if "time_embed" in name:
    _lowerCAmelCase    :					int							=   name.replace("""time_embed"""					, """timesformer.embeddings.time_embeddings"""						)
  if "patch_embed.proj" in name:
    _lowerCAmelCase    :					Union[str, Any]							=   name.replace("""patch_embed.proj"""					, """timesformer.embeddings.patch_embeddings.projection"""						)
  if "patch_embed.norm" in name:
    _lowerCAmelCase    :					List[Any]							=   name.replace("""patch_embed.norm"""					, """timesformer.embeddings.norm"""						)
  if "blocks" in name:
    _lowerCAmelCase    :					List[Any]							=   name.replace("""blocks"""					, """timesformer.encoder.layer"""						)
  if "attn.proj" in name:
    _lowerCAmelCase    :					List[str]							=   name.replace("""attn.proj"""					, """attention.output.dense"""						)
  if "attn" in name and "bias" not in name and "temporal" not in name:
    _lowerCAmelCase    :					List[str]							=   name.replace("""attn"""					, """attention.self"""						)
  if "attn" in name and "temporal" not in name:
    _lowerCAmelCase    :					Optional[int]							=   name.replace("""attn"""					, """attention.attention"""						)
  if "temporal_norm1" in name:
    _lowerCAmelCase    :					str							=   name.replace("""temporal_norm1"""					, """temporal_layernorm"""						)
  if "temporal_attn.proj" in name:
    _lowerCAmelCase    :					List[Any]							=   name.replace("""temporal_attn"""					, """temporal_attention.output.dense"""						)
  if "temporal_fc" in name:
    _lowerCAmelCase    :					Optional[Any]							=   name.replace("""temporal_fc"""					, """temporal_dense"""						)
  if "norm1" in name and "temporal" not in name:
    _lowerCAmelCase    :					Optional[int]							=   name.replace("""norm1"""					, """layernorm_before"""						)
  if "norm2" in name:
    _lowerCAmelCase    :					List[Any]							=   name.replace("""norm2"""					, """layernorm_after"""						)
  if "mlp.fc1" in name:
    _lowerCAmelCase    :					Optional[int]							=   name.replace("""mlp.fc1"""					, """intermediate.dense"""						)
  if "mlp.fc2" in name:
    _lowerCAmelCase    :					int							=   name.replace("""mlp.fc2"""					, """output.dense"""						)
  if "norm.weight" in name and "fc" not in name and "temporal" not in name:
    _lowerCAmelCase    :					List[str]							=   name.replace("""norm.weight"""					, """timesformer.layernorm.weight"""						)
  if "norm.bias" in name and "fc" not in name and "temporal" not in name:
    _lowerCAmelCase    :					str							=   name.replace("""norm.bias"""					, """timesformer.layernorm.bias"""						)
  if "head" in name:
    _lowerCAmelCase    :					List[str]							=   name.replace("""head"""					, """classifier"""						)

  return name


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    Any						)      ->  List[Any]:
  for key in orig_state_dict.copy().keys():
    _lowerCAmelCase    :					List[str]							=   orig_state_dict.pop(_lowerCamelCase						)

    if key.startswith("""model."""						):
      _lowerCAmelCase    :					Union[str, Any]							=   key.replace("""model."""					, """"""						)

    if "qkv" in key:
      _lowerCAmelCase    :					Union[str, Any]							=   key.split("""."""						)
      _lowerCAmelCase    :					Union[str, Any]							=   int(key_split[1]						)
      _lowerCAmelCase    :					List[str]							=   """timesformer.encoder.layer."""
      if "temporal" in key:
        _lowerCAmelCase    :					Any							=   """.temporal_attention.attention.qkv."""
      else:
        _lowerCAmelCase    :					Dict							=   """.attention.attention.qkv."""
      if "weight" in key:
        _lowerCAmelCase    :					Any							=   val
      else:
        _lowerCAmelCase    :					Dict							=   val
    else:
      _lowerCAmelCase    :					List[str]							=   val

  return orig_state_dict


def 		_UpperCAmelCase			(					)      ->  Optional[Any]:
  _lowerCAmelCase    :					int							=   hf_hub_download(
      repo_id="""hf-internal-testing/spaghetti-video"""					, filename="""eating_spaghetti.npy"""					, repo_type="""dataset"""						)
  _lowerCAmelCase    :					int							=   np.load(_lowerCamelCase						)
  return list(_lowerCamelCase						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Tuple					, _lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    Dict					, _lowerCamelCase  :    Dict						)      ->  str:
  _lowerCAmelCase    :					List[Any]							=   get_timesformer_config(_lowerCamelCase						)

  _lowerCAmelCase    :					List[Any]							=   TimesformerForVideoClassification(_lowerCamelCase						)

  # download original checkpoint, hosted on Google Drive
  _lowerCAmelCase    :					Optional[Any]							=   """pytorch_model.bin"""
  gdown.cached_download(_lowerCamelCase					, _lowerCamelCase					, quiet=_lowerCamelCase						)
  _lowerCAmelCase    :					Dict							=   torch.load(_lowerCamelCase					, map_location="""cpu"""						)
  if "model" in files:
    _lowerCAmelCase    :					int							=   files["""model"""]
  elif "module" in files:
    _lowerCAmelCase    :					Optional[int]							=   files["""module"""]
  else:
    _lowerCAmelCase    :					List[Any]							=   files["""model_state"""]
  _lowerCAmelCase    :					Dict							=   convert_state_dict(_lowerCamelCase					, _lowerCamelCase						)

  model.load_state_dict(_lowerCamelCase						)
  model.eval()

  # verify model on basic input
  _lowerCAmelCase    :					Tuple							=   VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]					, image_std=[0.5, 0.5, 0.5]						)
  _lowerCAmelCase    :					int							=   prepare_video()
  _lowerCAmelCase    :					Any							=   image_processor(video[:8]					, return_tensors="""pt"""						)

  _lowerCAmelCase    :					str							=   model(**_lowerCamelCase						)
  _lowerCAmelCase    :					int							=   outputs.logits

  _lowerCAmelCase    :					int							=   [
      # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
      """timesformer-base-finetuned-k400""",
      """timesformer-large-finetuned-k400""",
      """timesformer-hr-finetuned-k400""",
      # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
      """timesformer-base-finetuned-k600""",
      """timesformer-large-finetuned-k600""",
      """timesformer-hr-finetuned-k600""",
      # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
      """timesformer-base-finetuned-ssv2""",
      """timesformer-large-finetuned-ssv2""",
      """timesformer-hr-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 == "timesformer-base-finetuned-k400":
    _lowerCAmelCase    :					Tuple							=   torch.Size([1, 4_00]						)
    _lowerCAmelCase    :					Optional[int]							=   torch.tensor([-0.3016, -0.7713, -0.4205]						)
  elif model_name == "timesformer-base-finetuned-k600":
    _lowerCAmelCase    :					Union[str, Any]							=   torch.Size([1, 6_00]						)
    _lowerCAmelCase    :					int							=   torch.tensor([-0.7267, -0.7466, 3.2404]						)
  elif model_name == "timesformer-base-finetuned-ssv2":
    _lowerCAmelCase    :					Any							=   torch.Size([1, 1_74]						)
    _lowerCAmelCase    :					Dict							=   torch.tensor([-0.9059, 0.6433, -3.1457]						)
  elif model_name == "timesformer-large-finetuned-k400":
    _lowerCAmelCase    :					List[str]							=   torch.Size([1, 4_00]						)
    _lowerCAmelCase    :					List[str]							=   torch.tensor([0, 0, 0]						)
  elif model_name == "timesformer-large-finetuned-k600":
    _lowerCAmelCase    :					Dict							=   torch.Size([1, 6_00]						)
    _lowerCAmelCase    :					int							=   torch.tensor([0, 0, 0]						)
  elif model_name == "timesformer-large-finetuned-ssv2":
    _lowerCAmelCase    :					Optional[int]							=   torch.Size([1, 1_74]						)
    _lowerCAmelCase    :					int							=   torch.tensor([0, 0, 0]						)
  elif model_name == "timesformer-hr-finetuned-k400":
    _lowerCAmelCase    :					Tuple							=   torch.Size([1, 4_00]						)
    _lowerCAmelCase    :					Optional[Any]							=   torch.tensor([-0.9617, -3.7311, -3.7708]						)
  elif model_name == "timesformer-hr-finetuned-k600":
    _lowerCAmelCase    :					List[Any]							=   torch.Size([1, 6_00]						)
    _lowerCAmelCase    :					Tuple							=   torch.tensor([2.5273, 0.7127, 1.8848]						)
  elif model_name == "timesformer-hr-finetuned-ssv2":
    _lowerCAmelCase    :					Union[str, Any]							=   torch.Size([1, 1_74]						)
    _lowerCAmelCase    :					Tuple							=   torch.tensor([-3.6756, -0.7513, 0.7180]						)
  else:
    raise ValueError(f'Model name not supported. Should be one of {model_names}'						)

  # verify logits
  assert logits.shape == expected_shape
  assert torch.allclose(logits[0, :3]					, _lowerCamelCase					, atol=1e-4						)
  print("""Logits 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(_lowerCamelCase						)
    model.save_pretrained(_lowerCamelCase						)

  if push_to_hub:
    print("""Pushing to the hub..."""						)
    model.push_to_hub(f'fcakyon/{model_name}'						)


if __name__ == "__main__":
     UpperCamelCase_            =    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--checkpoint_url""",
         default="""https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download""",
         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="""""",
         type=str,
         help="""Path to the output PyTorch model directory.""",
     )
     parser.add_argument("""--model_name""", default="""timesformer-base-finetuned-k400""", 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_timesformer_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
     )




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCamelCase_            =    {"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GPTJForCausalLM""",
         """GPTJForQuestionAnswering""",
         """GPTJForSequenceClassification""",
         """GPTJModel""",
         """GPTJPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """TFGPTJForCausalLM""",
         """TFGPTJForQuestionAnswering""",
         """TFGPTJForSequenceClassification""",
         """TFGPTJModel""",
         """TFGPTJPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """FlaxGPTJForCausalLM""",
         """FlaxGPTJModel""",
         """FlaxGPTJPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     UpperCamelCase_            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


