

"""simple docstring"""





import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser




def 						_A						(						UpperCamelCase_  : List[Any]=None,    UpperCamelCase_  : List[Any]=None)			->		Optional[Any]:
   '''simple docstring'''


   return field(default_factory=lambda: default,    metadata=UpperCamelCase_)




@dataclass
class       _lowerCAmelCase						:







 """simple docstring"""







 __UpperCAmelCase			:      str     	=			field(
     metadata={"help": "The csv file to plot."}  ,)
 __UpperCAmelCase			:      bool     	=			field(
     default=lowercase  ,metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}  ,)
 __UpperCAmelCase			:      bool     	=			field(
     default=lowercase  ,metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}  ,)
 __UpperCAmelCase			:      bool     	=			field(
     default=lowercase  ,metadata={"help": "Disable logarithmic scale when plotting"}  ,)
 __UpperCAmelCase			:      bool     	=			field(
     default=lowercase  ,metadata={
         "help": "Whether the csv file has training results or inference results. Defaults to inference results."
     }  ,)
 __UpperCAmelCase			:      Optional[str]     	=			field(
     default=lowercase  ,metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}  ,)
 __UpperCAmelCase			:      Optional[List[str]]     	=			list_field(
     default=lowercase  ,metadata={"help": "List of model names that are used instead of the ones in the csv file."}     )







def 						_A						(						UpperCamelCase_  : int)			->		List[Any]:
   '''simple docstring'''


   try:
      int(UpperCamelCase_)
      return True
   except ValueError:
      return False




def 						_A						(						UpperCamelCase_  : Optional[Any])			->		int:
   '''simple docstring'''


   try:
      float(UpperCamelCase_)
      return True
   except ValueError:
      return False




class       _lowerCAmelCase						:







 """simple docstring"""







 def __init__(    self							:    Tuple,      UpperCAmelCase__							:    Union[str, Any]			):
    __lowercase			=		args
    __lowercase			=		defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

    with open(self.args.csv_file,      newline=""			) as csv_file:
       __lowercase			=		csv.DictReader(UpperCAmelCase__			)
       for row in reader:
          __lowercase			=		row["model"]
          self.result_dict[model_name]["bsz"].append(int(row["batch_size"]			)			)
          self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]			)			)
          if can_convert_to_int(row["result"]			):
             # value is not None
             __lowercase			=		int(row["result"]			)
          elif can_convert_to_float(row["result"]			):
             # value is not None
             __lowercase			=		float(row["result"]			)



 def       _lowercase				(    self							:    List[str]			):
    __lowercase	,__lowercase			=		plt.subplots()
    __lowercase			=		"Time usage" if self.args.is_time else "Memory usage"
    __lowercase			=		title_str + " for training" if self.args.is_train else title_str + " for inference"

    if not self.args.no_log_scale:
       # set logarithm scales
       ax.set_xscale("log"			)
       ax.set_yscale("log"			)

    for axis in [ax.xaxis, ax.yaxis]:
       axis.set_major_formatter(ScalarFormatter()			)

    for model_name_idx, model_name in enumerate(self.result_dict.keys()			):
       __lowercase			=		sorted(set(self.result_dict[model_name]["bsz"]			)			)
       __lowercase			=		sorted(set(self.result_dict[model_name]["seq_len"]			)			)
       __lowercase			=		self.result_dict[model_name]["result"]

       ((__lowercase)	,(__lowercase))			=		(
           (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
       )

       __lowercase			=		(
           model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
       )

       for inner_loop_value in inner_loop_array:
          if self.args.plot_along_batch:
             __lowercase			=		np.asarray(
                 [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results],      dtype=UpperCAmelCase__,      )
          else:
             __lowercase			=		np.asarray(
                 [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results],      dtype=np.floataa,      )

          ((__lowercase)	,(__lowercase))			=		(
              ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
          )

          __lowercase			=		np.asarray(UpperCAmelCase__,      UpperCAmelCase__			)[: len(UpperCAmelCase__			)]
          plt.scatter(
              UpperCAmelCase__,      UpperCAmelCase__,      label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""			)
          plt.plot(UpperCAmelCase__,      UpperCAmelCase__,      "--"			)

       title_str += F""" {label_model_name} vs."""

    __lowercase			=		title_str[:-4]
    __lowercase			=		"Time in s" if self.args.is_time else "Memory in MB"

    # plot
    plt.title(UpperCAmelCase__			)
    plt.xlabel(UpperCAmelCase__			)
    plt.ylabel(UpperCAmelCase__			)
    plt.legend()

    if self.args.figure_png_file is not None:
       plt.savefig(self.args.figure_png_file			)
    else:
       plt.show()







def 						_A						(						)			->		List[str]:
   '''simple docstring'''


   __lowercase			=		HfArgumentParser(UpperCamelCase_)
   __lowercase			=		parser.parse_args_into_dataclasses()[0]
   __lowercase			=		Plot(args=UpperCamelCase_)
   plot.plot()


if __name__ == "__main__":
   main()







"""simple docstring"""





import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling




def 						_A						(						UpperCamelCase_  : str)			->		Optional[Any]:
   '''simple docstring'''


   __lowercase			=		"https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"

   with open(UpperCamelCase_,    "wb") as f:
      with requests.get(UpperCamelCase_,    stream=UpperCamelCase_) as req:
         for chunk in req.iter_content(chunk_size=2048):
            f.write(UpperCamelCase_)




def 						_A						(						)			->		VivitConfig:
   '''simple docstring'''


   __lowercase			=		VivitConfig()

   __lowercase			=		400
   __lowercase			=		"huggingface/label-files"
   __lowercase			=		"kinetics400-id2label.json"

   __lowercase			=		json.load(open(hf_hub_download(UpperCamelCase_,    UpperCamelCase_,    repo_type="dataset"),    "r"))
   __lowercase			=		{int(UpperCamelCase_): v for k, v in idalabel.items()}
   __lowercase			=		idalabel
   __lowercase			=		{v: k for k, v in idalabel.items()}
   return config




def 						_A						(						)			->		List[Any]:
   '''simple docstring'''


   __lowercase			=		hf_hub_download(
       repo_id="hf-internal-testing/spaghetti-video",    filename="eating_spaghetti_32_frames.npy",    repo_type="dataset")
   __lowercase			=		np.load(UpperCamelCase_)
   return list(UpperCamelCase_)




def 						_A						(						UpperCamelCase_  : np.ndarray)			->		Optional[int]:
   '''simple docstring'''


   if np.ndim(UpperCamelCase_) == 2:
      return transform_attention_bias(UpperCamelCase_)

   elif np.ndim(UpperCamelCase_) == 3:
      return transform_attention_kernel(UpperCamelCase_)

   else:
      raise Exception(F"""Invalid number of dimesions: {np.ndim(UpperCamelCase_)}""")




def 						_A						(						UpperCamelCase_  : np.ndarray)			->		Optional[Any]:
   '''simple docstring'''


   return current.flatten()




def 						_A						(						UpperCamelCase_  : np.ndarray)			->		Any:
   '''simple docstring'''


   return np.reshape(UpperCamelCase_,    (current.shape[0], current.shape[1] * current.shape[2])).T




def 						_A						(						UpperCamelCase_  : np.ndarray)			->		Any:
   '''simple docstring'''


   return np.reshape(UpperCamelCase_,    (current.shape[0] * current.shape[1], current.shape[2])).T




def 						_A						(						UpperCamelCase_  : Tuple,    UpperCamelCase_  : Optional[Any])			->		Any:
   '''simple docstring'''


   __lowercase			=		state_dict["optimizer"]["target"]["Transformer"][F"""encoderblock_{i}"""]

   __lowercase			=		F"""encoder.layer.{i}."""
   __lowercase			=		{
       prefix + "intermediate.dense.bias": state["MlpBlock_0"]["Dense_0"]["bias"],
       prefix + "intermediate.dense.weight": np.transpose(state["MlpBlock_0"]["Dense_0"]["kernel"]),
       prefix + "output.dense.bias": state["MlpBlock_0"]["Dense_1"]["bias"],
       prefix + "output.dense.weight": np.transpose(state["MlpBlock_0"]["Dense_1"]["kernel"]),
       prefix + "layernorm_before.bias": state["LayerNorm_0"]["bias"],
       prefix + "layernorm_before.weight": state["LayerNorm_0"]["scale"],
       prefix + "layernorm_after.bias": state["LayerNorm_1"]["bias"],
       prefix + "layernorm_after.weight": state["LayerNorm_1"]["scale"],
       prefix
       + "attention.attention.query.bias": transform_attention(
           state["MultiHeadDotProductAttention_0"]["query"]["bias"]),
       prefix
       + "attention.attention.query.weight": transform_attention(
           state["MultiHeadDotProductAttention_0"]["query"]["kernel"]),
       prefix
       + "attention.attention.key.bias": transform_attention(state["MultiHeadDotProductAttention_0"]["key"]["bias"]),
       prefix
       + "attention.attention.key.weight": transform_attention(
           state["MultiHeadDotProductAttention_0"]["key"]["kernel"]),
       prefix
       + "attention.attention.value.bias": transform_attention(
           state["MultiHeadDotProductAttention_0"]["value"]["bias"]),
       prefix
       + "attention.attention.value.weight": transform_attention(
           state["MultiHeadDotProductAttention_0"]["value"]["kernel"]),
       prefix + "attention.output.dense.bias": state["MultiHeadDotProductAttention_0"]["out"]["bias"],
       prefix
       + "attention.output.dense.weight": transform_attention_output_weight(
           state["MultiHeadDotProductAttention_0"]["out"]["kernel"]),
   }

   return new_state




def 						_A						(						UpperCamelCase_  : str)			->		List[Any]:
   '''simple docstring'''


   return sum([1 if "encoderblock_" in k else 0 for k in state_dict["optimizer"]["target"]["Transformer"].keys()])




def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : Any=False)			->		Any:
   '''simple docstring'''


   __lowercase			=		get_n_layers(UpperCamelCase_)

   __lowercase			=		OrderedDict()

   __lowercase			=		state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
   __lowercase			=		state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

   __lowercase			=		np.transpose(
       state_dict["optimizer"]["target"]["embedding"]["kernel"],    (4, 3, 0, 1, 2))
   __lowercase			=		state_dict["optimizer"]["target"]["embedding"]["bias"]

   __lowercase			=		state_dict["optimizer"]["target"]["cls"]
   __lowercase			=		state_dict["optimizer"]["target"]["Transformer"]["posembed_input"][
       "pos_embedding"
   ]

   for i in range(UpperCamelCase_):
      new_state.update(transform_state_encoder_block(UpperCamelCase_,    UpperCamelCase_))

   if classification_head:
      __lowercase			=		{"vivit." + k: v for k, v in new_state.items()}
      __lowercase			=		np.transpose(state_dict["optimizer"]["target"]["output_projection"]["kernel"])
      __lowercase			=		np.transpose(state_dict["optimizer"]["target"]["output_projection"]["bias"])

   return {k: torch.tensor(UpperCamelCase_) for k, v in new_state.items()}




def 						_A						(						)			->		VivitImageProcessor:
   '''simple docstring'''


   __lowercase			=		VivitImageProcessor()

   assert extractor.do_resize is True
   assert extractor.size == {"shortest_edge": 256}
   assert extractor.do_center_crop is True
   assert extractor.crop_size == {"width": 224, "height": 224}
   assert extractor.resample == PILImageResampling.BILINEAR

   # here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
   # one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
   # which effectively means no normalization (and ViViT does not overwrite those when calling this func)
   assert extractor.do_normalize is False
   assert extractor.do_rescale is True
   assert extractor.rescale_factor == 1 / 255

   # zero-centering = True in original implementation
   assert extractor.do_zero_centering is True

   return extractor




def 						_A						(						UpperCamelCase_  : str)			->		List[str]:
   '''simple docstring'''


   __lowercase			=		"checkpoint"

   if not os.path.exists(UpperCamelCase_):
      download_checkpoint(UpperCamelCase_)

   __lowercase			=		restore_checkpoint(UpperCamelCase_,    UpperCamelCase_)
   __lowercase			=		transform_state(UpperCamelCase_,    classification_head=UpperCamelCase_)

   __lowercase			=		get_vivit_config()

   assert config.image_size == 224
   assert config.num_frames == 32

   __lowercase			=		VivitForVideoClassification(UpperCamelCase_)
   model.load_state_dict(UpperCamelCase_)
   model.eval()

   __lowercase			=		get_processor()

   __lowercase			=		prepare_video()
   __lowercase			=		extractor(UpperCamelCase_,    return_tensors="pt")

   __lowercase			=		model(**UpperCamelCase_)

   __lowercase			=		torch.Size([1, 400])
   __lowercase			=		torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658])

   assert outputs.logits.shape == expected_shape
   assert torch.allclose(outputs.logits[0, :5],    UpperCamelCase_,    atol=1E-4), outputs.logits[0, :5]

   model.save_pretrained(UpperCamelCase_)
   extractor.save_pretrained(UpperCamelCase_)


if __name__ == "__main__":
   _a				   =							argparse.ArgumentParser()

   parser.add_argument('--output_model_name', '-o', type=str, help='Output path for the converted HuggingFace model')

   _a				   =							parser.parse_args()
   convert(args.output_model_name)




