

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   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__=None  ,		SCREAMING_SNAKE_CASE__=None	):
      return field(default_factory=lambda: default  ,		metadata=snake_case__	)





@dataclass
class      snake_case_       :







   '''simple docstring'''

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






def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
      try:
            int(snake_case__	)
            return True
      except ValueError:
            return False


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
      try:
            float(snake_case__	)
            return True
      except ValueError:
            return False





class      snake_case_       :







   '''simple docstring'''

   def __init__(					self			:    List[str]							,       _UpperCamelCase			:    Optional[Any]							)				->Optional[int]:
         snake_case_       =     args
         snake_case_       =     defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}							)

         with open(self.args.csv_file							,       newline=''''''							) as csv_file:
               snake_case_       =     csv.DictReader(__SCREAMING_SNAKE_CASE							)
               for row in reader:
                     snake_case_       =     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
                           snake_case_       =     int(row['''result''']							)
                     elif can_convert_to_float(row['''result''']							):
                           # value is not None
                           snake_case_       =     float(row['''result''']							)







   def     snake_case__(					self			:    str							)				->Tuple:
         snake_case_,   snake_case_       =     plt.subplots()
         snake_case_       =     '''Time usage''' if self.args.is_time else '''Memory usage'''
         snake_case_       =     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()							):
               snake_case_       =     sorted(set(self.result_dict[model_name]['''bsz''']							)							)
               snake_case_       =     sorted(set(self.result_dict[model_name]['''seq_len''']							)							)
               snake_case_       =     self.result_dict[model_name]['''result''']

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

               snake_case_       =     (
                   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:
                           snake_case_       =     np.asarray(
                               [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							,       dtype=__SCREAMING_SNAKE_CASE							,       )
                     else:
                           snake_case_       =     np.asarray(
                               [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]							,       dtype=np.floataa							,       )

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

                     snake_case_       =     np.asarray(__SCREAMING_SNAKE_CASE							,       __SCREAMING_SNAKE_CASE							)[: len(__SCREAMING_SNAKE_CASE							)]
                     plt.scatter(
                         __SCREAMING_SNAKE_CASE							,       __SCREAMING_SNAKE_CASE							,       label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''							)
                     plt.plot(__SCREAMING_SNAKE_CASE							,       __SCREAMING_SNAKE_CASE							,       '''--'''							)

               title_str += f''' {label_model_name} vs.'''

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

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

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






def   __SCREAMING_SNAKE_CASE ():
      snake_case_       =     HfArgumentParser(snake_case__	)
      snake_case_       =     parser.parse_args_into_dataclasses()[0]
      snake_case_       =     Plot(args=snake_case__	)
      plot.plot()


if __name__ == "__main__":
     main()


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   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						snake_case_       =     TimesformerConfig()

						if "large" in model_name:
												snake_case_       =     96

						if "hr" in model_name:
												snake_case_       =     16
												snake_case_       =     448

						snake_case_       =     '''huggingface/label-files'''
						if "k400" in model_name:
												snake_case_       =     400
												snake_case_       =     '''kinetics400-id2label.json'''
						elif "k600" in model_name:
												snake_case_       =     600
												snake_case_       =     '''kinetics600-id2label.json'''
						elif "ssv2" in model_name:
												snake_case_       =     174
												snake_case_       =     '''something-something-v2-id2label.json'''
						else:
												raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'''	)
						snake_case_       =     json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		repo_type='''dataset'''	)  ,		'''r'''	)	)
						snake_case_       =     {int(SCREAMING_SNAKE_CASE__	): v for k, v in idalabel.items()}
						snake_case_       =     idalabel
						snake_case_       =     {v: k for k, v in idalabel.items()}

						return config


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						if "encoder." in name:
												snake_case_       =     name.replace('''encoder.'''  ,		''''''	)
						if "cls_token" in name:
												snake_case_       =     name.replace('''cls_token'''  ,		'''timesformer.embeddings.cls_token'''	)
						if "pos_embed" in name:
												snake_case_       =     name.replace('''pos_embed'''  ,		'''timesformer.embeddings.position_embeddings'''	)
						if "time_embed" in name:
												snake_case_       =     name.replace('''time_embed'''  ,		'''timesformer.embeddings.time_embeddings'''	)
						if "patch_embed.proj" in name:
												snake_case_       =     name.replace('''patch_embed.proj'''  ,		'''timesformer.embeddings.patch_embeddings.projection'''	)
						if "patch_embed.norm" in name:
												snake_case_       =     name.replace('''patch_embed.norm'''  ,		'''timesformer.embeddings.norm'''	)
						if "blocks" in name:
												snake_case_       =     name.replace('''blocks'''  ,		'''timesformer.encoder.layer'''	)
						if "attn.proj" in name:
												snake_case_       =     name.replace('''attn.proj'''  ,		'''attention.output.dense'''	)
						if "attn" in name and "bias" not in name and "temporal" not in name:
												snake_case_       =     name.replace('''attn'''  ,		'''attention.self'''	)
						if "attn" in name and "temporal" not in name:
												snake_case_       =     name.replace('''attn'''  ,		'''attention.attention'''	)
						if "temporal_norm1" in name:
												snake_case_       =     name.replace('''temporal_norm1'''  ,		'''temporal_layernorm'''	)
						if "temporal_attn.proj" in name:
												snake_case_       =     name.replace('''temporal_attn'''  ,		'''temporal_attention.output.dense'''	)
						if "temporal_fc" in name:
												snake_case_       =     name.replace('''temporal_fc'''  ,		'''temporal_dense'''	)
						if "norm1" in name and "temporal" not in name:
												snake_case_       =     name.replace('''norm1'''  ,		'''layernorm_before'''	)
						if "norm2" in name:
												snake_case_       =     name.replace('''norm2'''  ,		'''layernorm_after'''	)
						if "mlp.fc1" in name:
												snake_case_       =     name.replace('''mlp.fc1'''  ,		'''intermediate.dense'''	)
						if "mlp.fc2" in name:
												snake_case_       =     name.replace('''mlp.fc2'''  ,		'''output.dense'''	)
						if "norm.weight" in name and "fc" not in name and "temporal" not in name:
												snake_case_       =     name.replace('''norm.weight'''  ,		'''timesformer.layernorm.weight'''	)
						if "norm.bias" in name and "fc" not in name and "temporal" not in name:
												snake_case_       =     name.replace('''norm.bias'''  ,		'''timesformer.layernorm.bias'''	)
						if "head" in name:
												snake_case_       =     name.replace('''head'''  ,		'''classifier'''	)

						return name


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						for key in orig_state_dict.copy().keys():
												snake_case_       =     orig_state_dict.pop(SCREAMING_SNAKE_CASE__	)

												if key.startswith('''model.'''	):
																		snake_case_       =     key.replace('''model.'''  ,		''''''	)

												if "qkv" in key:
																		snake_case_       =     key.split('''.'''	)
																		snake_case_       =     int(key_split[1]	)
																		snake_case_       =     '''timesformer.encoder.layer.'''
																		if "temporal" in key:
																								snake_case_       =     '''.temporal_attention.attention.qkv.'''
																		else:
																								snake_case_       =     '''.attention.attention.qkv.'''
																		if "weight" in key:
																								snake_case_       =     val
																		else:
																								snake_case_       =     val
												else:
																		snake_case_       =     val

						return orig_state_dict


def   __SCREAMING_SNAKE_CASE ():
						snake_case_       =     hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video'''  ,		filename='''eating_spaghetti.npy'''  ,		repo_type='''dataset'''	)
						snake_case_       =     np.load(SCREAMING_SNAKE_CASE__	)
						return list(SCREAMING_SNAKE_CASE__	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     get_timesformer_config(SCREAMING_SNAKE_CASE__	)

						snake_case_       =     TimesformerForVideoClassification(SCREAMING_SNAKE_CASE__	)

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

						model.load_state_dict(SCREAMING_SNAKE_CASE__	)
						model.eval()

						# verify model on basic input
						snake_case_       =     VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]  ,		image_std=[0.5, 0.5, 0.5]	)
						snake_case_       =     prepare_video()
						snake_case_       =     image_processor(video[:8]  ,		return_tensors='''pt'''	)

						snake_case_       =     model(**SCREAMING_SNAKE_CASE__	)
						snake_case_       =     outputs.logits

						snake_case_       =     [
						    # 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":
												snake_case_       =     torch.Size([1, 400]	)
												snake_case_       =     torch.tensor([-0.3016, -0.7713, -0.4205]	)
						elif model_name == "timesformer-base-finetuned-k600":
												snake_case_       =     torch.Size([1, 600]	)
												snake_case_       =     torch.tensor([-0.7267, -0.7466, 3.2404]	)
						elif model_name == "timesformer-base-finetuned-ssv2":
												snake_case_       =     torch.Size([1, 174]	)
												snake_case_       =     torch.tensor([-0.9059, 0.6433, -3.1457]	)
						elif model_name == "timesformer-large-finetuned-k400":
												snake_case_       =     torch.Size([1, 400]	)
												snake_case_       =     torch.tensor([0, 0, 0]	)
						elif model_name == "timesformer-large-finetuned-k600":
												snake_case_       =     torch.Size([1, 600]	)
												snake_case_       =     torch.tensor([0, 0, 0]	)
						elif model_name == "timesformer-large-finetuned-ssv2":
												snake_case_       =     torch.Size([1, 174]	)
												snake_case_       =     torch.tensor([0, 0, 0]	)
						elif model_name == "timesformer-hr-finetuned-k400":
												snake_case_       =     torch.Size([1, 400]	)
												snake_case_       =     torch.tensor([-0.9617, -3.7311, -3.7708]	)
						elif model_name == "timesformer-hr-finetuned-k600":
												snake_case_       =     torch.Size([1, 600]	)
												snake_case_       =     torch.tensor([2.5273, 0.7127, 1.8848]	)
						elif model_name == "timesformer-hr-finetuned-ssv2":
												snake_case_       =     torch.Size([1, 174]	)
												snake_case_       =     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]  ,		SCREAMING_SNAKE_CASE__  ,		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(SCREAMING_SNAKE_CASE__	)
												model.save_pretrained(SCREAMING_SNAKE_CASE__	)

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


if __name__ == "__main__":
					lowerCAmelCase_	     =     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.'''
					)

					lowerCAmelCase_	     =     parser.parse_args()
					convert_timesformer_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
					)