'''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    _lowerCamelCase       ( lowerCamelCase_   :				Optional[Any]  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[Any]  = TimesformerConfig()

     if "large" in model_name:
          UpperCAmelCase_				:  Optional[Any]  = 96

     if "hr" in model_name:
          UpperCAmelCase_				:  str  = 16
          UpperCAmelCase_				:  List[str]  = 448

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

     return config


def    _lowerCamelCase       ( lowerCamelCase_   :				int  ):



     """simple docstring"""





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

     return name


def    _lowerCamelCase       ( lowerCamelCase_   :				Tuple      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_				:  Union[str, Any]  = orig_state_dict.pop(__lowerCamelCase  )

          if key.startswith('model.'  ):
               UpperCAmelCase_				:  List[Any]  = key.replace('model.'      ,	''  )

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

     return orig_state_dict


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  List[Any]  = hf_hub_download(
         repo_id='hf-internal-testing/spaghetti-video'      ,	filename='eating_spaghetti.npy'      ,	repo_type='dataset'  )
     UpperCAmelCase_				:  Optional[int]  = np.load(__lowerCamelCase  )
     return list(__lowerCamelCase  )


def    _lowerCamelCase       ( lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				Any  ):



     """simple docstring"""





     UpperCAmelCase_				:  Dict  = get_timesformer_config(__lowerCamelCase  )

     UpperCAmelCase_				:  str  = TimesformerForVideoClassification(__lowerCamelCase  )

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

     model.load_state_dict(__lowerCamelCase  )
     model.eval()

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

     UpperCAmelCase_				:  List[Any]  = model(**__lowerCamelCase  )
     UpperCAmelCase_				:  Union[str, Any]  = outputs.logits

     UpperCAmelCase_				:  Optional[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":
          UpperCAmelCase_				:  str  = torch.Size([1, 400]  )
          UpperCAmelCase_				:  List[str]  = torch.tensor([-0.3016, -0.7713, -0.4205]  )
     elif model_name == "timesformer-base-finetuned-k600":
          UpperCAmelCase_				:  int  = torch.Size([1, 600]  )
          UpperCAmelCase_				:  List[str]  = torch.tensor([-0.7267, -0.7466, 3.2404]  )
     elif model_name == "timesformer-base-finetuned-ssv2":
          UpperCAmelCase_				:  str  = torch.Size([1, 174]  )
          UpperCAmelCase_				:  List[Any]  = torch.tensor([-0.9059, 0.6433, -3.1457]  )
     elif model_name == "timesformer-large-finetuned-k400":
          UpperCAmelCase_				:  Optional[int]  = torch.Size([1, 400]  )
          UpperCAmelCase_				:  List[Any]  = torch.tensor([0, 0, 0]  )
     elif model_name == "timesformer-large-finetuned-k600":
          UpperCAmelCase_				:  str  = torch.Size([1, 600]  )
          UpperCAmelCase_				:  Tuple  = torch.tensor([0, 0, 0]  )
     elif model_name == "timesformer-large-finetuned-ssv2":
          UpperCAmelCase_				:  List[str]  = torch.Size([1, 174]  )
          UpperCAmelCase_				:  int  = torch.tensor([0, 0, 0]  )
     elif model_name == "timesformer-hr-finetuned-k400":
          UpperCAmelCase_				:  Any  = torch.Size([1, 400]  )
          UpperCAmelCase_				:  Tuple  = torch.tensor([-0.9617, -3.7311, -3.7708]  )
     elif model_name == "timesformer-hr-finetuned-k600":
          UpperCAmelCase_				:  Any  = torch.Size([1, 600]  )
          UpperCAmelCase_				:  Optional[Any]  = torch.tensor([2.5273, 0.7127, 1.8848]  )
     elif model_name == "timesformer-hr-finetuned-ssv2":
          UpperCAmelCase_				:  Dict  = torch.Size([1, 174]  )
          UpperCAmelCase_				:  Any  = 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__":
 snake_case__	:     Tuple      =    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.'''
 )

 snake_case__	:     List[Any]      =    parser.parse_args()
 convert_timesformer_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
 )







'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 				lowercase    (    __lowerCamelCase			,					unittest.TestCase			):
   lowerCamelCase_            =KandinskyPipeline
   lowerCamelCase_            =[
       'prompt',
       'image_embeds',
       'negative_image_embeds',
   ]
   lowerCamelCase_            =['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
   lowerCamelCase_            =[
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'negative_prompt',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   lowerCamelCase_            =False


   @property
   def __UpperCAmelCase							( self					:						Optional[int])   ->       Union[str, Any]:
         return 32


   @property
   def __UpperCAmelCase							( self					:						List[str])   ->       int:
         return 32


   @property
   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Any:
         return self.time_input_dim


   @property
   def __UpperCAmelCase							( self					:						List[str])   ->       str:
         return self.time_input_dim * 4


   @property
   def __UpperCAmelCase							( self					:						List[str])   ->       str:
         return 100


   @property
   def __UpperCAmelCase							( self					:						Tuple)   ->       List[str]:
         lowercase_     			=   XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base")
         return tokenizer


   @property
   def __UpperCAmelCase							( self					:						Any)   ->       Optional[int]:
         torch.manual_seed(0)
         lowercase_     			=   MCLIPConfig(
             numDims=self.cross_attention_dim ,		transformerDimensions=self.text_embedder_hidden_size ,		hidden_size=self.text_embedder_hidden_size ,		intermediate_size=37 ,		num_attention_heads=4 ,		num_hidden_layers=5 ,		vocab_size=1005 ,		)

         lowercase_     			=   MultilingualCLIP(__lowerCAmelCase)
         lowercase_     			=   text_encoder.eval()

         return text_encoder


   @property
   def __UpperCAmelCase							( self					:						Tuple)   ->       Any:
         torch.manual_seed(0)

         lowercase_     			=   {
             "in_channels": 4,
             # Out channels is double in channels because predicts mean and variance
             "out_channels": 8,
             "addition_embed_type": "text_image",
             "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
             "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
             "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "layers_per_block": 1,
             "encoder_hid_dim": self.text_embedder_hidden_size,
             "encoder_hid_dim_type": "text_image_proj",
             "cross_attention_dim": self.cross_attention_dim,
             "attention_head_dim": 4,
             "resnet_time_scale_shift": "scale_shift",
             "class_embed_type": None,
         }

         lowercase_     			=   UNetaDConditionModel(**__lowerCAmelCase)
         return model


   @property
   def __UpperCAmelCase							( self					:						int)   ->       Optional[int]:
         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }


   @property
   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Tuple:
         torch.manual_seed(0)
         lowercase_     			=   VQModel(**self.dummy_movq_kwargs)
         return model


   def __UpperCAmelCase							( self					:						Tuple)   ->       Union[str, Any]:
         lowercase_     			=   self.dummy_text_encoder
         lowercase_     			=   self.dummy_tokenizer
         lowercase_     			=   self.dummy_unet
         lowercase_     			=   self.dummy_movq

         lowercase_     			=   DDIMScheduler(
             num_train_timesteps=1000 ,		beta_schedule="linear" ,		beta_start=0.0_0085 ,		beta_end=0.012 ,		clip_sample=__lowerCAmelCase ,		set_alpha_to_one=__lowerCAmelCase ,		steps_offset=1 ,		prediction_type="epsilon" ,		thresholding=__lowerCAmelCase ,		)

         lowercase_     			=   {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						List[str]=0)   ->       List[Any]:
         lowercase_     			=   floats_tensor((1, self.cross_attention_dim) ,		rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
         lowercase_     			=   floats_tensor((1, self.cross_attention_dim) ,		rng=random.Random(seed + 1)).to(__lowerCAmelCase)
         if str(__lowerCAmelCase).startswith("mps"):
               lowercase_     			=   torch.manual_seed(__lowerCAmelCase)
         else:
               lowercase_     			=   torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
         lowercase_     			=   {
             "prompt": "horse",
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "generator": generator,
             "height": 64,
             "width": 64,
             "guidance_scale": 4.0,
             "num_inference_steps": 2,
             "output_type": "np",
         }
         return inputs


   def __UpperCAmelCase							( self					:						Any)   ->       Optional[int]:
         lowercase_     			=   "cpu"

         lowercase_     			=   self.get_dummy_components()

         lowercase_     			=   self.pipeline_class(**__lowerCAmelCase)
         lowercase_     			=   pipe.to(__lowerCAmelCase)

         pipe.set_progress_bar_config(disable=__lowerCAmelCase)

         lowercase_     			=   pipe(**self.get_dummy_inputs(__lowerCAmelCase))
         lowercase_     			=   output.images

         lowercase_     			=   pipe(
             **self.get_dummy_inputs(__lowerCAmelCase) ,		return_dict=__lowerCAmelCase ,		)[0]

         lowercase_     			=   image[0, -3:, -3:, -1]
         lowercase_     			=   image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         lowercase_     			=   np.array(
             [0.32_8663, 1.0, 0.2321_6873, 1.0, 0.9271_7564, 0.463_9046, 0.9689_4777, 0.3171_3378, 0.629_3953])

         assert (
             np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         ), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
         ), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'





@slow
@require_torch_gpu
class 				lowercase    (    unittest.TestCase			):


   def __UpperCAmelCase							( self					:						str)   ->       Tuple:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()


   def __UpperCAmelCase							( self					:						List[str])   ->       Tuple:
         lowercase_     			=   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinsky/kandinsky_text2img_cat_fp16.npy")

         lowercase_     			=   KandinskyPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-1-prior" ,		torch_dtype=torch.floataa)
         pipe_prior.to(__lowerCAmelCase)

         lowercase_     			=   KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1" ,		torch_dtype=torch.floataa)
         lowercase_     			=   pipeline.to(__lowerCAmelCase)
         pipeline.set_progress_bar_config(disable=__lowerCAmelCase)

         lowercase_     			=   "red cat, 4k photo"

         lowercase_     			=   torch.Generator(device="cuda").manual_seed(0)
         lowercase_			,				lowercase_     			=   pipe_prior(
             __lowerCAmelCase ,		generator=__lowerCAmelCase ,		num_inference_steps=5 ,		negative_prompt="" ,		).to_tuple()

         lowercase_     			=   torch.Generator(device="cuda").manual_seed(0)
         lowercase_     			=   pipeline(
             __lowerCAmelCase ,		image_embeds=__lowerCAmelCase ,		negative_image_embeds=__lowerCAmelCase ,		generator=__lowerCAmelCase ,		num_inference_steps=100 ,		output_type="np" ,		)

         lowercase_     			=   output.images[0]

         assert image.shape == (512, 512, 3)

         assert_mean_pixel_difference(__lowerCAmelCase ,		__lowerCAmelCase)






