

"""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   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   TimesformerConfig()

					if "large" in model_name:
										A     =   96

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

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

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

					return name
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					for key in orig_state_dict.copy().keys():
										A     =   orig_state_dict.pop(lowercase__ )

										if key.startswith("model." ):
															A     =   key.replace("model."     ,				"" )

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

					return orig_state_dict
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A     =   hf_hub_download(
					    repo_id="hf-internal-testing/spaghetti-video"     ,				filename="eating_spaghetti.npy"     ,				repo_type="dataset" )
					A     =   np.load(lowercase__ )
					return list(lowercase__ )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   get_timesformer_config(lowercase__ )

					A     =   TimesformerForVideoClassification(lowercase__ )

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

					model.load_state_dict(lowercase__ )
					model.eval()

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

					A     =   model(**lowercase__ )
					A     =   outputs.logits

					A     =   [
					    # 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":
										A     =   torch.Size([1, 400] )
										A     =   torch.tensor([-0.30_16, -0.77_13, -0.42_05] )
					elif model_name == "timesformer-base-finetuned-k600":
										A     =   torch.Size([1, 600] )
										A     =   torch.tensor([-0.72_67, -0.74_66, 3.24_04] )
					elif model_name == "timesformer-base-finetuned-ssv2":
										A     =   torch.Size([1, 174] )
										A     =   torch.tensor([-0.90_59, 0.64_33, -3.14_57] )
					elif model_name == "timesformer-large-finetuned-k400":
										A     =   torch.Size([1, 400] )
										A     =   torch.tensor([0, 0, 0] )
					elif model_name == "timesformer-large-finetuned-k600":
										A     =   torch.Size([1, 600] )
										A     =   torch.tensor([0, 0, 0] )
					elif model_name == "timesformer-large-finetuned-ssv2":
										A     =   torch.Size([1, 174] )
										A     =   torch.tensor([0, 0, 0] )
					elif model_name == "timesformer-hr-finetuned-k400":
										A     =   torch.Size([1, 400] )
										A     =   torch.tensor([-0.96_17, -3.73_11, -3.77_08] )
					elif model_name == "timesformer-hr-finetuned-k600":
										A     =   torch.Size([1, 600] )
										A     =   torch.tensor([2.52_73, 0.71_27, 1.88_48] )
					elif model_name == "timesformer-hr-finetuned-ssv2":
										A     =   torch.Size([1, 174] )
										A     =   torch.tensor([-3.67_56, -0.75_13, 0.71_80] )
					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]     ,				lowercase__     ,				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(lowercase__ )
										model.save_pretrained(lowercase__ )

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


if __name__ == "__main__":
	__A	:       Union[str, Any]        =						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.'
	)

	__A	:       int        =						parser.parse_args()
	convert_timesformer_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
	)








"""simple docstring"""




import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin





class      __UpperCamelCase					(  _A	,						_A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       UNetaDModel
		SCREAMING_SNAKE_CASE														=       "sample"


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   4
							A     =   1_4
							A     =   1_6

							A     =   floats_tensor((batch_size, num_features, seq_len)).to(__SCREAMING_SNAKE_CASE)
							A     =   torch.tensor([1_0] * batch_size).to(__SCREAMING_SNAKE_CASE)

							return {"sample": noise, "timestep": time_step}


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							return (4, 1_4, 1_6)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							return (4, 1_4, 1_6)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							pass


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							pass


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							super().test_determinism()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_outputs_equivalence()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_from_save_pretrained()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_from_save_pretrained_variant()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_model_from_pretrained()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							super().test_output()


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   {
							    "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
							    "in_channels": 1_4,
							    "out_channels": 1_4,
							    "time_embedding_type": "positional",
							    "use_timestep_embedding": True,
							    "flip_sin_to_cos": False,
							    "freq_shift": 1.0,
							    "out_block_type": "OutConv1DBlock",
							    "mid_block_type": "MidResTemporalBlock1D",
							    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
							    "up_block_types": ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D"),
							    "act_fn": "mish",
							}
							A     =   self.dummy_input
							return init_dict, inputs_dict


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     ,       A     =   UNetaDModel.from_pretrained(
							    "bglick13/hopper-medium-v2-value-function-hor32"	,	output_loading_info=__SCREAMING_SNAKE_CASE	,	subfolder="unet")
							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
							self.assertEqual(len(loading_info["missing_keys"])	,	0)

							model.to(__SCREAMING_SNAKE_CASE)
							A     =   model(**self.dummy_input)

							assert image is not None, "Make sure output is not None"


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     =   UNetaDModel.from_pretrained("bglick13/hopper-medium-v2-value-function-hor32"	,	subfolder="unet")
							torch.manual_seed(0)
							if torch.cuda.is_available():
												torch.cuda.manual_seed_all(0)

							A     =   model.config.in_channels
							A     =   1_6
							A     =   torch.randn((1, seq_len, num_features)).permute(
							    0	,	2	,	1)  # match original, we can update values and remove
							A     =   torch.full((num_features,)	,	0)

							with torch.no_grad():
												A     =   model(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE).sample.permute(0	,	2	,	1)

							A     =   output[0, -3:, -3:].flatten()
							# fmt: off
							A     =   torch.tensor([-2.1_3_7_1_7_2, 1.1_4_2_6_0_1_6, 0.3_6_8_8_6_8_7, -0.7_6_6_9_2_2, 0.7_3_0_3_1_4_6, 0.1_1_0_3_8_8_6_4, -0.4_7_6_0_6_3_3, 0.1_3_2_7_0_1_7_2, 0.0_2_5_9_1_3_4_8])
							# fmt: on
							self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	rtol=1E-3))


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							# Not implemented yet for this UNet
							pass


		@slow
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   "harmonai/maestro-150k"
							A     =   UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE	,	subfolder="unet")
							model.to(__SCREAMING_SNAKE_CASE)

							A     =   6_5_5_3_6
							A     =   torch.sin(torch.arange(__SCREAMING_SNAKE_CASE)[None, None, :].repeat(1	,	2	,	1)).to(__SCREAMING_SNAKE_CASE)
							A     =   torch.tensor([1]).to(__SCREAMING_SNAKE_CASE)

							with torch.no_grad():
												A     =   model(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE).sample

							A     =   output.abs().sum()
							A     =   output.abs().max()

							assert (output_sum - 2_2_4.0_8_9_6).abs() < 0.5
							assert (output_max - 0.0_6_0_7).abs() < 4E-4





class      __UpperCamelCase					(  _A	,						_A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       UNetaDModel
		SCREAMING_SNAKE_CASE														=       "sample"


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   4
							A     =   1_4
							A     =   1_6

							A     =   floats_tensor((batch_size, num_features, seq_len)).to(__SCREAMING_SNAKE_CASE)
							A     =   torch.tensor([1_0] * batch_size).to(__SCREAMING_SNAKE_CASE)

							return {"sample": noise, "timestep": time_step}


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							return (4, 1_4, 1_6)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							return (4, 1_4, 1)


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							super().test_determinism()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							super().test_outputs_equivalence()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							super().test_from_save_pretrained()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							super().test_from_save_pretrained_variant()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							super().test_model_from_pretrained()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							# UNetRL is a value-function is different output shape
							A     ,       A     =   self.prepare_init_args_and_inputs_for_common()
							A     =   self.model_class(**__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()

							with torch.no_grad():
												A     =   model(**__SCREAMING_SNAKE_CASE)

												if isinstance(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE):
																	A     =   output.sample

							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
							A     =   torch.Size((inputs_dict["sample"].shape[0], 1))
							self.assertEqual(output.shape	,	__SCREAMING_SNAKE_CASE	,	"Input and output shapes do not match")


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							pass


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							pass


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   {
							    "in_channels": 1_4,
							    "out_channels": 1_4,
							    "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
							    "up_block_types": [],
							    "out_block_type": "ValueFunction",
							    "mid_block_type": "ValueFunctionMidBlock1D",
							    "block_out_channels": [3_2, 6_4, 1_2_8, 2_5_6],
							    "layers_per_block": 1,
							    "downsample_each_block": True,
							    "use_timestep_embedding": True,
							    "freq_shift": 1.0,
							    "flip_sin_to_cos": False,
							    "time_embedding_type": "positional",
							    "act_fn": "mish",
							}
							A     =   self.dummy_input
							return init_dict, inputs_dict


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							A     ,       A     =   UNetaDModel.from_pretrained(
							    "bglick13/hopper-medium-v2-value-function-hor32"	,	output_loading_info=__SCREAMING_SNAKE_CASE	,	subfolder="value_function")
							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
							self.assertEqual(len(vf_loading_info["missing_keys"])	,	0)

							value_function.to(__SCREAMING_SNAKE_CASE)
							A     =   value_function(**self.dummy_input)

							assert image is not None, "Make sure output is not None"


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     ,       A     =   UNetaDModel.from_pretrained(
							    "bglick13/hopper-medium-v2-value-function-hor32"	,	output_loading_info=__SCREAMING_SNAKE_CASE	,	subfolder="value_function")
							torch.manual_seed(0)
							if torch.cuda.is_available():
												torch.cuda.manual_seed_all(0)

							A     =   value_function.config.in_channels
							A     =   1_4
							A     =   torch.randn((1, seq_len, num_features)).permute(
							    0	,	2	,	1)  # match original, we can update values and remove
							A     =   torch.full((num_features,)	,	0)

							with torch.no_grad():
												A     =   value_function(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE).sample

							# fmt: off
							A     =   torch.tensor([1_6_5.2_5] * seq_len)
							# fmt: on
							self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	rtol=1E-3))


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							# Not implemented yet for this UNet
							pass





