

"""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
	)









import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 					snake_case__(				UpperCAmelCase__,    UpperCAmelCase__,    unittest.TestCase				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	str       =				StableDiffusionXLPipeline
						SCREAMING_SNAKE_CASE__   :	Dict       =				TEXT_TO_IMAGE_PARAMS
						SCREAMING_SNAKE_CASE__   :	Optional[int]       =				TEXT_TO_IMAGE_BATCH_PARAMS
						SCREAMING_SNAKE_CASE__   :	Optional[Any]       =				TEXT_TO_IMAGE_IMAGE_PARAMS
						SCREAMING_SNAKE_CASE__   :	int       =				TEXT_TO_IMAGE_IMAGE_PARAMS




						def     lowercase_			(					self				)       ->     int:
										torch.manual_seed(0				)
										lowerCAmelCase_			:   Tuple     					=					UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4) ,    layers_per_block=2 ,    sample_size=3_2 ,    in_channels=4 ,    out_channels=4 ,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,    attention_head_dim=(2, 4) ,    use_linear_projection=__lowercase ,    addition_embed_type='''text_time''' ,    addition_time_embed_dim=8 ,    transformer_layers_per_block=(1, 2) ,    projection_class_embeddings_input_dim=8_0 ,    cross_attention_dim=6_4 ,    )
										lowerCAmelCase_			:   Any     					=					EulerDiscreteScheduler(
										    beta_start=0.0_00_85 ,    beta_end=0.0_12 ,    steps_offset=1 ,    beta_schedule='''scaled_linear''' ,    timestep_spacing='''leading''' ,    )
										torch.manual_seed(0				)
										lowerCAmelCase_			:   Any     					=					AutoencoderKL(
										    block_out_channels=[3_2, 6_4] ,    in_channels=3 ,    out_channels=3 ,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,    latent_channels=4 ,    sample_size=1_2_8 ,    )
										torch.manual_seed(0				)
										lowerCAmelCase_			:   Dict     					=					CLIPTextConfig(
										    bos_token_id=0 ,    eos_token_id=2 ,    hidden_size=3_2 ,    intermediate_size=3_7 ,    layer_norm_eps=1e-05 ,    num_attention_heads=4 ,    num_hidden_layers=5 ,    pad_token_id=1 ,    vocab_size=1_0_0_0 ,    hidden_act='''gelu''' ,    projection_dim=3_2 ,    )
										lowerCAmelCase_			:   str     					=					CLIPTextModel(__lowercase				)
										lowerCAmelCase_			:   List[str]     					=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ,    local_files_only=__lowercase				)

										lowerCAmelCase_			:   Tuple     					=					CLIPTextModelWithProjection(__lowercase				)
										lowerCAmelCase_			:   Optional[Any]     					=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ,    local_files_only=__lowercase				)

										lowerCAmelCase_			:   int     					=					{
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''text_encoder_2''': text_encoder_a,
										    '''tokenizer_2''': tokenizer_a,
										    # "safety_checker": None,
										    # "feature_extractor": None,
										}
										return components




						def     lowercase_			(					self ,    __lowercase ,    __lowercase=0				)       ->     Dict:
										if str(__lowercase				).startswith('''mps'''				):
														lowerCAmelCase_			:   Dict     					=					torch.manual_seed(__lowercase				)
										else:
														lowerCAmelCase_			:   Dict     					=					torch.Generator(device=__lowercase				).manual_seed(__lowercase				)
										lowerCAmelCase_			:   Dict     					=					{
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 5.0,
										    '''output_type''': '''numpy''',
										}
										return inputs




						def     lowercase_			(					self				)       ->     Optional[Any]:
										lowerCAmelCase_			:   Union[str, Any]     					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowerCAmelCase_			:   Any     					=					self.get_dummy_components()
										lowerCAmelCase_			:   Optional[int]     					=					StableDiffusionXLPipeline(**__lowercase				)
										lowerCAmelCase_			:   Optional[int]     					=					sd_pipe.to(__lowercase				)
										sd_pipe.set_progress_bar_config(disable=__lowercase				)

										lowerCAmelCase_			:   Dict     					=					self.get_dummy_inputs(__lowercase				)
										lowerCAmelCase_			:   str     					=					sd_pipe(**__lowercase				).images
										lowerCAmelCase_			:   Any     					=					image[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										lowerCAmelCase_			:   str     					=					np.array([0.58_73, 0.61_28, 0.47_97, 0.51_22, 0.56_74, 0.46_39, 0.52_27, 0.51_49, 0.47_47]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2




						def     lowercase_			(					self				)       ->     List[Any]:
										lowerCAmelCase_			:   Any     					=					self.get_dummy_components()
										lowerCAmelCase_			:   List[Any]     					=					StableDiffusionXLPipeline(**__lowercase				)
										lowerCAmelCase_			:   List[Any]     					=					sd_pipe.to(__lowercase				)
										lowerCAmelCase_			:   int     					=					sd_pipe.to(__lowercase				)
										sd_pipe.set_progress_bar_config(disable=__lowercase				)

										# forward without prompt embeds
										lowerCAmelCase_			:   Optional[Any]     					=					self.get_dummy_inputs(__lowercase				)
										lowerCAmelCase_			:   List[str]     					=					3 * ['''this is a negative prompt''']
										lowerCAmelCase_			:   Union[str, Any]     					=					negative_prompt
										lowerCAmelCase_			:   Union[str, Any]     					=					3 * [inputs['''prompt''']]

										lowerCAmelCase_			:   str     					=					sd_pipe(**__lowercase				)
										lowerCAmelCase_			:   Any     					=					output.images[0, -3:, -3:, -1]

										# forward with prompt embeds
										lowerCAmelCase_			:   Tuple     					=					self.get_dummy_inputs(__lowercase				)
										lowerCAmelCase_			:   Dict     					=					3 * ['''this is a negative prompt''']
										lowerCAmelCase_			:   Dict     					=					3 * [inputs.pop('''prompt'''				)]

										(
										    (
										    lowerCAmelCase_
										)					,	(
										    lowerCAmelCase_
										)					,	(
										    lowerCAmelCase_
										)					,	(
										    lowerCAmelCase_
										)					,	
										)			:   str     					=					sd_pipe.encode_prompt(__lowercase ,    negative_prompt=__lowercase				)

										lowerCAmelCase_			:   List[Any]     					=					sd_pipe(
										    **__lowercase ,    prompt_embeds=__lowercase ,    negative_prompt_embeds=__lowercase ,    pooled_prompt_embeds=__lowercase ,    negative_pooled_prompt_embeds=__lowercase ,    )
										lowerCAmelCase_			:   int     					=					output.images[0, -3:, -3:, -1]

										# make sure that it's equal
										assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()				).max() < 1e-4




						def     lowercase_			(					self				)       ->     List[str]:
										super().test_attention_slicing_forward_pass(expected_max_diff=3e-3				)




						def     lowercase_			(					self				)       ->     Tuple:
										super().test_inference_batch_single_identical(expected_max_diff=3e-3				)







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







						'''simple docstring'''




						def     lowercase_			(					self				)       ->     int:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




						def     lowercase_			(					self ,    __lowercase ,    __lowercase="cpu" ,    __lowercase=torch.floataa ,    __lowercase=0				)       ->     Dict:
										lowerCAmelCase_			:   Any     					=					torch.Generator(device=__lowercase				).manual_seed(__lowercase				)
										lowerCAmelCase_			:   str     					=					np.random.RandomState(__lowercase				).standard_normal((1, 4, 6_4, 6_4)				)
										lowerCAmelCase_			:   List[str]     					=					torch.from_numpy(__lowercase				).to(device=__lowercase ,    dtype=__lowercase				)
										lowerCAmelCase_			:   Optional[Any]     					=					{
										    '''prompt''': '''a photograph of an astronaut riding a horse''',
										    '''latents''': latents,
										    '''generator''': generator,
										    '''num_inference_steps''': 3,
										    '''guidance_scale''': 7.5,
										    '''output_type''': '''numpy''',
										}
										return inputs




						def     lowercase_			(					self				)       ->     List[str]:
										lowerCAmelCase_			:   Optional[int]     					=					DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''				)
										pipe.to(__lowercase				)
										pipe.set_progress_bar_config(disable=__lowercase				)

										lowerCAmelCase_			:   Any     					=					self.get_inputs(__lowercase				)
										lowerCAmelCase_			:   List[str]     					=					pipe(**__lowercase				).images
										lowerCAmelCase_			:   Dict     					=					image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										lowerCAmelCase_			:   Optional[int]     					=					np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]				)
										assert np.abs(image_slice - expected_slice				).max() < 7e-3