






import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 						_A( _lowerCamelCase    ,			unittest.TestCase       ):


				"""simple docstring"""


				UpperCamelCase   :  int    =     KandinskyVaaPriorEmbaEmbPipeline
				UpperCamelCase   :  Any    =     ['''prompt''', '''image''']
				UpperCamelCase   :  Tuple    =     ['''prompt''', '''image''']
				UpperCamelCase   :  Optional[int]    =     [
				    '''num_images_per_prompt''',
				    '''strength''',
				    '''generator''',
				    '''num_inference_steps''',
				    '''latents''',
				    '''negative_prompt''',
				    '''guidance_scale''',
				    '''output_type''',
				    '''return_dict''',
				]
				UpperCamelCase   :  List[Any]    =     False



				@property
				def  UpperCAmelCase_		(      self					):
									return 32



				@property
				def  UpperCAmelCase_		(      self					):
									return 32



				@property
				def  UpperCAmelCase_		(      self					):
									return self.time_input_dim



				@property
				def  UpperCAmelCase_		(      self					):
									return self.time_input_dim * 4



				@property
				def  UpperCAmelCase_		(      self					):
									return 100



				@property
				def  UpperCAmelCase_		(      self					):
									__A     :						Dict          =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)
									return tokenizer



				@property
				def  UpperCAmelCase_		(      self					):
									torch.manual_seed(0					)
									__A     :						Tuple          =    CLIPTextConfig(
									    bos_token_id=0					, eos_token_id=2					, hidden_size=self.text_embedder_hidden_size					, projection_dim=self.text_embedder_hidden_size					, intermediate_size=37					, layer_norm_eps=1e-0_5					, num_attention_heads=4					, num_hidden_layers=5					, pad_token_id=1					, vocab_size=1000					, )
									return CLIPTextModelWithProjection(__UpperCamelCase					)



				@property
				def  UpperCAmelCase_		(      self					):
									torch.manual_seed(0					)

									__A     :						List[str]          =    {
									    'num_attention_heads': 2,
									    'attention_head_dim': 12,
									    'embedding_dim': self.text_embedder_hidden_size,
									    'num_layers': 1,
									}

									__A     :						str          =    PriorTransformer(**__UpperCamelCase					)
									# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
									__A     :						str          =    nn.Parameter(torch.ones(model.clip_std.shape					)					)
									return model



				@property
				def  UpperCAmelCase_		(      self					):
									torch.manual_seed(0					)
									__A     :						str          =    CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size					, image_size=224					, projection_dim=self.text_embedder_hidden_size					, intermediate_size=37					, num_attention_heads=4					, num_channels=3					, num_hidden_layers=5					, patch_size=14					, )

									__A     :						Union[str, Any]          =    CLIPVisionModelWithProjection(__UpperCamelCase					)
									return model



				@property
				def  UpperCAmelCase_		(      self					):
									__A     :						Optional[Any]          =    CLIPImageProcessor(
									    crop_size=224					, do_center_crop=__UpperCamelCase					, do_normalize=__UpperCamelCase					, do_resize=__UpperCamelCase					, image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]					, image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]					, resample=3					, size=224					, )

									return image_processor



				def  UpperCAmelCase_		(      self					):
									__A     :						Dict          =    self.dummy_prior
									__A     :						Tuple          =    self.dummy_image_encoder
									__A     :						List[str]          =    self.dummy_text_encoder
									__A     :						Optional[Any]          =    self.dummy_tokenizer
									__A     :						List[str]          =    self.dummy_image_processor

									__A     :						List[str]          =    UnCLIPScheduler(
									    variance_type='fixed_small_log'					, prediction_type='sample'					, num_train_timesteps=1000					, clip_sample=__UpperCamelCase					, clip_sample_range=10.0					, )

									__A     :						List[str]          =    {
									    'prior': prior,
									    'image_encoder': image_encoder,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'scheduler': scheduler,
									    'image_processor': image_processor,
									}

									return components



				def  UpperCAmelCase_		(      self					, _A					, _A=0					):







									if str(__UpperCamelCase					).startswith('mps'					):
														__A     :						str          =    torch.manual_seed(__UpperCamelCase					)
									else:
														__A     :						Optional[Any]          =    torch.Generator(device=__UpperCamelCase					).manual_seed(__UpperCamelCase					)

									__A     :						Optional[int]          =    floats_tensor((1, 3, 64, 64)					, rng=random.Random(__UpperCamelCase					)					).to(__UpperCamelCase					)
									__A     :						Tuple          =    image.cpu().permute(0					, 2					, 3					, 1					)[0]
									__A     :						int          =    Image.fromarray(np.uinta(__UpperCamelCase					)					).convert('RGB'					).resize((256, 256)					)

									__A     :						str          =    {
									    'prompt': 'horse',
									    'image': init_image,
									    'strength': 0.5,
									    'generator': generator,
									    'guidance_scale': 4.0,
									    'num_inference_steps': 2,
									    'output_type': 'np',
									}
									return inputs



				def  UpperCAmelCase_		(      self					):
									__A     :						List[str]          =    'cpu'

									__A     :						Dict          =    self.get_dummy_components()

									__A     :						Union[str, Any]          =    self.pipeline_class(**__UpperCamelCase					)
									__A     :						List[str]          =    pipe.to(__UpperCamelCase					)

									pipe.set_progress_bar_config(disable=__UpperCamelCase					)

									__A     :						str          =    pipe(**self.get_dummy_inputs(__UpperCamelCase					)					)
									__A     :						Any          =    output.image_embeds

									__A     :						str          =    pipe(
									    **self.get_dummy_inputs(__UpperCamelCase					)					, return_dict=__UpperCamelCase					, )[0]

									__A     :						List[str]          =    image[0, -10:]
									__A     :						Optional[int]          =    image_from_tuple[0, -10:]

									assert image.shape == (1, 32)

									__A     :						List[str]          =    np.array(
									    [
									        0.1_0_7_1_2_8_4,
									        1.3_3_3_0_2_7_1,
									        0.6_1_2_6_0_2_2_3,
									        -0.6_6_9_1_0_6_5,
									        -0.3_8_4_6_8_5_2,
									        -1.0_3_0_3_6_6_1,
									        0.2_2_7_1_6_1_1_1,
									        0.0_3_3_4_8_9_0_1,
									        0.3_0_0_4_0_6_7_5,
									        -0.2_4_8_0_5_0_2_9,
									    ]					)

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



				@skip_mps
				def  UpperCAmelCase_		(      self					):
									__A     :						Optional[Any]          =    torch_device == 'cpu'
									__A     :						Optional[int]          =    True
									__A     :						Any          =    False

									self._test_inference_batch_single_identical(
									    test_max_difference=__UpperCamelCase					, relax_max_difference=__UpperCamelCase					, test_mean_pixel_difference=__UpperCamelCase					, )





				@skip_mps
				def  UpperCAmelCase_		(      self					):
									__A     :						List[Any]          =    torch_device == 'cpu'
									__A     :						Union[str, Any]          =    False

									self._test_attention_slicing_forward_pass(
									    test_max_difference=__UpperCamelCase					, test_mean_pixel_difference=__UpperCamelCase					, )











import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase						:       Tuple        =					logging.get_logger(__name__)

UpperCAmelCase						:       List[Any]        =					['''UtteranceLevel''']







@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a ,						a  )				->				List[str]:
					__A     :						Any          =    torch.load(a ,						map_location='cpu'  )
					if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
										raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""  )

					__A     :						Dict          =    checkpoint['Downstream']

					__A     :						Optional[Any]          =    HubertConfig.from_pretrained(a  )
					__A     :						Tuple          =    HubertForSequenceClassification.from_pretrained(a ,						config=a  )
					__A     :						str          =    WavaVecaFeatureExtractor.from_pretrained(
					    a ,						return_attention_mask=a ,						do_normalize=a  )

					if hf_congfig.use_weighted_layer_sum:
										__A     :						Optional[Any]          =    checkpoint['Featurizer']['weights']

					__A     :						Dict          =    downstream_dict['projector.weight']
					__A     :						int          =    downstream_dict['projector.bias']
					__A     :						List[Any]          =    downstream_dict['model.post_net.linear.weight']
					__A     :						int          =    downstream_dict['model.post_net.linear.bias']

					hf_feature_extractor.save_pretrained(a  )
					hf_model.save_pretrained(a  )


if __name__ == "__main__":
				UpperCAmelCase						:       Dict        =					argparse.ArgumentParser()
				parser.add_argument(
				    '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
				)
				parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
				parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
				parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
				UpperCAmelCase						:       Dict        =					parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




