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 			__snake_case  (      _lowerCamelCase   ,unittest.TestCase     ):
					__lowerCamelCase	 =       KandinskyVaaPriorEmbaEmbPipeline
					__lowerCamelCase	 =       ["""prompt""", """image"""]
					__lowerCamelCase	 =       ["""prompt""", """image"""]
					__lowerCamelCase	 =       [
					    """num_images_per_prompt""",
					    """strength""",
					    """generator""",
					    """num_inference_steps""",
					    """latents""",
					    """negative_prompt""",
					    """guidance_scale""",
					    """output_type""",
					    """return_dict""",
					]
					__lowerCamelCase	 =       False




					@property
					def       __a      (       self		)					->  Optional[Any]:



									'''simple docstring'''







									return 32




					@property
					def       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									return 32




					@property
					def       __a      (       self		)					->  int:



									'''simple docstring'''







									return self.time_input_dim




					@property
					def       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







									return self.time_input_dim * 4




					@property
					def       __a      (       self		)					->  Any:



									'''simple docstring'''







									return 100




					@property
					def       __a      (       self		)					->  str:



									'''simple docstring'''







									snake_case__ :     Dict 							=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)
									return tokenizer




					@property
					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									torch.manual_seed(0		)
									snake_case__ :     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-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1000   ,	)
									return CLIPTextModelWithProjection(__UpperCamelCase		)




					@property
					def       __a      (       self		)					->  Union[str, Any]:



									'''simple docstring'''







									torch.manual_seed(0		)

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

									snake_case__ :     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
									snake_case__ :     str 							=				nn.Parameter(torch.ones(model.clip_std.shape		)		)
									return model




					@property
					def       __a      (       self		)					->  int:



									'''simple docstring'''







									torch.manual_seed(0		)
									snake_case__ :     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   ,	)

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




					@property
					def       __a      (       self		)					->  int:



									'''simple docstring'''







									snake_case__ :     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       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__ :     Dict 							=				self.dummy_prior
									snake_case__ :     Tuple 							=				self.dummy_image_encoder
									snake_case__ :     List[str] 							=				self.dummy_text_encoder
									snake_case__ :     Optional[Any] 							=				self.dummy_tokenizer
									snake_case__ :     List[str] 							=				self.dummy_image_processor

									snake_case__ :     List[str] 							=				UnCLIPScheduler(
									    variance_type='fixed_small_log'   ,	prediction_type='sample'   ,	num_train_timesteps=1000   ,	clip_sample=__UpperCamelCase   ,	clip_sample_range=1_0.0   ,	)

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

									return components




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase=0		)					->  Any:



									'''simple docstring'''







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

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

									snake_case__ :     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       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__ :     List[str] 							=				'cpu'

									snake_case__ :     Dict 							=				self.get_dummy_components()

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

									pipe.set_progress_bar_config(disable=__UpperCamelCase		)

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

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

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

									assert image.shape == (1, 32)

									snake_case__ :     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       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







									snake_case__ :     Optional[Any] 							=				torch_device == 'cpu'
									snake_case__ :     Optional[int] 							=				True
									snake_case__ :     Any 							=				False

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




					@skip_mps
					def       __a      (       self		)					->  Union[str, Any]:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				torch_device == 'cpu'
									snake_case__ :     Union[str, Any] 							=				False

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



import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCAmelCase__					:       Any		=       logging.get_logger(__name__)

lowerCAmelCase__					:       int		=       {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__      ,			A__      ,			A__   )       -> Optional[int]:
				for attribute in key.split('.'   ):
								snake_case__ :     Optional[Any] 							=				getattr(A__      ,			A__   )

				if weight_type is not None:
								snake_case__ :     Any 							=				getattr(A__      ,			A__   ).shape
				else:
								snake_case__ :     str 							=				hf_pointer.shape

				assert hf_shape == value.shape, (
				    F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
				    F""" {value.shape} for {full_name}"""
				)

				if weight_type == "weight":
								snake_case__ :     List[str] 							=				value
				elif weight_type == "weight_g":
								snake_case__ :     str 							=				value
				elif weight_type == "weight_v":
								snake_case__ :     List[str] 							=				value
				elif weight_type == "bias":
								snake_case__ :     List[Any] 							=				value
				else:
								snake_case__ :     Union[str, Any] 							=				value

				logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""   )
def 			UpperCamelCase__						(    A__      ,			A__   )       -> List[str]:
				snake_case__ :     Tuple 							=				[]
				snake_case__ :     int 							=				fairseq_model.state_dict()

				snake_case__ :     int 							=				hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								snake_case__ :     Union[str, Any] 							=				False
								if "conv_layers" in name:
												load_conv_layer(
												    A__      ,			A__      ,			A__      ,			A__      ,			hf_model.config.feat_extract_norm == 'group'      ,			)
												snake_case__ :     Tuple 							=				True
								else:
												for key, mapped_key in MAPPING.items():
																snake_case__ :     Dict 							=				mapped_key

																if key in name:
																				snake_case__ :     int 							=				True
																				if "*" in mapped_key:
																								snake_case__ :     List[str] 							=				name.split(A__   )[0].split('.'   )[-2]
																								snake_case__ :     Dict 							=				mapped_key.replace('*'      ,			A__   )
																				if "weight_g" in name:
																								snake_case__ :     int 							=				'weight_g'
																				elif "weight_v" in name:
																								snake_case__ :     int 							=				'weight_v'
																				elif "weight" in name:
																								snake_case__ :     Any 							=				'weight'
																				elif "bias" in name:
																								snake_case__ :     Tuple 							=				'bias'
																				else:
																								snake_case__ :     int 							=				None
																				set_recursively(A__      ,			A__      ,			A__      ,			A__      ,			A__   )
																continue
								if not is_used:
												unused_weights.append(A__   )

				logger.warning(F"""Unused weights: {unused_weights}"""   )
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__      ,			A__      ,			A__   )       -> Tuple:
				snake_case__ :     Optional[int] 							=				full_name.split('conv_layers.'   )[-1]
				snake_case__ :     Dict 							=				name.split('.'   )
				snake_case__ :     str 							=				int(items[0]   )
				snake_case__ :     Optional[Any] 							=				int(items[1]   )

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
												)
												snake_case__ :     List[Any] 							=				value
												logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
												)
												snake_case__ :     Optional[int] 							=				value
												logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
												    " found."
												)
												snake_case__ :     Optional[int] 							=				value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
												)
												snake_case__ :     Union[str, Any] 							=				value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
				else:
								unused_weights.append(A__   )
def 			UpperCamelCase__						(    A__   )       -> Union[str, Any]:
				snake_case__ :     Optional[Any] 							=				HubertConfig()
				snake_case__ :     Dict 							=				model.config

				snake_case__ :     Optional[Any] 							=				fs_config.activation_dropout
				snake_case__ :     Union[str, Any] 							=				False
				snake_case__ :     Tuple 							=				fs_config.attention_dropout
				snake_case__ :     List[str] 							=				False
				snake_case__ :     Optional[int] 							=				eval(fs_config.extractor_conv_feature_layers   )
				snake_case__ :     Optional[int] 							=				[x[0] for x in conv_layers]
				snake_case__ :     Union[str, Any] 							=				[x[1] for x in conv_layers]
				snake_case__ :     Dict 							=				[x[2] for x in conv_layers]
				snake_case__ :     int 							=				'gelu'
				snake_case__ :     Dict 							=				'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
				snake_case__ :     Any 							=				False
				snake_case__ :     List[Any] 							=				0.0
				snake_case__ :     Any 							=				0.0
				snake_case__ :     Union[str, Any] 							=				fs_config.activation_fn
				snake_case__ :     List[str] 							=				fs_config.dropout
				snake_case__ :     Dict 							=				fs_config.encoder_embed_dim
				snake_case__ :     Any 							=				0.0_2
				snake_case__ :     Union[str, Any] 							=				fs_config.encoder_ffn_embed_dim
				snake_case__ :     List[Any] 							=				1e-5
				snake_case__ :     Union[str, Any] 							=				0.0
				snake_case__ :     int 							=				fs_config.encoder_attention_heads
				snake_case__ :     Any 							=				fs_config.conv_pos_groups
				snake_case__ :     int 							=				fs_config.conv_pos
				snake_case__ :     Any 							=				len(A__   )
				snake_case__ :     str 							=				fs_config.encoder_layers

				return config
@torch.no_grad()
def 			UpperCamelCase__						(    A__      ,			A__=None   )       -> str:
				snake_case__ :     int 							=				distilhubert().model.model

				if config_path is not None:
								snake_case__ :     Optional[int] 							=				HubertConfig.from_pretrained(A__   )
				else:
								snake_case__ :     int 							=				convert_config(A__   )
				snake_case__ :     Dict 							=				model.eval()

				snake_case__ :     int 							=				WavaVecaFeatureExtractor(
				    feature_size=1      ,			sampling_rate=1_6000      ,			padding_value=0      ,			do_normalize=A__      ,			return_attention_mask=A__      ,			)
				snake_case__ :     Optional[int] 							=				HubertModel(A__   )

				recursively_load_weights(A__      ,			A__   )

				feature_extractor.save_pretrained(A__   )
				hf_model.save_pretrained(A__   )


if __name__ == "__main__":
				lowerCAmelCase__					:       Union[str, Any]		=       argparse.ArgumentParser()
				parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
				parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
				lowerCAmelCase__					:       List[Any]		=       parser.parse_args()
				convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)


