



"""simple docstring"""
import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
A:   int								=		logging.get_logger(__name__)

A:   Union[str, Any]								=		{
    "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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
A:   Optional[int]								=		[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]

def       _snake_case						(						UpperCamelCase						:		str			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		List[str]			,		UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		List[Any]						):
 for attribute in key.split("""."""						):
  UpperCAmelCase	:					Optional[Any]	=	getattr(UpperCamelCase			,		UpperCamelCase						)

 if weight_type is not None:
  UpperCAmelCase	:					Optional[int]	=	getattr(UpperCamelCase			,		UpperCamelCase						).shape
 else:
  UpperCAmelCase	:					Optional[int]	=	hf_pointer.shape

 if hf_shape != value.shape:
  raise ValueError(
      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":
  UpperCAmelCase	:					str	=	value
 elif weight_type == "weight_g":
  UpperCAmelCase	:					int	=	value
 elif weight_type == "weight_v":
  UpperCAmelCase	:					Optional[int]	=	value
 elif weight_type == "bias":
  UpperCAmelCase	:					Optional[int]	=	value
 else:
  UpperCAmelCase	:					Optional[Any]	=	value

 logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."						)

def       _snake_case						(						UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Tuple						):
 UpperCAmelCase	:					Tuple	=	[]
 UpperCAmelCase	:					List[Any]	=	fairseq_model.state_dict()

 UpperCAmelCase	:					Optional[int]	=	hf_model.unispeech_sat.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCAmelCase	:					str	=	False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		hf_model.config.feat_extract_norm == """group"""			,		)
   UpperCAmelCase	:					Dict	=	True
  else:
   for key, mapped_key in MAPPING.items():
    UpperCAmelCase	:					int	=	"""unispeech_sat.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
    if key in name or key.split("""w2v_model."""						)[-1] == name.split("""."""						)[0]:
     if "layer_norm_for_extract" in name and (".".join(name.split("""."""						)[:-1]						) != key):
      # special case since naming is very similar
      continue
     UpperCAmelCase	:					int	=	True
     if "*" in mapped_key:
      UpperCAmelCase	:					List[Any]	=	name.split(UpperCamelCase						)[0].split("""."""						)[-2]
      UpperCAmelCase	:					Tuple	=	mapped_key.replace("""*"""			,		UpperCamelCase						)
     if "weight_g" in name:
      UpperCAmelCase	:					Optional[Any]	=	"""weight_g"""
     elif "weight_v" in name:
      UpperCAmelCase	:					List[str]	=	"""weight_v"""
     elif "bias" in name:
      UpperCAmelCase	:					Any	=	"""bias"""
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      UpperCAmelCase	:					Optional[int]	=	"""weight"""
     else:
      UpperCAmelCase	:					Dict	=	None
     set_recursively(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
    continue
  if not is_used:
   unused_weights.append(UpperCamelCase						)

 logger.warning(F"Unused weights: {unused_weights}"						)

def       _snake_case						(						UpperCamelCase						:		Optional[Any]			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		Optional[Any]			,		UpperCamelCase						:		List[str]			,		UpperCamelCase						:		List[Any]						):
 UpperCAmelCase	:					str	=	full_name.split("""conv_layers."""						)[-1]
 UpperCAmelCase	:					Any	=	name.split("""."""						)
 UpperCAmelCase	:					str	=	int(items[0]						)
 UpperCAmelCase	:					str	=	int(items[1]						)

 if type_id == 0:
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."						)
   UpperCAmelCase	:					List[Any]	=	value
   logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."						)
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."						)
   UpperCAmelCase	:					List[Any]	=	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:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."						)
   UpperCAmelCase	:					int	=	value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."						)
   UpperCAmelCase	:					Dict	=	value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
 else:
  unused_weights.append(UpperCamelCase						)

@torch.no_grad()
def       _snake_case						(						UpperCamelCase						:		List[str]			,		UpperCamelCase						:		int			,		UpperCamelCase						:		Optional[int]=None			,		UpperCamelCase						:		Optional[Any]=None			,		UpperCamelCase						:		Union[str, Any]=True						):
 if config_path is not None:
  UpperCAmelCase	:					Optional[Any]	=	UniSpeechSatConfig.from_pretrained(UpperCamelCase						)
 else:
  UpperCAmelCase	:					Optional[Any]	=	UniSpeechSatConfig()

 UpperCAmelCase	:					str	=	""""""

 if is_finetuned:
  UpperCAmelCase	:					int	=	UniSpeechSatForCTC(UpperCamelCase						)
 else:
  UpperCAmelCase	:					Optional[Any]	=	UniSpeechSatForPreTraining(UpperCamelCase						)

 UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Union[str, Any]	=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]			,		arg_overrides={"""data""": """/""".join(dict_path.split("""/"""						)[:-1]						)}						)
 UpperCAmelCase	:					int	=	model[0].eval()

 recursively_load_weights(UpperCamelCase			,		UpperCamelCase						)

 hf_wavavec.save_pretrained(UpperCamelCase						)


if __name__ == "__main__":
      A:   List[str]								=		argparse.ArgumentParser()
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
      parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
      parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      parser.add_argument(
          "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
      )
      A:   Dict								=		parser.parse_args()
      convert_unispeech_sat_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )

"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, 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 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			List[Any]							=		KandinskyImgaImgPipeline
     _UpperCamelCase							:			Optional[Any]							=		["prompt", "image_embeds", "negative_image_embeds", "image"]
     _UpperCamelCase							:			List[Any]							=		[
         "prompt",
         "negative_prompt",
         "image_embeds",
         "negative_image_embeds",
         "image",
     ]
     _UpperCamelCase							:			Dict							=		[
         "generator",
         "height",
         "width",
         "strength",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     _UpperCamelCase							:			Union[str, Any]							=		False






     @property
     def     __A  (		self  ):
       return 32






     @property
     def     __A  (		self  ):
       return 32






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






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






     @property
     def     __A  (		self  ):
       return 100






     @property
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""  )
       return tokenizer






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       List[Any]  			=     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				,     )

       _lowerCAmelCase						:       int  			=     MultilingualCLIP(a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     text_encoder.eval()

       return text_encoder






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

       _lowerCAmelCase						:       str  			=     {
           """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,
       }

       _lowerCAmelCase						:       Optional[Any]  			=     UNetaDConditionModel(**a__  )
       return model






     @property
     def     __A  (		self  ):
       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     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       str  			=     VQModel(**self.dummy_movq_kwargs  )
       return model






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.dummy_text_encoder
       _lowerCAmelCase						:       List[Any]  			=     self.dummy_tokenizer
       _lowerCAmelCase						:       int  			=     self.dummy_unet
       _lowerCAmelCase						:       Dict  			=     self.dummy_movq

       _lowerCAmelCase						:       Tuple  			=     {
           """num_train_timesteps""": 1000,
           """beta_schedule""": """linear""",
           """beta_start""": 0.0_0_0_8_5,
           """beta_end""": 0.0_1_2,
           """clip_sample""": False,
           """set_alpha_to_one""": False,
           """steps_offset""": 0,
           """prediction_type""": """epsilon""",
           """thresholding""": False,
       }

       _lowerCAmelCase						:       Optional[Any]  			=     DDIMScheduler(**a__  )

       _lowerCAmelCase						:       List[Any]  			=     {
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """unet""": unet,
           """scheduler""": scheduler,
           """movq""": movq,
       }

       return components






     def     __A  (		self				,     a__				,     a__=0  ):
       _lowerCAmelCase						:       Optional[int]  			=     floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(a__  )  ).to(a__  )
       _lowerCAmelCase						:       int  			=     floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(seed + 1  )  ).to(a__  )
       # create init_image
       _lowerCAmelCase						:       List[Any]  			=     floats_tensor((1, 3, 64, 64)				,     rng=random.Random(a__  )  ).to(a__  )
       _lowerCAmelCase						:       int  			=     image.cpu().permute(0				,     2				,     3				,     1  )[0]
       _lowerCAmelCase						:       List[Any]  			=     Image.fromarray(np.uinta(a__  )  ).convert("""RGB"""  ).resize((256, 256)  )

       if str(a__  ).startswith("""mps"""  ):
         _lowerCAmelCase						:       List[Any]  			=     torch.manual_seed(a__  )
       else:
         _lowerCAmelCase						:       Tuple  			=     torch.Generator(device=a__  ).manual_seed(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     {
           """prompt""": """horse""",
           """image""": init_image,
           """image_embeds""": image_embeds,
           """negative_image_embeds""": negative_image_embeds,
           """generator""": generator,
           """height""": 64,
           """width""": 64,
           """num_inference_steps""": 10,
           """guidance_scale""": 7.0,
           """strength""": 0.2,
           """output_type""": """np""",
       }
       return inputs






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     """cpu"""

       _lowerCAmelCase						:       int  			=     self.get_dummy_components()

       _lowerCAmelCase						:       int  			=     self.pipeline_class(**a__  )
       _lowerCAmelCase						:       Optional[int]  			=     pipe.to(a__  )

       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Tuple  			=     pipe(**self.get_dummy_inputs(a__  )  )
       _lowerCAmelCase						:       List[Any]  			=     output.images

       _lowerCAmelCase						:       Tuple  			=     pipe(
           **self.get_dummy_inputs(a__  )				,     return_dict=a__				,     )[0]

       _lowerCAmelCase						:       Dict  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       List[Any]  			=     image_from_tuple[0, -3:, -3:, -1]

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

       _lowerCAmelCase						:       str  			=     np.array(
           [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]  )
       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 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/kandinsky/kandinsky_img2img_frog.npy"""  )

       _lowerCAmelCase						:       List[str]  			=     load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""  )
       _lowerCAmelCase						:       Union[str, Any]  			=     """A red cartoon frog, 4k"""

       _lowerCAmelCase						:       int  			=     KandinskyPriorPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1-prior"""				,     torch_dtype=torch.floataa  )
       pipe_prior.to(a__  )

       _lowerCAmelCase						:       Tuple  			=     KandinskyImgaImgPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1"""				,     torch_dtype=torch.floataa  )
       _lowerCAmelCase						:       Any  			=     pipeline.to(a__  )

       pipeline.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Any  			=     torch.Generator(device="""cpu"""  ).manual_seed(0  )
       _lowerCAmelCase			,	_lowerCAmelCase						:       Dict  			=     pipe_prior(
           a__				,     generator=a__				,     num_inference_steps=5				,     negative_prompt=""""""				,     ).to_tuple()

       _lowerCAmelCase						:       Union[str, Any]  			=     pipeline(
           a__				,     image=a__				,     image_embeds=a__				,     negative_image_embeds=a__				,     generator=a__				,     num_inference_steps=100				,     height=768				,     width=768				,     strength=0.2				,     output_type="""np"""				,     )

       _lowerCAmelCase						:       Dict  			=     output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(a__				,     a__  )




