



"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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  _UpperCAmelCase (    lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    List[Any]	      =	KandinskyVaaControlnetImgaImgPipeline
 _lowerCAmelCase     :    List[str]	      =	["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
 _lowerCAmelCase     :    str	      =	["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
 _lowerCAmelCase     :    List[str]	      =	[
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 _lowerCAmelCase     :    int	      =	False

 @property
 def 		_snake_case      (  self	: Tuple       ):
     return 32

 @property
 def 		_snake_case      (  self	: Union[str, Any]       ):
     return 32

 @property
 def 		_snake_case      (  self	: str       ):
     return self.time_input_dim

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

 @property
 def 		_snake_case      (  self	: Optional[Any]       ):
     return 100

 @property
 def 		_snake_case      (  self	: Optional[int]       ):
     torch.manual_seed(0       )

     snake_case_				:  Any       =			{
         '''in_channels''': 8,
         # Out channels is double in channels because predicts mean and variance
         '''out_channels''': 8,
         '''addition_embed_type''': '''image_hint''',
         '''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''': '''image_proj''',
         '''cross_attention_dim''': self.cross_attention_dim,
         '''attention_head_dim''': 4,
         '''resnet_time_scale_shift''': '''scale_shift''',
         '''class_embed_type''': None,
     }

     snake_case_				:  Tuple       =			UNetaDConditionModel(**lowercase_       )
     return model

 @property
 def 		_snake_case      (  self	: Optional[Any]       ):
     return {
         "block_out_channels": [32, 32, 64, 64],
         "down_block_types": [
             "DownEncoderBlock2D",
             "DownEncoderBlock2D",
             "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
         "vq_embed_dim": 4,
     }

 @property
 def 		_snake_case      (  self	: Any       ):
     torch.manual_seed(0       )
     snake_case_				:  List[str]       =			VQModel(**self.dummy_movq_kwargs       )
     return model

 def 		_snake_case      (  self	: Any       ):
     snake_case_				:  Dict       =			self.dummy_unet
     snake_case_				:  Union[str, Any]       =			self.dummy_movq

     snake_case_				:  Optional[int]       =			{
         '''num_train_timesteps''': 1000,
         '''beta_schedule''': '''linear''',
         '''beta_start''': 0.0_00_85,
         '''beta_end''': 0.0_12,
         '''clip_sample''': False,
         '''set_alpha_to_one''': False,
         '''steps_offset''': 0,
         '''prediction_type''': '''epsilon''',
         '''thresholding''': False,
     }

     snake_case_				:  Dict       =			DDIMScheduler(**lowercase_       )

     snake_case_				:  Union[str, Any]       =			{
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''movq''': movq,
     }

     return components

 def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: Dict						,  lowercase_	: int=0       ):
     snake_case_				:  int       =			floats_tensor((1, self.text_embedder_hidden_size)						,  rng=random.Random(lowercase_       )       ).to(lowercase_       )
     snake_case_				:  str       =			floats_tensor((1, self.text_embedder_hidden_size)						,  rng=random.Random(seed + 1       )       ).to(
         lowercase_       )
     # create init_image
     snake_case_				:  List[Any]       =			floats_tensor((1, 3, 64, 64)						,  rng=random.Random(lowercase_       )       ).to(lowercase_       )
     snake_case_				:  Optional[Any]       =			image.cpu().permute(0						,  2						,  3						,  1       )[0]
     snake_case_				:  List[Any]       =			Image.fromarray(np.uinta(lowercase_       )       ).convert('''RGB'''       ).resize((256, 256)       )
     # create hint
     snake_case_				:  List[Any]       =			floats_tensor((1, 3, 64, 64)						,  rng=random.Random(lowercase_       )       ).to(lowercase_       )

     if str(lowercase_       ).startswith('''mps'''       ):
         snake_case_				:  int       =			torch.manual_seed(lowercase_       )
     else:
         snake_case_				:  List[str]       =			torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )
     snake_case_				:  List[str]       =			{
         '''image''': init_image,
         '''image_embeds''': image_embeds,
         '''negative_image_embeds''': negative_image_embeds,
         '''hint''': hint,
         '''generator''': generator,
         '''height''': 64,
         '''width''': 64,
         '''num_inference_steps''': 10,
         '''guidance_scale''': 7.0,
         '''strength''': 0.2,
         '''output_type''': '''np''',
     }
     return inputs






 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  Dict       =			'''cpu'''

     snake_case_				:  int       =			self.get_dummy_components()

     snake_case_				:  int       =			self.pipeline_class(**lowercase_       )
     snake_case_				:  List[Any]       =			pipe.to(lowercase_       )

     pipe.set_progress_bar_config(disable=lowercase_       )

     snake_case_				:  List[Any]       =			pipe(**self.get_dummy_inputs(lowercase_       )       )
     snake_case_				:  List[str]       =			output.images

     snake_case_				:  str       =			pipe(
         **self.get_dummy_inputs(lowercase_       )						,  return_dict=lowercase_						,  )[0]

     snake_case_				:  int       =			image[0, -3:, -3:, -1]
     snake_case_				:  Any       =			image_from_tuple[0, -3:, -3:, -1]

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

     snake_case_				:  Union[str, Any]       =			np.array(
         [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]       )
     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  _UpperCAmelCase (    unittest.TestCase):

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






 def 		_snake_case      (  self	: Optional[int]       ):
     snake_case_				:  Any       =			load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''       )

     snake_case_				:  Union[str, Any]       =			load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''       )
     snake_case_				:  Union[str, Any]       =			init_image.resize((512, 512)       )

     snake_case_				:  Optional[Any]       =			load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/kandinskyv22/hint_image_cat.png'''       )
     snake_case_				:  Tuple       =			torch.from_numpy(np.array(lowercase_       )       ).float() / 2_55.0
     snake_case_				:  Optional[int]       =			hint.permute(2						,  0						,  1       ).unsqueeze(0       )

     snake_case_				:  Any       =			'''A robot, 4k photo'''

     snake_case_				:  Union[str, Any]       =			KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-2-prior'''						,  torch_dtype=torch.floataa       )
     pipe_prior.to(lowercase_       )

     snake_case_				:  Optional[int]       =			KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-2-controlnet-depth'''						,  torch_dtype=torch.floataa       )
     snake_case_				:  Optional[int]       =			pipeline.to(lowercase_       )

     pipeline.set_progress_bar_config(disable=lowercase_       )

     snake_case_				:  Optional[int]       =			torch.Generator(device='''cpu'''       ).manual_seed(0       )

     snake_case_,			snake_case_				:  int       =			pipe_prior(
         lowercase_						,  image=lowercase_						,  strength=0.85						,  generator=lowercase_						,  negative_prompt=''''''						,  ).to_tuple()

     snake_case_				:  int       =			pipeline(
         image=lowercase_						,  image_embeds=lowercase_						,  negative_image_embeds=lowercase_						,  hint=lowercase_						,  generator=lowercase_						,  num_inference_steps=100						,  height=512						,  width=512						,  strength=0.5						,  output_type='''np'''						,  )

     snake_case_				:  Dict       =			output.images[0]

     assert image.shape == (512, 512, 3)

     assert_mean_pixel_difference(lowercase_						,  lowercase_       )










"""simple docstring"""

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowercase__					:						Dict												=			logging.get_logger(__name__)

lowercase__					:						List[str]												=			{
    '''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''',
}
lowercase__					:						Dict												=			[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a					):
    for attribute in key.split('''.'''					):
        snake_case_				:  Optional[Any]       =			getattr(_a	,    _a					)

    if weight_type is not None:
        snake_case_				:  Optional[int]       =			getattr(_a	,    _a					).shape
    else:
        snake_case_				:  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":
        snake_case_				:  Dict       =			value
    elif weight_type == "weight_g":
        snake_case_				:  Tuple       =			value
    elif weight_type == "weight_v":
        snake_case_				:  Tuple       =			value
    elif weight_type == "bias":
        snake_case_				:  int       =			value
    else:
        snake_case_				:  Optional[Any]       =			value

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




def __lowercase    (							_a	,    _a					):
    snake_case_				:  Optional[int]       =			[]
    snake_case_				:  Optional[Any]       =			fairseq_model.state_dict()

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

    for name, value in fairseq_dict.items():
        snake_case_				:  int       =			False
        if "conv_layers" in name:
            load_conv_layer(
                _a	,    _a	,    _a	,    _a	,    hf_model.config.feat_extract_norm == '''group'''	,    )
            snake_case_				:  Any       =			True
        else:
            for key, mapped_key in MAPPING.items():
                snake_case_				:  Tuple       =			'''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
                    snake_case_				:  List[str]       =			True
                    if "*" in mapped_key:
                        snake_case_				:  List[Any]       =			name.split(_a					)[0].split('''.'''					)[-2]
                        snake_case_				:  Dict       =			mapped_key.replace('''*'''	,    _a					)
                    if "weight_g" in name:
                        snake_case_				:  Any       =			'''weight_g'''
                    elif "weight_v" in name:
                        snake_case_				:  List[str]       =			'''weight_v'''
                    elif "bias" in name:
                        snake_case_				:  List[str]       =			'''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        snake_case_				:  Optional[int]       =			'''weight'''
                    else:
                        snake_case_				:  List[Any]       =			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 __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			full_name.split('''conv_layers.'''					)[-1]
    snake_case_				:  int       =			name.split('''.'''					)
    snake_case_				:  int       =			int(items[0]					)
    snake_case_				:  int       =			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."					)
            snake_case_				:  int       =			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."					)
            snake_case_				:  str       =			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."					)
            snake_case_				:  Dict       =			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."					)
            snake_case_				:  Optional[Any]       =			value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
    else:
        unused_weights.append(_a					)




@torch.no_grad()
def __lowercase    (							_a	,    _a	,    _a=None	,    _a=None	,    _a=True					):
    if config_path is not None:
        snake_case_				:  int       =			UniSpeechSatConfig.from_pretrained(_a					)
    else:
        snake_case_				:  Tuple       =			UniSpeechSatConfig()

    snake_case_				:  List[Any]       =			''''''

    if is_finetuned:
        snake_case_				:  str       =			UniSpeechSatForCTC(_a					)
    else:
        snake_case_				:  Dict       =			UniSpeechSatForPreTraining(_a					)

    snake_case_,			snake_case_,			snake_case_				:  str       =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]	,    arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''					)[:-1]					)}					)
    snake_case_				:  Dict       =			model[0].eval()

    recursively_load_weights(_a	,    _a					)

    hf_wavavec.save_pretrained(_a					)


if __name__ == "__main__":
     lowercase__					:						List[Any]												=			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'''
     )
     lowercase__					:						Optional[int]												=			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
     )





