



'''simple docstring'''





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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 PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 				snake_case (UpperCamelCase		,      UpperCamelCase		,      UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Union[str, Any]						   =					AltDiffusionPipeline
       lowerCAmelCase__  :str						   =					TEXT_TO_IMAGE_PARAMS
       lowerCAmelCase__  :List[str]						   =					TEXT_TO_IMAGE_BATCH_PARAMS
       lowerCAmelCase__  :List[Any]						   =					TEXT_TO_IMAGE_IMAGE_PARAMS
       lowerCAmelCase__  :Any						   =					TEXT_TO_IMAGE_IMAGE_PARAMS



       def 							_a      (				self      )  ->							int:
           torch.manual_seed(0      )
           lowercase__		       =						UNetaDConditionModel(
               block_out_channels=(32, 64)     ,layers_per_block=2     ,sample_size=32     ,in_channels=4     ,out_channels=4     ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")     ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")     ,cross_attention_dim=32     ,)
           lowercase__		       =						DDIMScheduler(
               beta_start=0.0_00_85     ,beta_end=0.0_12     ,beta_schedule="scaled_linear"     ,clip_sample=UpperCAmelCase_     ,set_alpha_to_one=UpperCAmelCase_     ,)
           torch.manual_seed(0      )
           lowercase__		       =						AutoencoderKL(
               block_out_channels=[32, 64]     ,in_channels=3     ,out_channels=3     ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,latent_channels=4     ,)

           # TODO: address the non-deterministic text encoder (fails for save-load tests)
           # torch.manual_seed(0)
           # text_encoder_config = RobertaSeriesConfig(
           #     hidden_size=32,
           #     project_dim=32,
           #     intermediate_size=37,
           #     layer_norm_eps=1e-05,
           #     num_attention_heads=4,
           #     num_hidden_layers=5,
           #     vocab_size=5002,
           # )
           # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

           torch.manual_seed(0      )
           lowercase__		       =						CLIPTextConfig(
               bos_token_id=0     ,eos_token_id=2     ,hidden_size=32     ,projection_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-0_5     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=5_002     ,)
           lowercase__		       =						CLIPTextModel(UpperCAmelCase_      )

           lowercase__		       =						XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"      )
           lowercase__		       =						77

           lowercase__		       =						{
               "unet": unet,
               "scheduler": scheduler,
               "vae": vae,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "safety_checker": None,
               "feature_extractor": None,
           }
           return components



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=0      )  ->							Optional[Any]:
           if str(UpperCAmelCase_      ).startswith("mps"      ):
               lowercase__		       =						torch.manual_seed(UpperCAmelCase_      )
           else:
               lowercase__		       =						torch.Generator(device=UpperCAmelCase_      ).manual_seed(UpperCAmelCase_      )
           lowercase__		       =						{
               "prompt": "A painting of a squirrel eating a burger",
               "generator": generator,
               "num_inference_steps": 2,
               "guidance_scale": 6.0,
               "output_type": "numpy",
           }
           return inputs



       def 							_a      (				self      )  ->							Any:
           super().test_attention_slicing_forward_pass(expected_max_diff=3E-3      )



       def 							_a      (				self      )  ->							Optional[int]:
           super().test_inference_batch_single_identical(expected_max_diff=3E-3      )



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						"cpu"  # ensure determinism for the device-dependent torch.Generator

           lowercase__		       =						self.get_dummy_components()
           torch.manual_seed(0      )
           lowercase__		       =						RobertaSeriesConfig(
               hidden_size=32     ,project_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-0_5     ,num_attention_heads=4     ,num_hidden_layers=5     ,vocab_size=5_002     ,)
           # TODO: remove after fixing the non-deterministic text encoder
           lowercase__		       =						RobertaSeriesModelWithTransformation(UpperCAmelCase_      )
           lowercase__		       =						text_encoder

           lowercase__		       =						AltDiffusionPipeline(**UpperCAmelCase_      )
           lowercase__		       =						alt_pipe.to(UpperCAmelCase_      )
           alt_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

           lowercase__		       =						self.get_dummy_inputs(UpperCAmelCase_      )
           lowercase__		       =						"A photo of an astronaut"
           lowercase__		       =						alt_pipe(**UpperCAmelCase_      )
           lowercase__		       =						output.images
           lowercase__		       =						image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           lowercase__		       =						np.array(
               [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93]      )

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







       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						"cpu"  # ensure determinism for the device-dependent torch.Generator

           lowercase__		       =						self.get_dummy_components()
           lowercase__		       =						PNDMScheduler(skip_prk_steps=UpperCAmelCase_      )
           torch.manual_seed(0      )
           lowercase__		       =						RobertaSeriesConfig(
               hidden_size=32     ,project_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-0_5     ,num_attention_heads=4     ,num_hidden_layers=5     ,vocab_size=5_002     ,)
           # TODO: remove after fixing the non-deterministic text encoder
           lowercase__		       =						RobertaSeriesModelWithTransformation(UpperCAmelCase_      )
           lowercase__		       =						text_encoder
           lowercase__		       =						AltDiffusionPipeline(**UpperCAmelCase_      )
           lowercase__		       =						alt_pipe.to(UpperCAmelCase_      )
           alt_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

           lowercase__		       =						self.get_dummy_inputs(UpperCAmelCase_      )
           lowercase__		       =						alt_pipe(**UpperCAmelCase_      )
           lowercase__		       =						output.images
           lowercase__		       =						image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           lowercase__		       =						np.array(
               [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37]      )

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





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



       def 							_a      (				self      )  ->							List[str]:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



       def 							_a      (				self      )  ->							Optional[int]:
           # make sure here that pndm scheduler skips prk
           lowercase__		       =						AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"     ,safety_checker=UpperCAmelCase_      )
           lowercase__		       =						alt_pipe.to(UpperCAmelCase_      )
           alt_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

           lowercase__		       =						"A painting of a squirrel eating a burger"
           lowercase__		       =						torch.manual_seed(0      )
           lowercase__		       =						alt_pipe([prompt]     ,generator=UpperCAmelCase_     ,guidance_scale=6.0     ,num_inference_steps=20     ,output_type="np"      )

           lowercase__		       =						output.images

           lowercase__		       =						image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           lowercase__		       =						np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86]      )

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







       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						DDIMScheduler.from_pretrained("BAAI/AltDiffusion"     ,subfolder="scheduler"      )

           lowercase__		       =						AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"     ,scheduler=UpperCAmelCase_     ,safety_checker=UpperCAmelCase_      )
           lowercase__		       =						alt_pipe.to(UpperCAmelCase_      )
           alt_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

           lowercase__		       =						"A painting of a squirrel eating a burger"
           lowercase__		       =						torch.manual_seed(0      )

           lowercase__		       =						alt_pipe([prompt]     ,generator=UpperCAmelCase_     ,num_inference_steps=2     ,output_type="numpy"      )
           lowercase__		       =						output.images

           lowercase__		       =						image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           lowercase__		       =						np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23]      )

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









'''simple docstring'''





import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "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.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "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",
    "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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
SCREAMING_SNAKE_CASE__      					=     [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						Union[str, Any]     ,_snake_case  :						Any     ,_snake_case  :						Optional[int]     ,_snake_case  :						List[str]       ):





    '''simple docstring'''


    for attribute in key.split("."       ):
        lowercase__		       =						getattr(_snake_case     ,_snake_case       )

    if weight_type is not None:
        lowercase__		       =						getattr(_snake_case     ,_snake_case       ).shape
    else:
        lowercase__		       =						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":
        lowercase__		       =						value
    elif weight_type == "weight_g":
        lowercase__		       =						value
    elif weight_type == "weight_v":
        lowercase__		       =						value
    elif weight_type == "bias":
        lowercase__		       =						value
    else:
        lowercase__		       =						value

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




def        lowerCamelCase  (				_snake_case  :						Any     ,_snake_case  :						int       ):





    '''simple docstring'''


    lowercase__		       =						[]
    lowercase__		       =						fairseq_model.state_dict()

    lowercase__		       =						hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        lowercase__		       =						False
        if "conv_layers" in name:
            load_conv_layer(
                _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,hf_model.config.feat_extract_norm == "group"     ,)
            lowercase__		       =						True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split("w2v_model."       )[-1] == name.split("."       )[0]:
                    lowercase__		       =						True
                    if "*" in mapped_key:
                        lowercase__		       =						name.split(_snake_case       )[0].split("."       )[-2]
                        lowercase__		       =						mapped_key.replace("*"     ,_snake_case       )
                    if "weight_g" in name:
                        lowercase__		       =						"weight_g"
                    elif "weight_v" in name:
                        lowercase__		       =						"weight_v"
                    elif "bias" in name and "relative_attention_bias" not in name:
                        lowercase__		       =						"bias"
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        lowercase__		       =						"weight"
                    else:
                        lowercase__		       =						None

                    set_recursively(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case       )
                continue
        if not is_used:
            unused_weights.append(_snake_case       )

    logger.warning(f'''Unused weights: {unused_weights}'''       )




def        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						int     ,_snake_case  :						Optional[Any]     ,_snake_case  :						Tuple     ,_snake_case  :						List[Any]       ):





    '''simple docstring'''


    lowercase__		       =						full_name.split("conv_layers."       )[-1]
    lowercase__		       =						name.split("."       )
    lowercase__		       =						int(items[0]       )
    lowercase__		       =						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.'''
            )
            lowercase__		       =						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.'''
            )
            lowercase__		       =						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."
            )
            lowercase__		       =						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.'''
            )
            lowercase__		       =						value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
    else:
        unused_weights.append(_snake_case       )




@torch.no_grad()
def        lowerCamelCase  (				_snake_case  :						Optional[int]     ,_snake_case  :						Any     ,_snake_case  :						List[Any]=None       ):





    '''simple docstring'''


    lowercase__		       =						torch.load(_snake_case       )
    lowercase__		       =						WavLMConfigOrig(checkpoint["cfg"]       )
    lowercase__		       =						WavLMOrig(_snake_case       )
    model.load_state_dict(checkpoint["model"]       )
    model.eval()

    if config_path is not None:
        lowercase__		       =						WavLMConfig.from_pretrained(_snake_case       )
    else:
        lowercase__		       =						WavLMConfig()

    lowercase__		       =						WavLMModel(_snake_case       )

    recursively_load_weights(_snake_case     ,_snake_case       )

    hf_wavlm.save_pretrained(_snake_case       )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__      					=     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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 SCREAMING_SNAKE_CASE__      					=     parser.parse_args()
 convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




