

'''simple docstring'''



import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    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_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': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
SCREAMING_SNAKE_CASE_     = [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def 	lowerCamelCase__							(   a__      ,   a__      ,   a__      ,   a__      ,   a__)		->     Dict:



    """simple docstring"""







    for attribute in key.split('.'):
        _snake_case			:			Any				       =		getattr(a__      ,   a__)

    if weight_type is not None:
        _snake_case			:			Dict				       =		getattr(a__      ,   a__).shape
    else:
        _snake_case			:			Optional[int]				       =		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			:			Union[str, Any]				       =		value
    elif weight_type == "weight_g":
        _snake_case			:			str				       =		value
    elif weight_type == "weight_v":
        _snake_case			:			Dict				       =		value
    elif weight_type == "bias":
        _snake_case			:			Optional[Any]				       =		value
    else:
        _snake_case			:			List[str]				       =		value

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

def 	lowerCamelCase__							(   a__      ,   a__)		->     List[Any]:



    """simple docstring"""







    _snake_case			:			List[str]				       =		[]
    _snake_case			:			str				       =		fairseq_model.state_dict()

    _snake_case			:			int				       =		hf_model.feature_extractor
    _snake_case			:			Optional[int]				       =		hf_model.adapter

    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			:			Tuple				       =		True
        elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']):
            load_adapter(a__      ,   a__      ,   a__      ,   a__)
            _snake_case			:			List[str]				       =		True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split('w2v_model.')[-1] == name.split('.')[0]:
                    _snake_case			:			Optional[int]				       =		True
                    if "*" in mapped_key:
                        _snake_case			:			int				       =		name.split(a__)[0].split('.')[-2]
                        _snake_case			:			List[Any]				       =		mapped_key.replace('*'      ,   a__)
                    if "weight_g" in name:
                        _snake_case			:			Any				       =		'weight_g'
                    elif "weight_v" in name:
                        _snake_case			:			Optional[int]				       =		'weight_v'
                    elif "bias" in name:
                        _snake_case			:			List[str]				       =		'bias'
                    elif "weight" in name:
                        _snake_case			:			List[Any]				       =		'weight'
                    else:
                        _snake_case			:			Optional[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 	lowerCamelCase__							(   a__      ,   a__      ,   a__      ,   a__      ,   a__)		->     Optional[int]:



    """simple docstring"""







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

def 	lowerCamelCase__							(   a__      ,   a__      ,   a__      ,   a__)		->     Optional[Any]:



    """simple docstring"""







    _snake_case			:			Dict				       =		full_name.split('adaptor.')[-1]
    _snake_case			:			str				       =		name.split('.')

    if items[1].isdigit():
        _snake_case			:			int				       =		int(items[1])
    else:
        _snake_case			:			str				       =		None

    if "adaptor" not in full_name:
        if "proj_ln" in full_name:
            # has to be layer norm
            if "bias" in name:
                assert (
                    value.shape == adapter.proj_layer_norm.bias.data.shape
                ), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
                _snake_case			:			List[str]				       =		value
                logger.info(F"""Adapter proj layer norm bias was initialized from {full_name}.""")
            if "weight" in name:
                assert (
                    value.shape == adapter.proj_layer_norm.weight.data.shape
                ), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
                _snake_case			:			Any				       =		value
        else:
            # has to be projection layer
            if "bias" in name:
                assert (
                    value.shape == adapter.proj.bias.data.shape
                ), F"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
                _snake_case			:			Optional[Any]				       =		value
                logger.info(F"""Adapter proj layer bias was initialized from {full_name}.""")
            if "weight" in name:
                assert (
                    value.shape == adapter.proj.weight.data.shape
                ), F"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
                _snake_case			:			List[str]				       =		value
                logger.info(F"""Adapter proj layer weight was initialized from {full_name}.""")
    elif isinstance(a__      ,   a__):
        if "bias" in name:
            assert (
                value.shape == adapter.layers[layer_id].conv.bias.data.shape
            ), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
            _snake_case			:			Union[str, Any]				       =		value
            logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}.""")
        elif "weight" in name:
            assert (
                value.shape == adapter.layers[layer_id].conv.weight.data.shape
            ), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
            _snake_case			:			Optional[Any]				       =		value
            logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}.""")
    else:
        unused_weights.append(a__)

def 	lowerCamelCase__							(   a__)		->     Any:



    """simple docstring"""







    _snake_case       ,				_snake_case			:			List[str]				       =		emb.weight.shape
    _snake_case			:			List[str]				       =		nn.Linear(a__      ,   a__      ,   bias=a__)
    _snake_case			:			List[str]				       =		emb.weight.data
    return lin_layer

@torch.no_grad()
def 	lowerCamelCase__							(   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   )		->     Tuple:



    """simple docstring"""







    _snake_case			:			str				       =		WavaVecaConfig.from_pretrained(
        a__      ,   add_adapter=a__      ,   adapter_stride=a__      ,   adapter_kernel_size=a__      ,   use_auth_token=a__      ,   output_hidden_size=a__      ,   )
    _snake_case			:			List[Any]				       =		MBartConfig.from_pretrained(a__)

    # load model
    _snake_case       ,				_snake_case       ,				_snake_case			:			Optional[int]				       =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]      ,   arg_overrides={
            'config_yaml': config_yaml_path,
            'data': '/'.join(dict_path.split('/')[:-1]),
            'w2v_path': checkpoint_path,
            'load_pretrained_decoder_from': None,
        }      ,   )
    _snake_case			:			Optional[int]				       =		model[0].eval()

    # load feature extractor
    _snake_case			:			Union[str, Any]				       =		WavaVecaFeatureExtractor.from_pretrained(a__      ,   use_auth_token=a__)

    # set weights for wav2vec2 encoder
    _snake_case			:			str				       =		WavaVecaModel(a__)

    recursively_load_weights_wavaveca(model.encoder      ,   a__)

    # load decoder weights
    _snake_case			:			str				       =		MBartForCausalLM(a__)
    _snake_case       ,				_snake_case			:			Optional[int]				       =		hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()      ,   strict=a__)
    logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}""")
    logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""")

    _snake_case			:			str				       =		SpeechEncoderDecoderModel(encoder=a__      ,   decoder=a__)
    _snake_case			:			Dict				       =		False

    _snake_case			:			List[str]				       =		MBartaaTokenizer(a__)
    tokenizer.save_pretrained(a__)

    _snake_case			:			Union[str, Any]				       =		hf_wavavec.config.to_dict()
    _snake_case			:			List[Any]				       =		tokenizer.pad_token_id
    _snake_case			:			List[str]				       =		tokenizer.bos_token_id
    _snake_case			:			Optional[int]				       =		tokenizer.eos_token_id
    _snake_case			:			Optional[int]				       =		'mbart50'
    _snake_case			:			int				       =		'wav2vec2'

    _snake_case			:			Optional[Any]				       =		tokenizer.eos_token_id
    _snake_case			:			str				       =		2_5_0_0_0_4
    _snake_case			:			List[str]				       =		tokenizer.eos_token_id

    _snake_case			:			Union[str, Any]				       =		SpeechEncoderDecoderConfig.from_dict(a__)

    hf_wavavec.save_pretrained(a__)
    feature_extractor.save_pretrained(a__)


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("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
   parser.add_argument("--config_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
   parser.add_argument(
       "--encoder_config_path",
       default="facebook/wav2vec2-xls-r-1b",
       type=str,
       help="Path to hf encoder wav2vec2 checkpoint config",
   )
   parser.add_argument(
       "--decoder_config_path",
       default="facebook/mbart-large-50-one-to-many-mmt",
       type=str,
       help="Path to hf decoder checkpoint config",
   )
   parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
   parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
   parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
   parser.add_argument("--encoder_output_dim", default=1_024, type=int, help="encoder output dim")
   parser.add_argument("--start_token_id", default=250_004, type=int, help="`decoder_start_token_id` of model config")

   SCREAMING_SNAKE_CASE_     = parser.parse_args()
   convert_wavaveca_checkpoint(
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.dict_path,
       args.config_yaml_path,
       encoder_config_path=args.encoder_config_path,
       decoder_config_path=args.decoder_config_path,
       add_adapter=args.add_adapter,
       adapter_kernel_size=args.adapter_kernel_size,
       adapter_stride=args.adapter_stride,
       decoder_start_token_id=args.start_token_id,
       encoder_output_dim=args.encoder_output_dim,
   )





"""simple docstring"""




import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class     lowercase(_lowercase    ,						_lowercase    ,						unittest.TestCase       ):
     __snake_case:	Optional[int]						=     IFImgaImgSuperResolutionPipeline
     __snake_case:	Dict						=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
     __snake_case:	Any						=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}       )
     __snake_case:	List[Any]						=     PipelineTesterMixin.required_optional_params - {'latents'}



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         return self._get_superresolution_dummy_components()



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=0   )		->     Union[str, Any]:







         """simple docstring"""



         if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
             a__		=			torch.manual_seed(__SCREAMING_SNAKE_CASE   )
         else:
             a__		=			torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )

         a__		=			floats_tensor((1, 3, 3_2, 3_2)     ,				rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			floats_tensor((1, 3, 1_6, 1_6)     ,				rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )

         a__		=			{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'original_image': original_image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs



     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()     ,				reason='XFormers attention is only available with CUDA and `xformers` installed'     ,				)
     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         self._test_save_load_optional_components()



     @unittest.skipIf(torch_device != 'cuda'     ,				reason='float16 requires CUDA'   )
     def 			lowercase__  (							self   )		->     List[Any]:







         """simple docstring"""



         super().test_save_load_floataa(expected_max_diff=1e-1   )



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )



     def 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         self._test_save_load_local()




     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2     ,				)


