


import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

snake_case				:    Dict       				=     {
    '''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''',
}
snake_case				:    Union[str, Any]       				=     [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]
def 				__lowerCamelCase						( UpperCAmelCase_						:		Dict ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Any						):




      """simple docstring"""





      for attribute in key.split('''.'''						):
            a						:List[str] 							=       getattr(UpperCAmelCase_ ,  UpperCAmelCase_						)

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

      logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[Any] ,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a						:Dict 							=       []
      a						:Optional[int] 							=       fairseq_model.state_dict()

      a						:Optional[Any] 							=       hf_model.feature_extractor

      # if encoder has different dim to decoder -> use proj_weight
      a						:Dict 							=       None

      for name, value in fairseq_dict.items():
            a						:List[Any] 							=       False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  hf_model.config.feat_extract_norm == '''group''' ,  )
                  a						:Optional[int] 							=       True
            elif name.split('''.'''						)[0] == "proj":
                  a						:Union[str, Any] 							=       fairseq_model.proj
                  a						:Optional[Any] 							=       True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split('''w2v_model.'''						)[-1] == name.split('''.'''						)[0]:
                              a						:int 							=       True
                              if "*" in mapped_key:
                                    a						:Any 							=       name.split(UpperCAmelCase_						)[0].split('''.'''						)[-2]
                                    a						:int 							=       mapped_key.replace('''*''' ,  UpperCAmelCase_						)
                              if "weight_g" in name:
                                    a						:int 							=       '''weight_g'''
                              elif "weight_v" in name:
                                    a						:Any 							=       '''weight_v'''
                              elif "bias" in name:
                                    a						:Union[str, Any] 							=       '''bias'''
                              elif "weight" in name:
                                    a						:Any 							=       '''weight'''
                              else:
                                    a						:Any 							=       None
                              set_recursively(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
                        continue
            if not is_used:
                  unused_weights.append(UpperCAmelCase_						)

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

      return proj_weight
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		Tuple ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





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




      """simple docstring"""





      a							,				a						:Any 							=       emb.weight.shape
      a						:Optional[int] 							=       nn.Linear(UpperCAmelCase_ ,  UpperCAmelCase_ ,  bias=UpperCAmelCase_						)
      a						:Dict 							=       emb.weight.data
      return lin_layer
def 				__lowerCamelCase						( UpperCAmelCase_						:		Dict						):




      """simple docstring"""





      with open(UpperCAmelCase_ ,  '''r''' ,  encoding='''utf-8'''						) as f:
            a						:Tuple 							=       f.readlines()
            a						:List[str] 							=       [line.split(''' '''						)[0] for line in lines]

      a						:str 							=       len(UpperCAmelCase_						)

      a						:Union[str, Any] 							=       {
          '''<s>''': 0,
          '''<pad>''': 1,
          '''</s>''': 2,
          '''<unk>''': 3,
      }

      vocab_dict.update(dict(zip(UpperCAmelCase_ ,  range(4 ,  num_words + 4						)						)						)						)
      return vocab_dict
@torch.no_grad()
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[Any] ,  UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		List[Any] ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Dict ,  UpperCAmelCase_						:		Optional[int] ,  ):




      """simple docstring"""





      a						:int 							=       WavaVecaConfig.from_pretrained(UpperCAmelCase_						)
      a						:Union[str, Any] 							=       SpeechaTextaConfig.from_pretrained(
          UpperCAmelCase_ ,  vocab_size=UpperCAmelCase_ ,  decoder_layers=UpperCAmelCase_ ,  do_stable_layer_norm=UpperCAmelCase_						)

      a						:str 							=       WavaVecaFeatureExtractor(
          feature_size=1 ,  sampling_rate=1_6000 ,  padding_value=0 ,  do_normalize=UpperCAmelCase_ ,  return_attention_mask=UpperCAmelCase_ ,  )

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

      # set weights for wav2vec2 encoder
      a						:List[Any] 							=       WavaVecaModel(UpperCAmelCase_						)
      a						:Optional[int] 							=       recursively_load_weights_wavaveca(model.encoder ,  UpperCAmelCase_						)

      a						:Union[str, Any] 							=       SpeechaTextaForCausalLM(UpperCAmelCase_						)
      a							,				a						:str 							=       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,  strict=UpperCAmelCase_						)

      # set output linear layer
      unexpected_keys.remove('''embed_out'''						)
      a						:List[str] 							=       nn.Parameter(model.decoder.embed_out.detach()						)

      # layer norm is init to identity matrix so leaving it is fine
      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}'''						)

      a						:Optional[int] 							=       SpeechEncoderDecoderModel(encoder=UpperCAmelCase_ ,  decoder=UpperCAmelCase_						)
      a						:List[str] 							=       False

      # add projection layer
      a						:int 							=       nn.Parameter(projection_layer.weight						)
      a						:List[str] 							=       nn.Parameter(projection_layer.bias						)

      a						:Optional[Any] 							=       create_vocab_dict(UpperCAmelCase_						)

      with open(os.path.join(UpperCAmelCase_ ,  '''vocab.json'''						) ,  '''w'''						) as fp:
            json.dump(UpperCAmelCase_ ,  UpperCAmelCase_						)

      a						:Tuple 							=       SpeechaTextaTokenizer(os.path.join(UpperCAmelCase_ ,  '''vocab.json'''						)						)
      tokenizer.save_pretrained(UpperCAmelCase_						)

      a						:Any 							=       hf_wavavec.config.to_dict()
      a						:Optional[int] 							=       tokenizer.pad_token_id
      a						:List[str] 							=       tokenizer.bos_token_id
      a						:List[str] 							=       tokenizer.eos_token_id
      a						:Dict 							=       '''speech_to_text_2'''
      a						:Union[str, Any] 							=       '''wav2vec2'''

      a						:List[str] 							=       SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase_						)

      hf_wavavec.save_pretrained(UpperCAmelCase_						)
      feature_extractor.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
     snake_case				:    Optional[int]       				=     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(
         '''--encoder_config_path''',
         default='''facebook/wav2vec2-large-lv60''',
         type=str,
         help='''Path to hf encoder wav2vec2 checkpoint config''',
     )
     parser.add_argument(
         '''--decoder_config_path''',
         default='''facebook/s2t-small-mustc-en-fr-st''',
         type=str,
         help='''Path to hf decoder s2t checkpoint config''',
     )
     parser.add_argument('''--vocab_size''', default=1_02_24, type=int, help='''Vocab size of decoder''')
     parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

     snake_case				:    List[Any]       				=     parser.parse_args()
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.dict_path,
         encoder_config_path=args.encoder_config_path,
         decoder_config_path=args.decoder_config_path,
         vocab_size=args.vocab_size,
         num_decoder_layers=args.num_decoder_layers,
     )










import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    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 SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      KandinskyVaaControlnetPipeline
    __lowerCamelCase			: Optional[int]					=      ["image_embeds", "negative_image_embeds", "hint"]
    __lowerCamelCase			: Dict					=      ["image_embeds", "negative_image_embeds", "hint"]
    __lowerCamelCase			: List[str]					=      [
        "generator",
        "height",
        "width",
        "latents",
        "guidance_scale",
        "num_inference_steps",
        "return_dict",
        "guidance_scale",
        "num_images_per_prompt",
        "output_type",
        "return_dict",
    ]
    __lowerCamelCase			: Dict					=      False






    @property
    def 			_lowerCAmelCase					(       self		):
        return 32






    @property
    def 			_lowerCAmelCase					(       self		):
        return 32






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






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






    @property
    def 			_lowerCAmelCase					(       self		):
        return 100






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

        A :       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,
        }

        A :       List[str]          =   UNetaDConditionModel(**lowerCamelCase__		)
        return model






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






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[Any]          =   self.dummy_unet
        A :       Tuple          =   self.dummy_movq

        A :       List[Any]          =   DDIMScheduler(
            num_train_timesteps=1000,		beta_schedule="""linear""",		beta_start=0.0_0085,		beta_end=0.012,		clip_sample=lowerCamelCase__,		set_alpha_to_one=lowerCamelCase__,		steps_offset=1,		prediction_type="""epsilon""",		thresholding=lowerCamelCase__,		)

        A :       int          =   {
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }
        return components






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=0		):
        A :       Optional[Any]          =   floats_tensor((1, self.text_embedder_hidden_size),		rng=random.Random(lowerCamelCase__		)		).to(lowerCamelCase__		)
        A :       List[Any]          =   floats_tensor((1, self.text_embedder_hidden_size),		rng=random.Random(seed + 1		)		).to(
            lowerCamelCase__		)

        # create hint
        A :       int          =   floats_tensor((1, 3, 64, 64),		rng=random.Random(lowerCamelCase__		)		).to(lowerCamelCase__		)

        if str(lowerCamelCase__		).startswith("""mps"""		):
            A :       Optional[Any]          =   torch.manual_seed(lowerCamelCase__		)
        else:
            A :       str          =   torch.Generator(device=lowerCamelCase__		).manual_seed(lowerCamelCase__		)
        A :       List[str]          =   {
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """hint""": hint,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """guidance_scale""": 4.0,
            """num_inference_steps""": 2,
            """output_type""": """np""",
        }
        return inputs






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   """cpu"""

        A :       List[str]          =   self.get_dummy_components()

        A :       Dict          =   self.pipeline_class(**lowerCamelCase__		)
        A :       Optional[Any]          =   pipe.to(lowerCamelCase__		)

        pipe.set_progress_bar_config(disable=lowerCamelCase__		)

        A :       int          =   pipe(**self.get_dummy_inputs(lowerCamelCase__		)		)
        A :       Union[str, Any]          =   output.images

        A :       str          =   pipe(
            **self.get_dummy_inputs(lowerCamelCase__		),		return_dict=lowerCamelCase__,		)[0]

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

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

        A :       Dict          =   np.array(
            [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]		)

        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 SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






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






    def 			_lowerCAmelCase					(       self		):
        A :       Tuple          =   load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""		)

        A :       Any          =   load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/hint_image_cat.png"""		)
        A :       Optional[Any]          =   torch.from_numpy(np.array(lowerCamelCase__		)		).float() / 255.0
        A :       List[str]          =   hint.permute(2,		0,		1		).unsqueeze(0		)

        A :       Union[str, Any]          =   KandinskyVaaPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-prior""",		torch_dtype=torch.floataa		)
        pipe_prior.to(lowerCamelCase__		)

        A :       Tuple          =   KandinskyVaaControlnetPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-controlnet-depth""",		torch_dtype=torch.floataa		)
        A :       Union[str, Any]          =   pipeline.to(lowerCamelCase__		)
        pipeline.set_progress_bar_config(disable=lowerCamelCase__		)

        A :       Optional[Any]          =   """A robot, 4k photo"""

        A :       Union[str, Any]          =   torch.Generator(device="""cuda"""		).manual_seed(0		)
        A     ,							A :       int          =   pipe_prior(
            lowerCamelCase__,		generator=lowerCamelCase__,		num_inference_steps=5,		negative_prompt="""""",		).to_tuple()

        A :       Union[str, Any]          =   torch.Generator(device="""cuda"""		).manual_seed(0		)
        A :       int          =   pipeline(
            image_embeds=lowerCamelCase__,		negative_image_embeds=lowerCamelCase__,		hint=lowerCamelCase__,		generator=lowerCamelCase__,		num_inference_steps=100,		output_type="""np""",		)

        A :       Optional[int]          =   output.images[0]

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

        assert_mean_pixel_difference(lowerCamelCase__,		lowerCamelCase__		)


