






'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCamelCase							=False




class     A    (      unittest.TestCase						):






 """simple docstring"""





 pass






@nightly
@require_torch_gpu
class     A    (      unittest.TestCase						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(lowerCAmelCase_ )
       pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

       UpperCamelCase_				:   Any					    =							"""A painting of a squirrel eating a burger """
       UpperCamelCase_				:   Tuple					    =							torch.manual_seed(0 )
       UpperCamelCase_				:   List[Any]					    =							pipe(
           prompt=lowerCAmelCase_			,     generator=lowerCAmelCase_			,     guidance_scale=7.5			,     num_inference_steps=2			,     output_type="""numpy""" ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(lowerCAmelCase_ )
             UpperCamelCase_				:   Union[str, Any]					    =							VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCAmelCase_ )
       pipe.to(lowerCAmelCase_ )
       pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

       UpperCamelCase_				:   int					    =							generator.manual_seed(0 )
       UpperCamelCase_				:   int					    =							pipe(
           prompt=lowerCAmelCase_			,     generator=lowerCAmelCase_			,     guidance_scale=7.5			,     num_inference_steps=2			,     output_type="""numpy""" ).images

       assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Tuple					    =							VersatileDiffusionTextToImagePipeline.from_pretrained(
           """shi-labs/versatile-diffusion"""			,     torch_dtype=torch.floataa )
       pipe.to(lowerCAmelCase_ )
       pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

       UpperCamelCase_				:   List[Any]					    =							"""A painting of a squirrel eating a burger """
       UpperCamelCase_				:   Optional[int]					    =							torch.manual_seed(0 )
       UpperCamelCase_				:   Optional[int]					    =							pipe(
           prompt=lowerCAmelCase_			,     generator=lowerCAmelCase_			,     guidance_scale=7.5			,     num_inference_steps=50			,     output_type="""numpy""" ).images

       UpperCamelCase_				:   int					    =							image[0, 2_53:2_56, 2_53:2_56, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       UpperCamelCase_				:   int					    =							np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78] )

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














'''simple docstring'''

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()
UpperCamelCase							=logging.get_logger(__name__)

UpperCamelCase							={
    "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",
}
UpperCamelCase							=[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def 							snake_case					(     a_							: str    ,   a_							: Any    ,   a_							: Dict    ,   a_							: Optional[int]    ,   a_							: Optional[int]	)       ->				int:





      """simple docstring"""

      for attribute in key.split("""."""	):
            UpperCamelCase_				:   Union[str, Any]					    =							getattr(a_    ,   a_	)

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

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



def 							snake_case					(     a_							: Optional[int]    ,   a_							: int	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   int					    =							[]
      UpperCamelCase_				:   Dict					    =							fairseq_model.state_dict()

      UpperCamelCase_				:   Union[str, Any]					    =							hf_model.feature_extractor

      # if encoder has different dim to decoder -> use proj_weight
      UpperCamelCase_				:   Optional[int]					    =							None

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

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

      return proj_weight



def 							snake_case					(     a_							: Any    ,   a_							: Any    ,   a_							: Union[str, Any]    ,   a_							: Dict    ,   a_							: Tuple	)       ->				List[str]:





      """simple docstring"""

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



def 							snake_case					(     a_							: str	)       ->				Union[str, Any]:





      """simple docstring"""

      UpperCamelCase_    ,			UpperCamelCase_				:   List[str]					    =							emb.weight.shape
      UpperCamelCase_				:   str					    =							nn.Linear(a_    ,   a_    ,   bias=a_	)
      UpperCamelCase_				:   Optional[int]					    =							emb.weight.data
      return lin_layer



def 							snake_case					(     a_							: Any	)       ->				Tuple:





      """simple docstring"""

      with open(a_    ,   """r"""    ,   encoding="""utf-8"""	) as f:
            UpperCamelCase_				:   Optional[int]					    =							f.readlines()
            UpperCamelCase_				:   Union[str, Any]					    =							[line.split(""" """	)[0] for line in lines]

      UpperCamelCase_				:   List[Any]					    =							len(a_	)

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

      vocab_dict.update(dict(zip(a_    ,   range(4    ,   num_words + 4	)	)	)	)
      return vocab_dict



@torch.no_grad()
def 							snake_case					(     a_							: List[Any]    ,   a_							: str    ,   a_							: int    ,   a_							: int    ,   a_							: Optional[int]    ,   a_							: Any    ,   a_							: Tuple    ,   )       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							WavaVecaConfig.from_pretrained(a_	)
      UpperCamelCase_				:   Any					    =							SpeechaTextaConfig.from_pretrained(
          a_    ,   vocab_size=a_    ,   decoder_layers=a_    ,   do_stable_layer_norm=a_	)

      UpperCamelCase_				:   Optional[Any]					    =							WavaVecaFeatureExtractor(
          feature_size=1    ,   sampling_rate=16_000    ,   padding_value=0    ,   do_normalize=a_    ,   return_attention_mask=a_    ,   )

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

      # set weights for wav2vec2 encoder
      UpperCamelCase_				:   Tuple					    =							WavaVecaModel(a_	)
      UpperCamelCase_				:   Any					    =							recursively_load_weights_wavaveca(model.encoder    ,   a_	)

      UpperCamelCase_				:   Any					    =							SpeechaTextaForCausalLM(a_	)
      UpperCamelCase_    ,			UpperCamelCase_				:   Optional[Any]					    =							hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()    ,   strict=a_	)

      # set output linear layer
      unexpected_keys.remove("""embed_out"""	)
      UpperCamelCase_				:   Tuple					    =							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}"	)

      UpperCamelCase_				:   Optional[Any]					    =							SpeechEncoderDecoderModel(encoder=a_    ,   decoder=a_	)
      UpperCamelCase_				:   Dict					    =							False

      # add projection layer
      UpperCamelCase_				:   Any					    =							nn.Parameter(projection_layer.weight	)
      UpperCamelCase_				:   Optional[Any]					    =							nn.Parameter(projection_layer.bias	)

      UpperCamelCase_				:   Dict					    =							create_vocab_dict(a_	)

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

      UpperCamelCase_				:   Optional[int]					    =							SpeechaTextaTokenizer(os.path.join(a_    ,   """vocab.json"""	)	)
      tokenizer.save_pretrained(a_	)

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

      UpperCamelCase_				:   Dict					    =							SpeechEncoderDecoderConfig.from_dict(a_	)

      hf_wavavec.save_pretrained(a_	)
      feature_extractor.save_pretrained(a_	)


if __name__ == "__main__":
      UpperCamelCase							=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_0224, type=int, help="Vocab size of decoder")
      parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

      UpperCamelCase							=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,
      )






