
import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					List[str]    =     CONFIG_NAME

 @register_to_config
 def __init__(   self  ,						__UpperCamelCase = True  ,						__UpperCamelCase = 8  ,						__UpperCamelCase = "lanczos"  ,						__UpperCamelCase = True  ,						__UpperCamelCase = False  ,						):




        """simple docstring"""


        super().__init__()

 @staticmethod
 def lowerCamelCase_					(   __UpperCamelCase  ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_											=							images[None, ...]
        UpperCamelCase_											=							(images * 2_5_5).round().astype("""uint8"""  )
        if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               UpperCamelCase_											=							[Image.fromarray(image.squeeze()  ,						mode="""L"""  ) for image in images]
        else:
               UpperCamelCase_											=							[Image.fromarray(__UpperCamelCase  ) for image in images]

        return pil_images

 @staticmethod
 def lowerCamelCase_					(   __UpperCamelCase  ):




        """simple docstring"""


        if not isinstance(__UpperCamelCase  ,						__UpperCamelCase  ):
               UpperCamelCase_											=							[images]
        UpperCamelCase_											=							[np.array(__UpperCamelCase  ).astype(np.floataa  ) / 255.0 for image in images]
        UpperCamelCase_											=							np.stack(__UpperCamelCase  ,						axis=0  )

        return images

 @staticmethod
 def lowerCamelCase_					(   __UpperCamelCase  ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_											=							images[..., None]

        UpperCamelCase_											=							torch.from_numpy(images.transpose(0  ,						3  ,						1  ,						2  )  )
        return images

 @staticmethod
 def lowerCamelCase_					(   __UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							images.cpu().permute(0  ,						2  ,						3  ,						1  ).float().numpy()
        return images

 @staticmethod
 def lowerCamelCase_					(   __UpperCamelCase  ):




        """simple docstring"""


        return 2.0 * images - 1.0

 @staticmethod
 def lowerCamelCase_					(   __UpperCamelCase  ):




        """simple docstring"""


        return (images / 2 + 0.5).clamp(0  ,						1  )

 @staticmethod
 def lowerCamelCase_					(   __UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							image.convert("""RGB"""  )
        return image

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						):




        """simple docstring"""


        if height is None:
               UpperCamelCase_											=							image.height
        if width is None:
               UpperCamelCase_											=							image.width

        UpperCamelCase_							,					UpperCamelCase_											=							(
            x - x % self.config.vae_scale_factor for x in (width, height)
        )  # resize to integer multiple of vae_scale_factor
        UpperCamelCase_											=							image.resize((width, height)  ,						resample=PIL_INTERPOLATION[self.config.resample]  )
        return image

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						):




        """simple docstring"""


        UpperCamelCase_											=							(PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(__UpperCamelCase  ,						__UpperCamelCase  ):
               UpperCamelCase_											=							[image]
        elif not (isinstance(__UpperCamelCase  ,						__UpperCamelCase  ) and all(isinstance(__UpperCamelCase  ,						__UpperCamelCase  ) for i in image  )):
               raise ValueError(
                   f'''Input is in incorrect format: {[type(__UpperCamelCase  ) for i in image]}. Currently, we only support {", ".join(__UpperCamelCase  )}'''  )

        if isinstance(image[0]  ,						PIL.Image.Image  ):
               if self.config.do_convert_rgb:
                      UpperCamelCase_											=							[self.convert_to_rgb(__UpperCamelCase  ) for i in image]
               if self.config.do_resize:
                      UpperCamelCase_											=							[self.resize(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ) for i in image]
               UpperCamelCase_											=							self.pil_to_numpy(__UpperCamelCase  )  # to np
               UpperCamelCase_											=							self.numpy_to_pt(__UpperCamelCase  )  # to pt

        elif isinstance(image[0]  ,						np.ndarray  ):
               UpperCamelCase_											=							np.concatenate(__UpperCamelCase  ,						axis=0  ) if image[0].ndim == 4 else np.stack(__UpperCamelCase  ,						axis=0  )
               UpperCamelCase_											=							self.numpy_to_pt(__UpperCamelCase  )
               UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							image.shape
               if self.config.do_resize and (
                   height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
               ):
                      raise ValueError(
                          f'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
                          f'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''  )

        elif isinstance(image[0]  ,						torch.Tensor  ):
               UpperCamelCase_											=							torch.cat(__UpperCamelCase  ,						axis=0  ) if image[0].ndim == 4 else torch.stack(__UpperCamelCase  ,						axis=0  )
               UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							image.shape

               # don't need any preprocess if the image is latents
               if channel == 4:
                      return image

               if self.config.do_resize and (
                   height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
               ):
                      raise ValueError(
                          f'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
                          f'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''  )

        # expected range [0,1], normalize to [-1,1]
        UpperCamelCase_											=							self.config.do_normalize
        if image.min() < 0:
               warnings.warn(
                   """Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] """
                   f'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'''  ,						__UpperCamelCase  ,						)
               UpperCamelCase_											=							False

        if do_normalize:
               UpperCamelCase_											=							self.normalize(__UpperCamelCase  )

        return image

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = "pil"  ,						__UpperCamelCase = None  ,						):




        """simple docstring"""


        if not isinstance(__UpperCamelCase  ,						torch.Tensor  ):
               raise ValueError(
                   f'''Input for postprocessing is in incorrect format: {type(__UpperCamelCase  )}. We only support pytorch tensor'''  )
        if output_type not in ["latent", "pt", "np", "pil"]:
               UpperCamelCase_											=							(
                   f'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
                   """`pil`, `np`, `pt`, `latent`"""
               )
               deprecate("""Unsupported output_type"""  ,						"""1.0.0"""  ,						__UpperCamelCase  ,						standard_warn=__UpperCamelCase  )
               UpperCamelCase_											=							"""np"""

        if output_type == "latent":
               return image

        if do_denormalize is None:
               UpperCamelCase_											=							[self.config.do_normalize] * image.shape[0]

        UpperCamelCase_											=							torch.stack(
            [self.denormalize(image[i]  ) if do_denormalize[i] else image[i] for i in range(image.shape[0]  )]  )

        if output_type == "pt":
               return image

        UpperCamelCase_											=							self.pt_to_numpy(__UpperCamelCase  )

        if output_type == "np":
               return image

        if output_type == "pil":
               return self.numpy_to_pil(__UpperCamelCase  )



class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					Optional[int]    =     CONFIG_NAME

 @register_to_config
 def __init__(   self  ,						__UpperCamelCase = True  ,						__UpperCamelCase = 8  ,						__UpperCamelCase = "lanczos"  ,						__UpperCamelCase = True  ,						):




        """simple docstring"""


        super().__init__()

 @staticmethod
 def lowerCamelCase_					(   __UpperCamelCase  ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_											=							images[None, ...]
        UpperCamelCase_											=							(images * 2_5_5).round().astype("""uint8"""  )
        if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               UpperCamelCase_											=							[Image.fromarray(image.squeeze()  ,						mode="""L"""  ) for image in images]
        else:
               UpperCamelCase_											=							[Image.fromarray(image[:, :, :3]  ) for image in images]

        return pil_images

 @staticmethod
 def lowerCamelCase_					(   __UpperCamelCase  ):




        """simple docstring"""


        return image[:, :, 1] * 2**8 + image[:, :, 2]

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_											=							images[None, ...]
        UpperCamelCase_											=							images[:, :, :, 3:]
        if images.shape[-1] == 6:
               UpperCamelCase_											=							(images_depth * 2_5_5).round().astype("""uint8"""  )
               UpperCamelCase_											=							[
                   Image.fromarray(self.rgblike_to_depthmap(__UpperCamelCase  )  ,						mode="""I;16"""  ) for image_depth in images_depth
               ]
        elif images.shape[-1] == 4:
               UpperCamelCase_											=							(images_depth * 65_535.0).astype(np.uintaa  )
               UpperCamelCase_											=							[Image.fromarray(__UpperCamelCase  ,						mode="""I;16"""  ) for image_depth in images_depth]
        else:
               raise Exception("""Not supported"""  )

        return pil_images

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = "pil"  ,						__UpperCamelCase = None  ,						):




        """simple docstring"""


        if not isinstance(__UpperCamelCase  ,						torch.Tensor  ):
               raise ValueError(
                   f'''Input for postprocessing is in incorrect format: {type(__UpperCamelCase  )}. We only support pytorch tensor'''  )
        if output_type not in ["latent", "pt", "np", "pil"]:
               UpperCamelCase_											=							(
                   f'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
                   """`pil`, `np`, `pt`, `latent`"""
               )
               deprecate("""Unsupported output_type"""  ,						"""1.0.0"""  ,						__UpperCamelCase  ,						standard_warn=__UpperCamelCase  )
               UpperCamelCase_											=							"""np"""

        if do_denormalize is None:
               UpperCamelCase_											=							[self.config.do_normalize] * image.shape[0]

        UpperCamelCase_											=							torch.stack(
            [self.denormalize(image[i]  ) if do_denormalize[i] else image[i] for i in range(image.shape[0]  )]  )

        UpperCamelCase_											=							self.pt_to_numpy(__UpperCamelCase  )

        if output_type == "np":
               if image.shape[-1] == 6:
                      UpperCamelCase_											=							np.stack([self.rgblike_to_depthmap(im[:, :, 3:]  ) for im in image]  ,						axis=0  )
               else:
                      UpperCamelCase_											=							image[:, :, :, 3:]
               return image[:, :, :, :3], image_depth

        if output_type == "pil":
               return self.numpy_to_pil(__UpperCamelCase  ), self.numpy_to_depth(__UpperCamelCase  )
        else:
               raise Exception(f'''This type {output_type} is not supported'''  )





import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


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

_A							=       {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''models.0.layer_norm''': '''feature_projection.layer_norm''',
    '''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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
_A							=       [
    '''lm_head''',
]




def 					lowerCamelCase__					(    a__	:	Tuple						,   a__	:	Any						,   a__	:	Optional[int]						,   a__	:	Optional[Any]						,   a__	:	Tuple  )		->						str:
       for attribute in key.split("""."""  ):
              UpperCamelCase_											=							getattr(a__						,   a__  )

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

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




def 					lowerCamelCase__					(    a__	:	List[str]						,   a__	:	int						,   a__	:	List[str]  )		->						int:
       UpperCamelCase_											=							[]
       UpperCamelCase_											=							fairseq_model.state_dict()

       if not is_headless:
              UpperCamelCase_											=							hf_model.dataavec_audio.feature_extractor
              UpperCamelCase_											=							hf_model.dataavec_audio.encoder.pos_conv_embed

       else:
              UpperCamelCase_											=							hf_model.feature_extractor
              UpperCamelCase_											=							hf_model.encoder.pos_conv_embed

       for name, value in fairseq_dict.items():
              UpperCamelCase_											=							False
              if "conv_layers" in name:
                     load_conv_layer(
                         a__						,   a__						,   a__						,   a__						,   )
                     UpperCamelCase_											=							True
              elif "pos_conv" in name:
                     load_pos_conv_layer(
                         a__						,   a__						,   a__						,   a__						,   )
                     UpperCamelCase_											=							True
              else:
                     for key, mapped_key in MAPPING.items():
                            if not is_headless:
                                   UpperCamelCase_											=							"""data2vec_audio.""" + 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]:
                                   UpperCamelCase_											=							True
                                   if "*" in mapped_key:
                                          UpperCamelCase_											=							name.split(a__  )[0].split("""."""  )[-2]
                                          UpperCamelCase_											=							mapped_key.replace("""*"""						,   a__  )
                                   if "weight_g" in name:
                                          UpperCamelCase_											=							"""weight_g"""
                                   elif "weight_v" in name:
                                          UpperCamelCase_											=							"""weight_v"""
                                   elif "bias" in name:
                                          UpperCamelCase_											=							"""bias"""
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          UpperCamelCase_											=							"""weight"""
                                   else:
                                          UpperCamelCase_											=							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__	:	Optional[Any]						,   a__	:	List[str]  )		->						int:
       UpperCamelCase_											=							path.split("""."""  )
       return reduce(a__						,   a__						,   a__  )




def 					lowerCamelCase__					(    a__	:	Union[str, Any]						,   a__	:	int						,   a__	:	Optional[int]						,   a__	:	int  )		->						Any:
       UpperCamelCase_											=							access_by_string(a__						,   a__  )
       UpperCamelCase_											=							hf_weight.data

       if fsq_value.shape != hf_value.shape:
              raise ValueError(f'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''  )
       UpperCamelCase_											=							fsq_value
       logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''  )




def 					lowerCamelCase__					(    a__	:	Dict						,   a__	:	str						,   a__	:	str						,   a__	:	str  )		->						int:
       UpperCamelCase_											=							full_name.split("""conv_layers."""  )[-1]
       UpperCamelCase_											=							name.split("""."""  )
       UpperCamelCase_											=							int(items[0]  )
       UpperCamelCase_											=							int(items[1]  )

       UpperCamelCase_											=							name.split("""."""  )[-1]
       if type_id == 0:
              UpperCamelCase_											=							"""conv"""
       elif type_id == 2:
              UpperCamelCase_											=							"""layer_norm"""
       else:
              unused_weights.append(a__  )
              return

       set_weights(a__						,   a__						,   a__						,   f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''  )




def 					lowerCamelCase__					(    a__	:	Tuple						,   a__	:	Any						,   a__	:	Tuple						,   a__	:	Dict  )		->						Any:
       UpperCamelCase_											=							full_name.split("""pos_conv."""  )[-1]
       UpperCamelCase_											=							name.split("""."""  )
       UpperCamelCase_											=							int(items[0]  )
       UpperCamelCase_											=							int(items[1]  )

       UpperCamelCase_											=							name.split("""."""  )[-1]
       if type_id != 0:
              unused_weights.append(a__  )
              return
       else:
              UpperCamelCase_											=							"""conv"""

       set_weights(a__						,   a__						,   a__						,   f'''layers.{layer_id}.{layer_type}.{weight_type}'''  )




@torch.no_grad()
def 					lowerCamelCase__					(    a__	:	List[Any]						,   a__	:	List[Any]						,   a__	:	Optional[int]=None						,   a__	:	List[str]=None						,   a__	:	Optional[int]=True  )		->						str:
       if config_path is not None:
              UpperCamelCase_											=							DataaVecAudioConfig.from_pretrained(a__  )
       else:
              UpperCamelCase_											=							DataaVecAudioConfig()

       if not is_finetuned:
              # Modify final_proj layer name
              UpperCamelCase_											=							DataaVecAudioModel(a__  )
              UpperCamelCase_											=							os.path.dirname(a__  )

              UpperCamelCase_											=							torch.load(a__  )
              UpperCamelCase_											=							state_dict["""model"""].pop("""final_proj.0.weight"""  )
              UpperCamelCase_											=							state_dict["""model"""].pop("""final_proj.0.bias"""  )
              UpperCamelCase_											=							os.path.join(a__						,   """converted.pt"""  )
              torch.save(a__						,   a__  )
       else:
              UpperCamelCase_											=							DataaVecAudioForCTC(a__  )
              UpperCamelCase_											=							checkpoint_path

       def load_dataavec(a__	:	Optional[int]  ):
              UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							fairseq.checkpoint_utils.load_model_ensemble_and_task([path]  )
              return model[0].eval()

       UpperCamelCase_											=							load_dataavec(a__  )

       recursively_load_weights(a__						,   a__						,   not is_finetuned  )

       UpperCamelCase_											=							WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""  )

       UpperCamelCase_											=							load_dataset("""patrickvonplaten/librispeech_asr_dummy"""						,   """clean"""						,   split="""validation"""  )
       UpperCamelCase_											=							[x["""array"""] for x in ds[:4]["""audio"""]]

       UpperCamelCase_											=							processor(a__						,   return_tensors="""pt"""						,   padding=a__  )

       UpperCamelCase_											=							inputs.input_values
       UpperCamelCase_											=							inputs.attention_mask
       #    input_values = inputs.input_values[:, :-1]
       #    attention_mask = inputs.attention_mask[:, :-1]

       hf_wavavec.eval()
       model.eval()
       if is_finetuned:
              UpperCamelCase_											=							model(source=a__						,   padding_mask=(1 - attention_mask)						,   mask=a__						,   features_only=a__  )[
                  """encoder_out"""
              ].transpose(0						,   1  )
              UpperCamelCase_											=							hf_wavavec(a__						,   attention_mask=a__  )["""logits"""]

              UpperCamelCase_											=							torch.argmax(a__						,   dim=-1  )
              UpperCamelCase_											=							processor.batch_decode(a__  )

              print(f'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''  )
       else:
              UpperCamelCase_											=							model(source=a__						,   padding_mask=(1 - attention_mask)						,   mask=a__						,   features_only=a__  )[
                  """layer_results"""
              ][-1][0].transpose(0						,   1  )
              UpperCamelCase_											=							hf_wavavec(a__						,   attention_mask=a__  )["""last_hidden_state"""]

       print(our_output.shape						,   their_output.shape  )
       UpperCamelCase_											=							torch.max(torch.abs(our_output - their_output  )  ).item()
       print(f'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
       UpperCamelCase_											=							torch.allclose(a__						,   a__						,   atol=1e-3  )
       print("""Do both models output the same tensors?"""						,   """🔥""" if success else """💩"""  )
       if not success:
              raise Exception("""Something went wRoNg"""  )

       hf_wavavec.save_pretrained(a__  )

       if is_finetuned:
              processor.save_pretrained(a__  )
       else:
              processor.feature_extractor.save_pretrained(a__  )


if __name__ == "__main__":
      _A							=       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'''
      )
      _A							=       parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )



