"""simple docstring"""


import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
__UpperCamelCase									=			logging.get_logger('''transformers.models.speecht5''')

__UpperCamelCase									=			{
    '''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
    '''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
    '''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
    '''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
__UpperCamelCase									=			{
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
__UpperCamelCase									=			{
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
    '''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
    '''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
    '''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
__UpperCamelCase									=			{
    '''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
    '''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
    '''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
    '''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
    '''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
    '''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
    '''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
    '''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
__UpperCamelCase									=			{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
__UpperCamelCase									=			{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
__UpperCamelCase									=			{
    '''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
    '''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
    '''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
    '''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
    '''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
    '''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
    '''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
    '''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
    '''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
__UpperCamelCase									=			{
    '''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
    '''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
    '''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
    '''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
    '''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
    '''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
    '''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
    '''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
    '''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
    '''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
    '''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
    '''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
    '''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
__UpperCamelCase									=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__UpperCamelCase									=			{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__UpperCamelCase									=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__UpperCamelCase									=			[]
__UpperCamelCase									=			[
    '''encoder.version''',
    '''encoder.layers.*.norm_k.weight''',
    '''encoder.layers.*.norm_k.bias''',
    '''decoder.version''',
    '''decoder.layers.*.norm_k.weight''',
    '''decoder.layers.*.norm_k.bias''',
    '''decoder.pos_emb.pe_k''',
    '''speech_encoder_prenet.embed_positions._float_tensor''',
    '''text_decoder_prenet.embed_positions._float_tensor''',
]
__UpperCamelCase									=			IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
__UpperCamelCase									=			IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
__UpperCamelCase									=			IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)				->      str:
     for attribute in key.split('.'					):
          snake_case_					       =					getattr(UpperCAmelCase						,					UpperCAmelCase					)

     if weight_type is not None:
          snake_case_					       =					getattr(UpperCAmelCase						,					UpperCAmelCase					).shape
     else:
          snake_case_					       =					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":
          snake_case_					       =					value
     elif weight_type == "weight_g":
          snake_case_					       =					value
     elif weight_type == "weight_v":
          snake_case_					       =					value
     elif weight_type == "bias":
          snake_case_					       =					value
     elif weight_type == "running_mean":
          snake_case_					       =					value
     elif weight_type == "running_var":
          snake_case_					       =					value
     elif weight_type == "num_batches_tracked":
          snake_case_					       =					value
     else:
          snake_case_					       =					value

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







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase					)				->      List[str]:
     for key in ignore_keys:
          if key.endswith('.*'					):
               if name.startswith(key[:-1]					):
                    return True
          elif ".*." in key:
               snake_case_       ,	snake_case_					       =					key.split('.*.'					)
               if prefix in name and suffix in name:
                    return True
          elif key in name:
               return True
     return False







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)				->      Any:
     snake_case_					       =					[]

     if task == "s2t":
          snake_case_					       =					hf_model.speechta.encoder.prenet.feature_encoder
          snake_case_					       =					MAPPING_S2T
          snake_case_					       =					IGNORE_KEYS_S2T
     elif task == "t2s":
          snake_case_					       =					None
          snake_case_					       =					MAPPING_T2S
          snake_case_					       =					IGNORE_KEYS_T2S
     elif task == "s2s":
          snake_case_					       =					hf_model.speechta.encoder.prenet.feature_encoder
          snake_case_					       =					MAPPING_S2S
          snake_case_					       =					IGNORE_KEYS_S2S
     else:
          raise ValueError(f'Unsupported task: {task}'					)

     for name, value in fairseq_dict.items():
          if should_ignore(UpperCAmelCase						,					UpperCAmelCase					):
               logger.info(f'{name} was ignored'					)
               continue

          snake_case_					       =					False
          if "conv_layers" in name:
               load_conv_layer(
                   UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					hf_model.config.feat_extract_norm == 'group'						,					)
               snake_case_					       =					True
          else:
               for key, mapped_key in MAPPING.items():
                    # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

                    if "*" in key:
                         snake_case_       ,	snake_case_					       =					key.split('.*.'					)
                         if prefix in name and suffix in name:
                              snake_case_					       =					suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                    if key in name:
                         snake_case_					       =					True
                         if "*" in mapped_key:
                              snake_case_					       =					name.split(UpperCAmelCase					)[0].split('.'					)[-2]
                              snake_case_					       =					mapped_key.replace('*'						,					UpperCAmelCase					)
                         if "weight_g" in name:
                              snake_case_					       =					'weight_g'
                         elif "weight_v" in name:
                              snake_case_					       =					'weight_v'
                         elif "bias" in name:
                              snake_case_					       =					'bias'
                         elif "weight" in name:
                              snake_case_					       =					'weight'
                         elif "running_mean" in name:
                              snake_case_					       =					'running_mean'
                         elif "running_var" in name:
                              snake_case_					       =					'running_var'
                         elif "num_batches_tracked" in name:
                              snake_case_					       =					'num_batches_tracked'
                         else:
                              snake_case_					       =					None
                         set_recursively(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)
                    continue
          if not is_used:
               unused_weights.append(UpperCAmelCase					)

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







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)				->      List[str]:
     snake_case_					       =					full_name.split('conv_layers.'					)[-1]
     snake_case_					       =					name.split('.'					)
     snake_case_					       =					int(items[0]					)
     snake_case_					       =					int(items[1]					)

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'					)
               snake_case_					       =					value
               logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'					)
               snake_case_					       =					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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'					)
               snake_case_					       =					value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'					)
               snake_case_					       =					value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
     else:
          unused_weights.append(UpperCAmelCase					)







@torch.no_grad()
def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase=None						,					UpperCAmelCase=None						,					UpperCAmelCase=None						,					)				->      Dict:
     if config_path is not None:
          snake_case_					       =					SpeechTaConfig.from_pretrained(UpperCAmelCase					)
     else:
          snake_case_					       =					SpeechTaConfig()

     if task == "s2t":
          snake_case_					       =					config.max_text_positions
          snake_case_					       =					SpeechTaForSpeechToText(UpperCAmelCase					)
     elif task == "t2s":
          snake_case_					       =					1876
          snake_case_					       =					600
          snake_case_					       =					config.max_speech_positions
          snake_case_					       =					SpeechTaForTextToSpeech(UpperCAmelCase					)
     elif task == "s2s":
          snake_case_					       =					1876
          snake_case_					       =					config.max_speech_positions
          snake_case_					       =					SpeechTaForSpeechToSpeech(UpperCAmelCase					)
     else:
          raise ValueError(f'Unknown task name: {task}'					)

     if vocab_path:
          snake_case_					       =					SpeechTaTokenizer(UpperCAmelCase						,					model_max_length=config.max_text_positions					)

          # Mask token behaves like a normal word, i.e. include the space before it
          snake_case_					       =					AddedToken('<mask>'						,					lstrip=UpperCAmelCase						,					rstrip=UpperCAmelCase					)
          snake_case_					       =					mask_token
          tokenizer.add_special_tokens({'mask_token': mask_token}					)
          tokenizer.add_tokens(['<ctc_blank>']					)

     snake_case_					       =					SpeechTaFeatureExtractor()
     snake_case_					       =					SpeechTaProcessor(tokenizer=UpperCAmelCase						,					feature_extractor=UpperCAmelCase					)
     processor.save_pretrained(UpperCAmelCase					)

     snake_case_					       =					torch.load(UpperCAmelCase					)
     recursively_load_weights(fairseq_checkpoint['model']						,					UpperCAmelCase						,					UpperCAmelCase					)

     model.save_pretrained(UpperCAmelCase					)

     if repo_id:
          print('Pushing to the hub...'					)
          processor.push_to_hub(UpperCAmelCase					)
          model.push_to_hub(UpperCAmelCase					)


if __name__ == "__main__":
    __UpperCamelCase									=			argparse.ArgumentParser()
    parser.add_argument(
        '''--task''',
        default='''s2t''',
        type=str,
        help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
    )
    parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
    parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
    parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
    parser.add_argument(
        '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
    )
    parser.add_argument(
        '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
    )

    __UpperCamelCase									=			parser.parse_args()
    convert_speechta_checkpoint(
        args.task,
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.vocab_path,
        args.push_to_hub,
    )





import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
    import PIL.Image

    from .features import FeatureType


A      :    Optional[List[str]]		=					None
A      :    str		=					"<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
A      :    str		=					[
    np.dtype("|b1"),
    np.dtype("|u1"),
    np.dtype("<u2"),
    np.dtype(">u2"),
    np.dtype("<i2"),
    np.dtype(">i2"),
    np.dtype("<u4"),
    np.dtype(">u4"),
    np.dtype("<i4"),
    np.dtype(">i4"),
    np.dtype("<f4"),
    np.dtype(">f4"),
    np.dtype("<f8"),
    np.dtype(">f8"),
]







@dataclass
class 			_lowercase :


       """simple docstring"""




       A__											=			True
       A__											=			None
       # Automatically constructed
       A__											=			"PIL.Image.Image"
       A__											=			pa.struct({"bytes": pa.binary(), "path": pa.string()})
       A__											=			field(default="Image"							,  init=lowercase__							,  repr=lowercase__)





       def __call__(   self							:			Any       ):


             '''simple docstring'''



             return self.pa_type





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]       ):


             '''simple docstring'''



             if config.PIL_AVAILABLE:
                   import PIL.Image
             else:
                   raise ImportError("To support encoding images, please install 'Pillow'."       )

             if isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                   lowerCamelCase__		:				str    =  np.array(__lowerCamelCase       )

             if isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                   return {"path": value, "bytes": None}
             elif isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                   return {"path": None, "bytes": value}
             elif isinstance(__lowerCamelCase					,      np.ndarray       ):
                   # convert the image array to PNG/TIFF bytes
                   return encode_np_array(__lowerCamelCase       )
             elif isinstance(__lowerCamelCase					,      PIL.Image.Image       ):
                   # convert the PIL image to bytes (default format is PNG/TIFF)
                   return encode_pil_image(__lowerCamelCase       )
             elif value.get("path"       ) is not None and os.path.isfile(value["path"]       ):
                   # we set "bytes": None to not duplicate the data if they're already available locally
                   return {"bytes": None, "path": value.get("path"       )}
             elif value.get("bytes"       ) is not None or value.get("path"       ) is not None:
                   # store the image bytes, and path is used to infer the image format using the file extension
                   return {"bytes": value.get("bytes"       ), "path": value.get("path"       )}
             else:
                   raise ValueError(
                       f"An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."       )





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			dict					,      __lowerCamelCase							:			List[Any]=None       ):


             '''simple docstring'''



             if not self.decode:
                   raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead."       )

             if config.PIL_AVAILABLE:
                   import PIL.Image
             else:
                   raise ImportError("To support decoding images, please install 'Pillow'."       )

             if token_per_repo_id is None:
                   lowerCamelCase__		:				Union[str, Any]    =  {}

             lowerCamelCase__    ,   lowerCamelCase__		:				Optional[int]    =  value["path"], value["bytes"]
             if bytes_ is None:
                   if path is None:
                         raise ValueError(f"An image should have one of 'path' or 'bytes' but both are None in {value}."       )
                   else:
                         if is_local_path(__lowerCamelCase       ):
                               lowerCamelCase__		:				Union[str, Any]    =  PIL.Image.open(__lowerCamelCase       )
                         else:
                               lowerCamelCase__		:				Tuple    =  path.split("::"       )[-1]
                               try:
                                     lowerCamelCase__		:				str    =  string_to_dict(__lowerCamelCase					,      config.HUB_DATASETS_URL       )["repo_id"]
                                     lowerCamelCase__		:				Any    =  token_per_repo_id.get(__lowerCamelCase       )
                               except ValueError:
                                     lowerCamelCase__		:				int    =  None
                               with xopen(__lowerCamelCase					,      "rb"					,      use_auth_token=__lowerCamelCase       ) as f:
                                     lowerCamelCase__		:				List[str]    =  BytesIO(f.read()       )
                               lowerCamelCase__		:				Optional[int]    =  PIL.Image.open(bytes_       )
             else:
                   lowerCamelCase__		:				Dict    =  PIL.Image.open(BytesIO(bytes_       )       )
             image.load()  # to avoid "Too many open files" errors
             return image





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             from .features import Value

             return (
                 self
                 if self.decode
                 else {
                     "bytes": Value("binary"       ),
                     "path": Value("string"       ),
                 }
             )





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			Union[pa.StringArray, pa.StructArray, pa.ListArray]       ):


             '''simple docstring'''



             if pa.types.is_string(storage.type       ):
                   lowerCamelCase__		:				Dict    =  pa.array([None] * len(__lowerCamelCase       )					,      type=pa.binary()       )
                   lowerCamelCase__		:				List[str]    =  pa.StructArray.from_arrays([bytes_array, storage]					,      ["bytes", "path"]					,      mask=storage.is_null()       )
             elif pa.types.is_binary(storage.type       ):
                   lowerCamelCase__		:				List[Any]    =  pa.array([None] * len(__lowerCamelCase       )					,      type=pa.string()       )
                   lowerCamelCase__		:				Any    =  pa.StructArray.from_arrays([storage, path_array]					,      ["bytes", "path"]					,      mask=storage.is_null()       )
             elif pa.types.is_struct(storage.type       ):
                   if storage.type.get_field_index("bytes"       ) >= 0:
                         lowerCamelCase__		:				Dict    =  storage.field("bytes"       )
                   else:
                         lowerCamelCase__		:				Optional[int]    =  pa.array([None] * len(__lowerCamelCase       )					,      type=pa.binary()       )
                   if storage.type.get_field_index("path"       ) >= 0:
                         lowerCamelCase__		:				Dict    =  storage.field("path"       )
                   else:
                         lowerCamelCase__		:				Dict    =  pa.array([None] * len(__lowerCamelCase       )					,      type=pa.string()       )
                   lowerCamelCase__		:				int    =  pa.StructArray.from_arrays([bytes_array, path_array]					,      ["bytes", "path"]					,      mask=storage.is_null()       )
             elif pa.types.is_list(storage.type       ):
                   lowerCamelCase__		:				Union[str, Any]    =  pa.array(
                       [encode_np_array(np.array(__lowerCamelCase       )       )["bytes"] if arr is not None else None for arr in storage.to_pylist()]					,      type=pa.binary()					,      )
                   lowerCamelCase__		:				Dict    =  pa.array([None] * len(__lowerCamelCase       )					,      type=pa.string()       )
                   lowerCamelCase__		:				Dict    =  pa.StructArray.from_arrays(
                       [bytes_array, path_array]					,      ["bytes", "path"]					,      mask=bytes_array.is_null()       )
             return array_cast(__lowerCamelCase					,      self.pa_type       )





       def 						lowerCAmelCase     (   self							:			int					,      __lowerCamelCase							:			pa.StructArray       ):


             '''simple docstring'''



             @no_op_if_value_is_null
             def path_to_bytes(__lowerCamelCase							:			Union[str, Any]       ):
                   with xopen(__lowerCamelCase					,      "rb"       ) as f:
                         lowerCamelCase__		:				str    =  f.read()
                   return bytes_

             lowerCamelCase__		:				List[Any]    =  pa.array(
                 [
                     (path_to_bytes(x["path"]       ) if x["bytes"] is None else x["bytes"]) if x is not None else None
                     for x in storage.to_pylist()
                 ]					,      type=pa.binary()					,      )
             lowerCamelCase__		:				Optional[int]    =  pa.array(
                 [os.path.basename(__lowerCamelCase       ) if path is not None else None for path in storage.field("path"       ).to_pylist()]					,      type=pa.string()					,      )
             lowerCamelCase__		:				Tuple    =  pa.StructArray.from_arrays([bytes_array, path_array]					,      ["bytes", "path"]					,      mask=bytes_array.is_null()       )
             return array_cast(__lowerCamelCase					,      self.pa_type       )



def       lowercase_		(		):

      """simple docstring"""




      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("To support encoding images, please install 'Pillow'."						)

      global _IMAGE_COMPRESSION_FORMATS
      if _IMAGE_COMPRESSION_FORMATS is None:
            PIL.Image.init()
            lowerCamelCase__		:				List[str]    =  list(set(PIL.Image.OPEN.keys()						) & set(PIL.Image.SAVE.keys()						)						)
      return _IMAGE_COMPRESSION_FORMATS

def       lowercase_		(		_A			:    "PIL.Image.Image"						):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  BytesIO()
      if image.format in list_image_compression_formats():
            lowerCamelCase__		:				int    =  image.format
      else:
            lowerCamelCase__		:				int    =  "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
      image.save(_A    ,					format=_A						)
      return buffer.getvalue()

def       lowercase_		(		_A			:    "PIL.Image.Image"						):

      """simple docstring"""




      if hasattr(_A    ,					"filename"						) and image.filename != "":
            return {"path": image.filename, "bytes": None}
      else:
            return {"path": None, "bytes": image_to_bytes(_A						)}

def       lowercase_		(		_A			:    np.ndarray						):

      """simple docstring"""




      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("To support encoding images, please install 'Pillow'."						)

      lowerCamelCase__		:				int    =  array.dtype
      lowerCamelCase__		:				List[str]    =  dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
      lowerCamelCase__		:				List[str]    =  dtype.kind
      lowerCamelCase__		:				Optional[Any]    =  dtype.itemsize

      lowerCamelCase__		:				Dict    =  None

      # Multi-channel array case (only np.dtype("|u1") is allowed)
      if array.shape[2:]:
            lowerCamelCase__		:				List[Any]    =  np.dtype("|u1"						)
            if dtype_kind not in ["u", "i"]:
                  raise TypeError(
                      F"Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."						)
            if dtype is not dest_dtype:
                  warnings.warn(F"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"						)
    # Exact match
      elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
            lowerCamelCase__		:				Any    =  dtype
      else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
            while dtype_itemsize >= 1:
                  lowerCamelCase__		:				Optional[Any]    =  dtype_byteorder + dtype_kind + str(_A						)
                  lowerCamelCase__		:				int    =  np.dtype(_A						)
                  if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                        warnings.warn(F"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"						)
                        break
                  else:
                        dtype_itemsize //= 2

      if dest_dtype is None:
            raise TypeError(
                F"Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"						)
      lowerCamelCase__		:				List[Any]    =  PIL.Image.fromarray(array.astype(_A						)						)
      return {"path": None, "bytes": image_to_bytes(_A						)}


def       lowercase_		(		_A			:    Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]						):

      """simple docstring"""




      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("To support encoding images, please install 'Pillow'."						)

      if objs:
            lowerCamelCase__    ,   lowerCamelCase__		:				int    =  first_non_null_value(_A						)
            if isinstance(_A    ,					_A						):
                  return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
            if isinstance(_A    ,					np.ndarray						):
                  lowerCamelCase__		:				Optional[Any]    =  no_op_if_value_is_null(_A						)
                  return [obj_to_image_dict_func(_A						) for obj in objs]
            elif isinstance(_A    ,					PIL.Image.Image						):
                  lowerCamelCase__		:				int    =  no_op_if_value_is_null(_A						)
                  return [obj_to_image_dict_func(_A						) for obj in objs]
            else:
                  return objs
      else:
            return objs


