


"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

lowercase_			     =  {
    "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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
lowercase_			     =  [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     Optional[int]    ,					lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     Any			) ->      List[str]:
		for attribute in key.split('''.'''			):
				if is_finetuned:
						if attribute in ["quantizer", "project_q", "project_hid"]:
								# those layers are only relevant for pretraining and should be dropped
								return

						if attribute == "ctc_proj":
								# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
								__a           =			'''lm_head'''

				__a           =			getattr(lowerCAmelCase__    ,					lowerCAmelCase__			)

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

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

def        lowercase							(		lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     Tuple			) ->      Union[str, Any]:
		__a           =			[]
		__a           =			fairseq_model.state_dict()

		__a           =			hf_model.unispeech.feature_extractor

		for name, value in fairseq_dict.items():
				__a           =			False
				if "conv_layers" in name:
						load_conv_layer(
						    lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					hf_model.config.feat_extract_norm == '''group'''    ,					)
						__a           =			True
				else:
						for key, mapped_key in MAPPING.items():
								__a           =			'''unispeech.''' + 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]:
										__a           =			True
										if "*" in mapped_key:
												__a           =			name.split(lowerCAmelCase__			)[0].split('''.'''			)[-2]
												__a           =			mapped_key.replace('''*'''    ,					lowerCAmelCase__			)
										if "weight_g" in name:
												__a           =			'''weight_g'''
										elif "weight_v" in name:
												__a           =			'''weight_v'''
										elif "bias" in name:
												__a           =			'''bias'''
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												__a           =			'''weight'''
										else:
												__a           =			None
										set_recursively(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
								continue
				if not is_used:
						unused_weights.append(lowerCAmelCase__			)

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

def        lowercase							(		lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     Tuple			) ->      Dict:
		__a           =			full_name.split('''conv_layers.'''			)[-1]
		__a           =			name.split('''.'''			)
		__a           =			int(items[0]			)
		__a           =			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           =			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           =			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           =			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           =			value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
		else:
				unused_weights.append(lowerCAmelCase__			)

@torch.no_grad()
def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     List[Any]    ,					lowerCAmelCase__ :     Union[str, Any]=None    ,					lowerCAmelCase__ :     Dict=None    ,					lowerCAmelCase__ :     List[str]=True			) ->      Tuple:
		if config_path is not None:
				__a           =			UniSpeechConfig.from_pretrained(lowerCAmelCase__			)
		else:
				__a           =			UniSpeechConfig()

		if is_finetuned:
				if dict_path:
						__a           =			Dictionary.load_from_json(lowerCAmelCase__			)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						__a           =			target_dict.pad_index
						__a           =			target_dict.bos_index
						__a           =			target_dict.eos_index
						__a           =			len(target_dict.symbols			)
						__a           =			os.path.join(lowerCAmelCase__    ,					'''vocab.json'''			)
						if not os.path.isdir(lowerCAmelCase__			):
								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowerCAmelCase__			)			)
								return
						os.makedirs(lowerCAmelCase__    ,					exist_ok=lowerCAmelCase__			)
						__a           =			target_dict.indices

						# fairseq has the <pad> and <s> switched
						__a           =			42
						__a           =			43
						with open(lowerCAmelCase__    ,					'''w'''    ,					encoding='''utf-8'''			) as vocab_handle:
								json.dump(lowerCAmelCase__    ,					lowerCAmelCase__			)
						__a           =			WavaVecaPhonemeCTCTokenizer(
						    lowerCAmelCase__    ,					unk_token=target_dict.unk_word    ,					pad_token=target_dict.pad_word    ,					bos_token=target_dict.bos_word    ,					eos_token=target_dict.eos_word    ,					word_delimiter_token='''|'''    ,					do_lower_case=lowerCAmelCase__    ,					)
						__a           =			True if config.feat_extract_norm == '''layer''' else False
						__a           =			WavaVecaFeatureExtractor(
						    feature_size=1    ,					sampling_rate=16000    ,					padding_value=0    ,					do_normalize=lowerCAmelCase__    ,					return_attention_mask=lowerCAmelCase__    ,					)
						__a           =			WavaVecaProcessor(feature_extractor=lowerCAmelCase__    ,					tokenizer=lowerCAmelCase__			)
						processor.save_pretrained(lowerCAmelCase__			)

				__a           =			UniSpeechForCTC(lowerCAmelCase__			)
		else:
				__a           =			UniSpeechForPreTraining(lowerCAmelCase__			)

		if is_finetuned:
				__a           =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]    ,					arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''			)[:-1]			), '''w2v_path''': checkpoint_path}			)
		else:
				__a           =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

		__a           =			model[0].eval()

		recursively_load_weights(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)

		hf_unispeech.save_pretrained(lowerCAmelCase__			)


if __name__ == "__main__":
				lowercase_			     =  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"
				)
				lowercase_			     =  parser.parse_args()
				convert_unispeech_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)










import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

UpperCamelCase								=	collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
UpperCamelCase								=	"""https://storage.googleapis.com/cvdf-datasets/mnist/"""


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Any		     =					numpy.dtype(numpy.uintaa       ).newbyteorder('''>'''       )
  return numpy.frombuffer(bytestream.read(4       ) ,			dtype=SCREAMING_SNAKE_CASE       )[0]


@deprecated(SCREAMING_SNAKE_CASE ,			'''Please use tf.data to implement this functionality.'''       )
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  print('''Extracting''' ,			f.name       )
  with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE       ) as bytestream:
    A_      :				Union[str, Any]		     =					_readaa(SCREAMING_SNAKE_CASE       )
    if magic != 2_051:
      raise ValueError(
          '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)       )
    A_      :				str		     =					_readaa(SCREAMING_SNAKE_CASE       )
    A_      :				Tuple		     =					_readaa(SCREAMING_SNAKE_CASE       )
    A_      :				Dict		     =					_readaa(SCREAMING_SNAKE_CASE       )
    A_      :				Tuple		     =					bytestream.read(rows * cols * num_images       )
    A_      :				Dict		     =					numpy.frombuffer(SCREAMING_SNAKE_CASE ,			dtype=numpy.uinta       )
    A_      :				Union[str, Any]		     =					data.reshape(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			1       )
    return data


@deprecated(SCREAMING_SNAKE_CASE ,			'''Please use tf.one_hot on tensors.'''       )
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				Optional[Any]		     =					labels_dense.shape[0]
  A_      :				List[str]		     =					numpy.arange(SCREAMING_SNAKE_CASE       ) * num_classes
  A_      :				int		     =					numpy.zeros((num_labels, num_classes)       )
  A_      :				Tuple		     =					1
  return labels_one_hot


@deprecated(SCREAMING_SNAKE_CASE ,			'''Please use tf.data to implement this functionality.'''       )
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=False ,			SCREAMING_SNAKE_CASE=10       ):
  print('''Extracting''' ,			f.name       )
  with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE       ) as bytestream:
    A_      :				Any		     =					_readaa(SCREAMING_SNAKE_CASE       )
    if magic != 2_049:
      raise ValueError(
          '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)       )
    A_      :				Tuple		     =					_readaa(SCREAMING_SNAKE_CASE       )
    A_      :				List[Any]		     =					bytestream.read(SCREAMING_SNAKE_CASE       )
    A_      :				int		     =					numpy.frombuffer(SCREAMING_SNAKE_CASE ,			dtype=numpy.uinta       )
    if one_hot:
      return _dense_to_one_hot(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
    return labels






class   _lowerCamelCase :




       """simple docstring"""



       @deprecated(
           _SCREAMING_SNAKE_CASE				,       '''Please use alternatives such as official/mnist/_DataSet.py'''
           ''' from tensorflow/models.'''				,       )
       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=dtypes.floataa				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=None				,       )->Tuple:




         '''simple docstring'''





         A_     ,       A_      :				List[str]		     =					random_seed.get_seed(_SCREAMING_SNAKE_CASE		)
         # If op level seed is not set, use whatever graph level seed is returned
         numpy.random.seed(seeda if seed is None else seeda		)
         A_      :				Tuple		     =					dtypes.as_dtype(_SCREAMING_SNAKE_CASE		).base_dtype
         if dtype not in (dtypes.uinta, dtypes.floataa):
           raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype		)
         if fake_data:
           A_      :				Optional[Any]		     =					1_0000
           A_      :				List[str]		     =					one_hot
         else:
           assert (
               images.shape[0] == labels.shape[0]
           ), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
           A_      :				List[str]		     =					images.shape[0]

           # Convert shape from [num examples, rows, columns, depth]
           # to [num examples, rows*columns] (assuming depth == 1)
           if reshape:
             assert images.shape[3] == 1
             A_      :				int		     =					images.reshape(
                 images.shape[0]				,       images.shape[1] * images.shape[2]		)
           if dtype == dtypes.floataa:
             # Convert from [0, 255] -> [0.0, 1.0].
             A_      :				Optional[int]		     =					images.astype(numpy.floataa		)
             A_      :				List[str]		     =					numpy.multiply(_SCREAMING_SNAKE_CASE				,       1.0 / 2_5_5.0		)
         A_      :				int		     =					images
         A_      :				Optional[int]		     =					labels
         A_      :				List[str]		     =					0
         A_      :				List[Any]		     =					0



       @property
       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         return self._images



       @property
       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         return self._labels



       @property
       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         return self._num_examples



       @property
       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         return self._epochs_completed







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=True		)->str:




         '''simple docstring'''





         if fake_data:
           A_      :				Any		     =					[1] * 784
           A_      :				int		     =					[1] + [0] * 9 if self.one_hot else 0
           return (
               [fake_image for _ in range(_SCREAMING_SNAKE_CASE		)],
               [fake_label for _ in range(_SCREAMING_SNAKE_CASE		)],
           )
         A_      :				Any		     =					self._index_in_epoch
         # Shuffle for the first epoch
         if self._epochs_completed == 0 and start == 0 and shuffle:
           A_      :				Any		     =					numpy.arange(self._num_examples		)
           numpy.random.shuffle(_SCREAMING_SNAKE_CASE		)
           A_      :				Optional[Any]		     =					self.images[perma]
           A_      :				List[Any]		     =					self.labels[perma]
         # Go to the next epoch
         if start + batch_size > self._num_examples:
           # Finished epoch
           self._epochs_completed += 1
           # Get the rest examples in this epoch
           A_      :				Tuple		     =					self._num_examples - start
           A_      :				Union[str, Any]		     =					self._images[start : self._num_examples]
           A_      :				List[Any]		     =					self._labels[start : self._num_examples]
           # Shuffle the data
           if shuffle:
             A_      :				List[Any]		     =					numpy.arange(self._num_examples		)
             numpy.random.shuffle(_SCREAMING_SNAKE_CASE		)
             A_      :				Tuple		     =					self.images[perm]
             A_      :				int		     =					self.labels[perm]
           # Start next epoch
           A_      :				Tuple		     =					0
           A_      :				Optional[Any]		     =					batch_size - rest_num_examples
           A_      :				Tuple		     =					self._index_in_epoch
           A_      :				Union[str, Any]		     =					self._images[start:end]
           A_      :				Any		     =					self._labels[start:end]
           return (
               numpy.concatenate((images_rest_part, images_new_part)				,       axis=0		),
               numpy.concatenate((labels_rest_part, labels_new_part)				,       axis=0		),
           )
         else:
           self._index_in_epoch += batch_size
           A_      :				List[str]		     =					self._index_in_epoch
           return self._images[start:end], self._labels[start:end]




@deprecated(SCREAMING_SNAKE_CASE ,			'''Please write your own downloading logic.'''       )
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  if not gfile.Exists(SCREAMING_SNAKE_CASE       ):
    gfile.MakeDirs(SCREAMING_SNAKE_CASE       )
  A_      :				str		     =					os.path.join(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  if not gfile.Exists(SCREAMING_SNAKE_CASE       ):
    urllib.request.urlretrieve(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )  # noqa: S310
    with gfile.GFile(SCREAMING_SNAKE_CASE       ) as f:
      A_      :				Dict		     =					f.size()
    print('''Successfully downloaded''' ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			'''bytes.'''       )
  return filepath







@deprecated(
    SCREAMING_SNAKE_CASE ,			'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''       )
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=False ,			SCREAMING_SNAKE_CASE=False ,			SCREAMING_SNAKE_CASE=dtypes.floataa ,			SCREAMING_SNAKE_CASE=True ,			SCREAMING_SNAKE_CASE=5_000 ,			SCREAMING_SNAKE_CASE=None ,			SCREAMING_SNAKE_CASE=DEFAULT_SOURCE_URL ,			):
  if fake_data:

    def fake():
      return _DataSet(
          [] ,			[] ,			fake_data=SCREAMING_SNAKE_CASE ,			one_hot=SCREAMING_SNAKE_CASE ,			dtype=SCREAMING_SNAKE_CASE ,			seed=SCREAMING_SNAKE_CASE       )

    A_      :				List[str]		     =					fake()
    A_      :				Tuple		     =					fake()
    A_      :				Union[str, Any]		     =					fake()
    return _Datasets(train=SCREAMING_SNAKE_CASE ,			validation=SCREAMING_SNAKE_CASE ,			test=SCREAMING_SNAKE_CASE       )

  if not source_url:  # empty string check
    A_      :				List[str]		     =					DEFAULT_SOURCE_URL

  A_      :				List[Any]		     =					'''train-images-idx3-ubyte.gz'''
  A_      :				Tuple		     =					'''train-labels-idx1-ubyte.gz'''
  A_      :				Optional[int]		     =					'''t10k-images-idx3-ubyte.gz'''
  A_      :				Any		     =					'''t10k-labels-idx1-ubyte.gz'''

  A_      :				Dict		     =					_maybe_download(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			source_url + train_images_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE ,			'''rb'''       ) as f:
    A_      :				Optional[int]		     =					_extract_images(SCREAMING_SNAKE_CASE       )

  A_      :				List[str]		     =					_maybe_download(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			source_url + train_labels_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE ,			'''rb'''       ) as f:
    A_      :				Tuple		     =					_extract_labels(SCREAMING_SNAKE_CASE ,			one_hot=SCREAMING_SNAKE_CASE       )

  A_      :				Dict		     =					_maybe_download(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			source_url + test_images_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE ,			'''rb'''       ) as f:
    A_      :				List[str]		     =					_extract_images(SCREAMING_SNAKE_CASE       )

  A_      :				int		     =					_maybe_download(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			source_url + test_labels_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE ,			'''rb'''       ) as f:
    A_      :				Any		     =					_extract_labels(SCREAMING_SNAKE_CASE ,			one_hot=SCREAMING_SNAKE_CASE       )

  if not 0 <= validation_size <= len(SCREAMING_SNAKE_CASE       ):
    A_      :				str		     =					(
        '''Validation size should be between 0 and '''
        f'''{len(SCREAMING_SNAKE_CASE       )}. Received: {validation_size}.'''
    )
    raise ValueError(SCREAMING_SNAKE_CASE       )

  A_      :				Optional[Any]		     =					train_images[:validation_size]
  A_      :				Optional[Any]		     =					train_labels[:validation_size]
  A_      :				Any		     =					train_images[validation_size:]
  A_      :				Any		     =					train_labels[validation_size:]

  A_      :				Optional[Any]		     =					{'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

  A_      :				List[str]		     =					_DataSet(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			**SCREAMING_SNAKE_CASE       )
  A_      :				Dict		     =					_DataSet(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			**SCREAMING_SNAKE_CASE       )
  A_      :				Dict		     =					_DataSet(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			**SCREAMING_SNAKE_CASE       )

  return _Datasets(train=SCREAMING_SNAKE_CASE ,			validation=SCREAMING_SNAKE_CASE ,			test=SCREAMING_SNAKE_CASE       )




