





'''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()
a        =						logging.get_logger("transformers.models.speecht5")

a        =						{
    "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",
}
a        =						{
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
a        =						{
    "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",
}
a        =						{
    "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",
}
a        =						{
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
a        =						{
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
a        =						{
    "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",
}
a        =						{
    "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",
}
a        =						{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
a        =						{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a        =						{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a        =						[]
a        =						[
    "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",
]
a        =						IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
a        =						IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
a        =						IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Optional[int]:

   '''simple docstring'''







   for attribute in key.split("""."""      ):
      __SCREAMING_SNAKE_CASE      							=       getattr(__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE      )

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

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

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   for key in ignore_keys:
      if key.endswith(""".*"""      ):
         if name.startswith(key[:-1]      ):
            return True
      elif ".*." in key:
         __SCREAMING_SNAKE_CASE      							=       key.split(""".*."""      )
         if prefix in name and suffix in name:
            return True
      elif key in name:
         return True
   return False

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       []

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

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

      __SCREAMING_SNAKE_CASE      							=       False
      if "conv_layers" in name:
         load_conv_layer(
             __SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	hf_model.config.feat_extract_norm == """group"""							,	)
         __SCREAMING_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:
               __SCREAMING_SNAKE_CASE      							=       key.split(""".*."""      )
               if prefix in name and suffix in name:
                  __SCREAMING_SNAKE_CASE      							=       suffix

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

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

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					List[str]:

   '''simple docstring'''







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

@torch.no_grad()
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=None							,	__UpperCAmelCase=None							,	__UpperCAmelCase=None							,	)						->					Union[str, Any]:

   '''simple docstring'''







   if config_path is not None:
      __SCREAMING_SNAKE_CASE      							=       SpeechTaConfig.from_pretrained(__SCREAMING_SNAKE_CASE      )
   else:
      __SCREAMING_SNAKE_CASE      							=       SpeechTaConfig()

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

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

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

   __SCREAMING_SNAKE_CASE      							=       SpeechTaFeatureExtractor()
   __SCREAMING_SNAKE_CASE      							=       SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE							,	feature_extractor=__SCREAMING_SNAKE_CASE      )
   processor.save_pretrained(__SCREAMING_SNAKE_CASE      )

   __SCREAMING_SNAKE_CASE      							=       torch.load(__SCREAMING_SNAKE_CASE      )
   recursively_load_weights(fairseq_checkpoint["""model"""]							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE      )

   model.save_pretrained(__SCREAMING_SNAKE_CASE      )

   if repo_id:
      print("""Pushing to the hub..."""      )
      processor.push_to_hub(__SCREAMING_SNAKE_CASE      )
      model.push_to_hub(__SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
    a        =						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."
    )

    a        =						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,
    )












'''simple docstring'''

from __future__ import annotations

import bisect

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   if hi < 0:
      __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )

   while lo < hi:
      __SCREAMING_SNAKE_CASE      							=       lo + (hi - lo) // 2
      if sorted_collection[mid] < item:
         __SCREAMING_SNAKE_CASE      							=       mid + 1
      else:
         __SCREAMING_SNAKE_CASE      							=       mid

   return lo

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   if hi < 0:
      __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )

   while lo < hi:
      __SCREAMING_SNAKE_CASE      							=       lo + (hi - lo) // 2
      if sorted_collection[mid] <= item:
         __SCREAMING_SNAKE_CASE      							=       mid + 1
      else:
         __SCREAMING_SNAKE_CASE      							=       mid

   return lo

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					None:

   '''simple docstring'''







   sorted_collection.insert(bisect_left(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					None:

   '''simple docstring'''







   sorted_collection.insert(bisect_right(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					int | None:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       0
   __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      ) - 1

   while left <= right:
      __SCREAMING_SNAKE_CASE      							=       left + (right - left) // 2
      __SCREAMING_SNAKE_CASE      							=       sorted_collection[midpoint]
      if current_item == item:
         return midpoint
      elif item < current_item:
         __SCREAMING_SNAKE_CASE      							=       midpoint - 1
      else:
         __SCREAMING_SNAKE_CASE      							=       midpoint + 1
   return None

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					int | None:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       bisect.bisect_left(__UpperCAmelCase							,	__UpperCAmelCase      )
   if index != len(__UpperCAmelCase      ) and sorted_collection[index] == item:
      return index
   return None

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					int | None:

   '''simple docstring'''







   if right < left:
      return None

   __SCREAMING_SNAKE_CASE      							=       left + (right - left) // 2

   if sorted_collection[midpoint] == item:
      return midpoint
   elif sorted_collection[midpoint] > item:
      return binary_search_by_recursion(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	midpoint - 1      )
   else:
      return binary_search_by_recursion(__UpperCAmelCase							,	__UpperCAmelCase							,	midpoint + 1							,	__UpperCAmelCase      )


if __name__ == "__main__":
    a        =						input("Enter numbers separated by comma:\n").strip()
    a        =						sorted(int(item) for item in user_input.split(","))
    a        =						int(input("Enter a single number to be found in the list:\n"))
    a        =						binary_search(collection, target)
    if result is None:
        print(F'''{target} was not found in {collection}.''')
    else:
        print(F'''{target} was found at position {result} in {collection}.''')





