





'''simple docstring'''


import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
_lowerCamelCase :		str 			=				logging.get_logger('transformers.models.encodec')

_lowerCamelCase :		Tuple 			=				{
    'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
    'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
    'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
    'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
_lowerCamelCase :		Any 			=				{
    'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
    'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
    'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
    'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
    'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
    'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
    'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
    'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
    'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
    'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
    'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
    'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
    'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
    'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
    'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
    'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
    'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
    'encoder.model.13.lstm': 'encoder.layers.13.lstm',
    'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
_lowerCamelCase :		List[Any] 			=				{
    'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
    'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
    'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
    'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
    'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
    'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
    'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
    'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
    'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
    'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
    'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
    'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
    'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
    'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
    'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
    'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
    'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
    'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
_lowerCamelCase :		Dict 			=				{
    'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
    'decoder.model.1.lstm': 'decoder.layers.1.lstm',
    'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
    'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
    'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
    'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
    'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
    'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
    'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
    'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
    'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
    'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
    'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
    'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
    'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
    'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
    'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
    'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
    'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
_lowerCamelCase :		Optional[Any] 			=				{
    'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
    'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
    'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
    'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
    'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
    'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
    'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
    'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
    'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
    'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
    'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
    'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
    'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
    'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
    'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
    'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
    'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
    'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
_lowerCamelCase :		List[str] 			=				{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_lowerCamelCase :		str 			=				{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_lowerCamelCase :		Optional[int] 			=				[]
_lowerCamelCase :		int 			=				[]




def       __a       (   UpperCAmelCase				,	UpperCAmelCase				,	UpperCAmelCase				,	UpperCAmelCase				,	UpperCAmelCase			)					->int:



	"""simple docstring"""







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

	if weight_type is not None:
		A							     =					getattr(_lowerCamelCase				,	_lowerCamelCase			).shape
	else:
		A							     =					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":
		A							     =					value
	elif weight_type == "weight_g":
		A							     =					value
	elif weight_type == "weight_v":
		A							     =					value
	elif weight_type == "bias":
		A							     =					value
	elif weight_type == "running_mean":
		A							     =					value
	elif weight_type == "running_var":
		A							     =					value
	elif weight_type == "num_batches_tracked":
		A							     =					value
	elif weight_type == "weight_ih_l0":
		A							     =					value
	elif weight_type == "weight_hh_l0":
		A							     =					value
	elif weight_type == "bias_ih_l0":
		A							     =					value
	elif weight_type == "bias_hh_l0":
		A							     =					value
	elif weight_type == "weight_ih_l1":
		A							     =					value
	elif weight_type == "weight_hh_l1":
		A							     =					value
	elif weight_type == "bias_ih_l1":
		A							     =					value
	elif weight_type == "bias_hh_l1":
		A							     =					value
	else:
		A							     =					value

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




def       __a       (   UpperCAmelCase				,	UpperCAmelCase			)					->List[str]:



	"""simple docstring"""







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




def       __a       (   UpperCAmelCase				,	UpperCAmelCase				,	UpperCAmelCase			)					->str:



	"""simple docstring"""







	A							     =					[]

	if model_name == "encodec_24khz" or "encodec_32khz":
		A							     =					MAPPING_24K
	elif model_name == "encodec_48khz":
		A							     =					MAPPING_48K
	else:
		raise ValueError(f"""Unsupported model: {model_name}"""			)

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

		A							     =					False
		for key, mapped_key in MAPPING.items():
			if "*" in key:
				A							     =					key.split(""".*."""			)
				if prefix in name and suffix in name:
					A							     =					suffix

			if key in name:
				# HACK otherwise .embed gets initialized with .embed_avg too
				if key.endswith("""embed"""			) and name.endswith("""embed_avg"""			):
					continue

				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 "weight_ih_l0" in name:
					A							     =					"weight_ih_l0"
				elif "weight_hh_l0" in name:
					A							     =					"weight_hh_l0"
				elif "bias_ih_l0" in name:
					A							     =					"bias_ih_l0"
				elif "bias_hh_l0" in name:
					A							     =					"bias_hh_l0"
				elif "weight_ih_l1" in name:
					A							     =					"weight_ih_l1"
				elif "weight_hh_l1" in name:
					A							     =					"weight_hh_l1"
				elif "bias_ih_l1" in name:
					A							     =					"bias_ih_l1"
				elif "bias_hh_l1" in name:
					A							     =					"bias_hh_l1"
				elif "bias" in name:
					A							     =					"bias"
				elif "weight" in name:
					A							     =					"weight"
				elif "running_mean" in name:
					A							     =					"running_mean"
				elif "running_var" in name:
					A							     =					"running_var"
				elif "num_batches_tracked" in name:
					A							     =					"num_batches_tracked"
				else:
					A							     =					None
				set_recursively(_lowerCamelCase				,	_lowerCamelCase				,	_lowerCamelCase				,	_lowerCamelCase				,	_lowerCamelCase			)
			continue
		if not is_used:
			unused_weights.append(_lowerCamelCase			)

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




@torch.no_grad()
def       __a       (   UpperCAmelCase				,	UpperCAmelCase				,	UpperCAmelCase				,	UpperCAmelCase=None				,	UpperCAmelCase=None				,	)					->List[str]:



	"""simple docstring"""







	if config_path is not None:
		A							     =					EncodecConfig.from_pretrained(_lowerCamelCase			)
	else:
		A							     =					EncodecConfig()

	if model_name == "encodec_24khz":
		pass  # config is already correct
	elif model_name == "encodec_32khz":
		A							     =					[8, 5, 4, 4]
		A							     =					[2.2]
		A							     =					64
		A							     =					32000
		A							     =					2048
		A							     =					False
		A							     =					False
		A							     =					False
	elif model_name == "encodec_48khz":
		A							     =					[8, 5, 4, 2]
		A							     =					[3.0, 6.0, 12.0, 24.0]
		A							     =					48000
		A							     =					2
		A							     =					False
		A							     =					"time_group_norm"
		A							     =					True
		A							     =					1.0
		A							     =					0.01
	else:
		raise ValueError(f"""Unknown model name: {model_name}"""			)

	A							     =					EncodecModel(_lowerCamelCase			)

	A							     =					EncodecFeatureExtractor(
	    feature_size=config.audio_channels				,	sampling_rate=config.sampling_rate				,	chunk_length_s=config.chunk_length_s				,	overlap=config.overlap				,	)
	feature_extractor.save_pretrained(_lowerCamelCase			)

	A							     =					torch.load(_lowerCamelCase			)
	if "best_state" in original_checkpoint:
		# we might have a training state saved, in which case discard the yaml results and just retain the weights
		A							     =					original_checkpoint["best_state"]
	recursively_load_weights(_lowerCamelCase				,	_lowerCamelCase				,	_lowerCamelCase			)
	model.save_pretrained(_lowerCamelCase			)

	if repo_id:
		print("""Pushing to the hub..."""			)
		feature_extractor.push_to_hub(_lowerCamelCase			)
		model.push_to_hub(_lowerCamelCase			)


if __name__ == "__main__":
				_lowerCamelCase :		Tuple 			=				argparse.ArgumentParser()
				parser.add_argument(
				    '--model',
				    default='encodec_24khz',
				    type=str,
				    help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
				)
				parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
				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.'
				)

				_lowerCamelCase :		Dict 			=				parser.parse_args()
				convert_checkpoint(
				    args.model,
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)



import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
		import tensorflow as tf

if is_torch_available():
		import torch


lowercase  :					List[str]													=       logging.get_logger(__name__)




class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:str     ,   a		:str = None     ,   a		:uuid.UUID = None     ,   a		:Tuple=None     ,   a		:Optional[Any]=None						)		-> str:
								if not conversation_id:
															__UpperCamelCase	:   Dict			   =					uuid.uuida()
								if past_user_inputs is None:
															__UpperCamelCase	:   List[Any]			   =					[]
								if generated_responses is None:
															__UpperCamelCase	:   Any			   =					[]

								__UpperCamelCase	:   uuid.UUID			   =					conversation_id
								__UpperCamelCase	:   List[str]			   =					past_user_inputs
								__UpperCamelCase	:   List[str]			   =					generated_responses
								__UpperCamelCase	:   Optional[str]			   =					text







	def __eq__(					self		:Optional[int]     ,   a		:Optional[int]						)		-> Union[str, Any]:
								if not isinstance(a     ,   a						):
															return False
								if self.uuid == other.uuid:
															return True
								return (
								    self.new_user_input == other.new_user_input
								    and self.past_user_inputs == other.past_user_inputs
								    and self.generated_responses == other.generated_responses
								)







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:str     ,   a		:bool = False						)		-> str:
								if self.new_user_input:
															if overwrite:
																						logger.warning(
																						    f'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
																						    f'with: "{text}".'						)
																						__UpperCamelCase	:   Any			   =					text
															else:
																						logger.warning(
																						    f'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
																						    f'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'						)
								else:
															__UpperCamelCase	:   int			   =					text







	def    _lowerCamelCase				(					self		:List[str]						)		-> int:
								if self.new_user_input:
															self.past_user_inputs.append(self.new_user_input						)
								__UpperCamelCase	:   Dict			   =					None







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:str						)		-> Optional[int]:
								self.generated_responses.append(a						)







	def    _lowerCamelCase				(					self		:int						)		-> Optional[Any]:
								for user_input, generated_response in zip(self.past_user_inputs     ,   self.generated_responses						):
															yield True, user_input
															yield False, generated_response
								if self.new_user_input:
															yield True, self.new_user_input







	def __repr__(					self		:List[str]						)		-> List[Any]:
								__UpperCamelCase	:   Any			   =					f'Conversation id: {self.uuid} \n'
								for is_user, text in self.iter_texts():
															__UpperCamelCase	:   str			   =					"user" if is_user else "bot"
															output += f'{name} >> {text} \n'
								return output




@add_end_docstrings(
    __lowercase     ,						R'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '     ,						)
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __init__(					self		:Tuple     ,   *a		:Tuple     ,   **a		:List[str]						)		-> Tuple:
								super().__init__(*a     ,   **a						)
								if self.tokenizer.pad_token_id is None:
															__UpperCamelCase	:   int			   =					self.tokenizer.eos_token







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:List[Any]=None     ,   a		:str=None     ,   a		:int=None     ,   **a		:str						)		-> List[str]:
								__UpperCamelCase	:   List[str]			   =					{}
								__UpperCamelCase	:   List[str]			   =					{}
								__UpperCamelCase	:   str			   =					{}

								if min_length_for_response is not None:
															__UpperCamelCase	:   Optional[Any]			   =					min_length_for_response
								if minimum_tokens is not None:
															__UpperCamelCase	:   List[str]			   =					minimum_tokens

								if "max_length" in generate_kwargs:
															__UpperCamelCase	:   List[Any]			   =					generate_kwargs["max_length"]
															# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
								if clean_up_tokenization_spaces is not None:
															__UpperCamelCase	:   List[Any]			   =					clean_up_tokenization_spaces

								if generate_kwargs:
															forward_params.update(a						)
								return preprocess_params, forward_params, postprocess_params







	def __call__(					self		:Dict     ,   a		:Union[Conversation, List[Conversation]]     ,   a		:List[Any]=0     ,   **a		:Any						)		-> Union[str, Any]:
								__UpperCamelCase	:   Optional[int]			   =					super().__call__(a     ,   num_workers=a     ,   **a						)
								if isinstance(a     ,   a						) and len(a						) == 1:
															return outputs[0]
								return outputs







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:Conversation     ,   a		:Dict=3_2						)		-> Dict[str, Any]:
								if not isinstance(a     ,   a						):
															raise ValueError("ConversationalPipeline, expects Conversation as inputs"						)
								if conversation.new_user_input is None:
															raise ValueError(
															    f'Conversation with UUID {type(conversation.uuid						)} does not contain new user input to process. '
															    "Add user inputs with the conversation's `add_user_input` method"						)
								if hasattr(self.tokenizer     ,   "_build_conversation_input_ids"						):
															__UpperCamelCase	:   str			   =					self.tokenizer._build_conversation_input_ids(a						)
								else:
															# If the tokenizer cannot handle conversations, we default to only the old version
															__UpperCamelCase	:   Optional[Any]			   =					self._legacy_parse_and_tokenize(a						)

								if self.framework == "pt":
															__UpperCamelCase	:   Dict			   =					torch.LongTensor([input_ids]						)
								elif self.framework == "tf":
															__UpperCamelCase	:   Any			   =					tf.constant([input_ids]						)
								return {"input_ids": input_ids, "conversation": conversation}







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[Any]     ,   a		:Optional[Any]=1_0     ,   **a		:Tuple						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					generate_kwargs.get("max_length"     ,   self.model.config.max_length						)

								__UpperCamelCase	:   Dict			   =					model_inputs["input_ids"].shape[1]
								if max_length - minimum_tokens < n:
															logger.warning(f'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'						)
															__UpperCamelCase	:   Dict			   =					max_length - minimum_tokens
															__UpperCamelCase	:   Optional[int]			   =					model_inputs["input_ids"][:, -trim:]
															if "attention_mask" in model_inputs:
																						__UpperCamelCase	:   Dict			   =					model_inputs["attention_mask"][:, -trim:]
								__UpperCamelCase	:   List[str]			   =					model_inputs.pop("conversation"						)
								__UpperCamelCase	:   Optional[int]			   =					max_length
								__UpperCamelCase	:   str			   =					self.model.generate(**a     ,   **a						)
								if self.model.config.is_encoder_decoder:
															__UpperCamelCase	:   List[str]			   =					1
								else:
															__UpperCamelCase	:   Optional[int]			   =					n
								return {"output_ids": output_ids[:, start_position:], "conversation": conversation}







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:str     ,   a		:Optional[int]=True						)		-> Union[str, Any]:
								__UpperCamelCase	:   List[str]			   =					model_outputs["output_ids"]
								__UpperCamelCase	:   Any			   =					self.tokenizer.decode(
								    output_ids[0]     ,   skip_special_tokens=a     ,   clean_up_tokenization_spaces=a     ,   )
								__UpperCamelCase	:   int			   =					model_outputs["conversation"]
								conversation.mark_processed()
								conversation.append_response(a						)
								return conversation







	def    _lowerCamelCase				(					self		:str     ,   a		:Conversation						)		-> Dict:
								__UpperCamelCase	:   int			   =					self.tokenizer.eos_token_id
								__UpperCamelCase	:   Any			   =					[]
								for is_user, text in conversation.iter_texts():
															if eos_token_id is not None:
																						input_ids.extend(self.tokenizer.encode(a     ,   add_special_tokens=a						) + [eos_token_id]						)
															else:
																						input_ids.extend(self.tokenizer.encode(a     ,   add_special_tokens=a						)						)

								if len(a						) > self.tokenizer.model_max_length:
															__UpperCamelCase	:   Union[str, Any]			   =					input_ids[-self.tokenizer.model_max_length :]
								return input_ids