

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCAmelCase							:  Optional[Any]   	=						{
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						for attribute in key.split("."    ):
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       getattr(_UpperCAmelCase				,	_UpperCAmelCase    )

						if weight_type is not None:
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       getattr(_UpperCAmelCase				,	_UpperCAmelCase    ).shape
						else:
												SCREAMING_SNAKE_CASE_: List[str]  		=       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":
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       value
						elif weight_type == "weight_g":
												SCREAMING_SNAKE_CASE_: int  		=       value
						elif weight_type == "weight_v":
												SCREAMING_SNAKE_CASE_: Any  		=       value
						elif weight_type == "bias":
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       value
						else:
												SCREAMING_SNAKE_CASE_: List[Any]  		=       value

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







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       []
						SCREAMING_SNAKE_CASE_: int  		=       fairseq_model.state_dict()

						SCREAMING_SNAKE_CASE_: Tuple  		=       hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	hf_model.config.feat_extract_norm == "group"				,	)
																		SCREAMING_SNAKE_CASE_: Any  		=       True
												else:
																		for key, mapped_key in MAPPING.items():
																								SCREAMING_SNAKE_CASE_: Dict  		=       "sew." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

																								if key in name or key.split("w2v_model."    )[-1] == name.split("."    )[0]:
																														SCREAMING_SNAKE_CASE_: List[Any]  		=       True
																														if "*" in mapped_key:
																																				SCREAMING_SNAKE_CASE_: Any  		=       name.split(_UpperCAmelCase    )[0].split("."    )[-2]
																																				SCREAMING_SNAKE_CASE_: Tuple  		=       mapped_key.replace("*"				,	_UpperCAmelCase    )
																														if "weight_g" in name:
																																				SCREAMING_SNAKE_CASE_: Tuple  		=       "weight_g"
																														elif "weight_v" in name:
																																				SCREAMING_SNAKE_CASE_: Optional[int]  		=       "weight_v"
																														elif "weight" in name:
																																				SCREAMING_SNAKE_CASE_: Optional[int]  		=       "weight"
																														elif "bias" in name:
																																				SCREAMING_SNAKE_CASE_: Any  		=       "bias"
																														else:
																																				SCREAMING_SNAKE_CASE_: Tuple  		=       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 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       full_name.split("conv_layers."    )[-1]
						SCREAMING_SNAKE_CASE_: List[Any]  		=       name.split("."    )
						SCREAMING_SNAKE_CASE_: int  		=       int(items[0]    )
						SCREAMING_SNAKE_CASE_: str  		=       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."
																		)
																		SCREAMING_SNAKE_CASE_: Tuple  		=       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."
																		)
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       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."
																		)
																		SCREAMING_SNAKE_CASE_: int  		=       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."
																		)
																		SCREAMING_SNAKE_CASE_: Tuple  		=       value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
						else:
												unused_weights.append(_UpperCAmelCase    )







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       SEWConfig()
						if is_finetuned:
												SCREAMING_SNAKE_CASE_: str  		=       model.wav_encoder.wav_model.cfg
						else:
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model.cfg

						SCREAMING_SNAKE_CASE_: int  		=       fs_config.conv_bias
						SCREAMING_SNAKE_CASE_: List[str]  		=       eval(fs_config.conv_feature_layers    )
						SCREAMING_SNAKE_CASE_: int  		=       [x[0] for x in conv_layers]
						SCREAMING_SNAKE_CASE_: str  		=       [x[1] for x in conv_layers]
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [x[2] for x in conv_layers]
						SCREAMING_SNAKE_CASE_: int  		=       "gelu"
						SCREAMING_SNAKE_CASE_: Dict  		=       "layer" if fs_config.extractor_mode == "layer_norm" else "group"
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       0.0
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       fs_config.activation_fn.name
						SCREAMING_SNAKE_CASE_: Tuple  		=       fs_config.encoder_embed_dim
						SCREAMING_SNAKE_CASE_: List[str]  		=       0.0_2
						SCREAMING_SNAKE_CASE_: List[Any]  		=       fs_config.encoder_ffn_embed_dim
						SCREAMING_SNAKE_CASE_: List[str]  		=       1e-5
						SCREAMING_SNAKE_CASE_: Tuple  		=       fs_config.encoder_layerdrop
						SCREAMING_SNAKE_CASE_: Dict  		=       fs_config.encoder_attention_heads
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       fs_config.conv_pos_groups
						SCREAMING_SNAKE_CASE_: Any  		=       fs_config.conv_pos
						SCREAMING_SNAKE_CASE_: Tuple  		=       len(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: int  		=       fs_config.encoder_layers
						SCREAMING_SNAKE_CASE_: List[str]  		=       fs_config.squeeze_factor

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												SCREAMING_SNAKE_CASE_: Any  		=       model.cfg
												SCREAMING_SNAKE_CASE_: Tuple  		=       fs_config.final_dropout
												SCREAMING_SNAKE_CASE_: List[str]  		=       fs_config.layerdrop
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       fs_config.activation_dropout
						SCREAMING_SNAKE_CASE_: str  		=       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						SCREAMING_SNAKE_CASE_: Dict  		=       fs_config.attention_dropout
						SCREAMING_SNAKE_CASE_: int  		=       fs_config.dropout_input
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       fs_config.dropout
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       fs_config.mask_channel_length
						SCREAMING_SNAKE_CASE_: List[Any]  		=       fs_config.mask_channel_prob
						SCREAMING_SNAKE_CASE_: str  		=       fs_config.mask_length
						SCREAMING_SNAKE_CASE_: Any  		=       fs_config.mask_prob

						SCREAMING_SNAKE_CASE_: List[str]  		=       "Wav2Vec2FeatureExtractor"
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       "Wav2Vec2CTCTokenizer"

						return config







@torch.no_grad()
def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=None				,	_UpperCAmelCase=None				,	_UpperCAmelCase=True    ):

						if is_finetuned:
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path]				,	arg_overrides={"data": "/".join(dict_path.split("/"    )[:-1]    )}    )
						else:
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )

						if config_path is not None:
												SCREAMING_SNAKE_CASE_: int  		=       SEWConfig.from_pretrained(_UpperCAmelCase    )
						else:
												SCREAMING_SNAKE_CASE_: int  		=       convert_config(model[0]				,	_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: List[str]  		=       model[0].eval()

						SCREAMING_SNAKE_CASE_: str  		=       True if config.feat_extract_norm == "layer" else False
						SCREAMING_SNAKE_CASE_: Tuple  		=       WavaVecaFeatureExtractor(
						    feature_size=1				,	sampling_rate=1_60_00				,	padding_value=0				,	do_normalize=_UpperCAmelCase				,	return_attention_mask=_UpperCAmelCase				,	)

						if is_finetuned:
												if dict_path:
																		SCREAMING_SNAKE_CASE_: Any  		=       Dictionary.load(_UpperCAmelCase    )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       target_dict.pad_index
																		SCREAMING_SNAKE_CASE_: Any  		=       target_dict.bos_index
																		SCREAMING_SNAKE_CASE_: Any  		=       target_dict.pad_index
																		SCREAMING_SNAKE_CASE_: str  		=       target_dict.bos_index
																		SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       target_dict.eos_index
																		SCREAMING_SNAKE_CASE_: str  		=       len(target_dict.symbols    )
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       os.path.join(_UpperCAmelCase				,	"vocab.json"    )
																		if not os.path.isdir(_UpperCAmelCase    ):
																								logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_UpperCAmelCase    )    )
																								return
																		os.makedirs(_UpperCAmelCase				,	exist_ok=_UpperCAmelCase    )
																		with open(_UpperCAmelCase				,	"w"				,	encoding="utf-8"    ) as vocab_handle:
																								json.dump(target_dict.indices				,	_UpperCAmelCase    )
																		SCREAMING_SNAKE_CASE_: int  		=       WavaVecaCTCTokenizer(
																		    _UpperCAmelCase				,	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=_UpperCAmelCase				,	)
																		SCREAMING_SNAKE_CASE_: Dict  		=       WavaVecaProcessor(feature_extractor=_UpperCAmelCase				,	tokenizer=_UpperCAmelCase    )
																		processor.save_pretrained(_UpperCAmelCase    )

												SCREAMING_SNAKE_CASE_: Any  		=       SEWForCTC(_UpperCAmelCase    )
						else:
												SCREAMING_SNAKE_CASE_: Tuple  		=       SEWModel(_UpperCAmelCase    )
												feature_extractor.save_pretrained(_UpperCAmelCase    )

						recursively_load_weights(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

						hf_model.save_pretrained(_UpperCAmelCase    )


if __name__ == "__main__":
						lowerCAmelCase							:  List[Any]   	=						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(
						    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
						)
						lowerCAmelCase							:  Any   	=						parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)







"""simple docstring"""







import enum
import warnings

from ..tokenization_utils import TruncationStrategy
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

       from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			enum.Enum  ):
   lowercase							=  0
   lowercase							=  1







@add_end_docstrings(lowercase_  )
class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  'generated'
   def __init__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      Tuple:


        '''simple docstring'''


        super().__init__(*__UpperCamelCase     ,**__UpperCamelCase			)

        self.check_model_type(
            TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
            if self.framework == 'tf'
            else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,**__UpperCamelCase     ,)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	{}
        if truncation is not None:
             lowercase_	:				int													=	truncation

        lowercase_	:				Dict													=	generate_kwargs

        lowercase_	:				List[Any]													=	{}
        if return_tensors is not None and return_type is None:
             lowercase_	:				Union[str, Any]													=	ReturnType.TENSORS if return_tensors else ReturnType.TEXT
        if return_type is not None:
             lowercase_	:				str													=	return_type

        if clean_up_tokenization_spaces is not None:
             lowercase_	:				Dict													=	clean_up_tokenization_spaces

        if stop_sequence is not None:
             lowercase_	:				Union[str, Any]													=	self.tokenizer.encode(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase			)
             if len(__UpperCamelCase			) > 1:
                  warnings.warn(
                      'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
                      ' the stop sequence will be used as the stop sequence string in the interim.'			)
             lowercase_	:				Optional[int]													=	stop_sequence_ids[0]

        return preprocess_params, forward_params, postprocess_params
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        return True
   def 			_UpperCAmelCase   (			self     ,*__UpperCamelCase     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Dict													=	self.model.config.prefix if self.model.config.prefix is not None else ''
        if isinstance(args[0]     ,__UpperCamelCase			):
             if self.tokenizer.pad_token_id is None:
                  raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input'			)
             lowercase_	:				str													=	([prefix + arg for arg in args[0]],)
             lowercase_	:				Union[str, Any]													=	True

        elif isinstance(args[0]     ,__UpperCamelCase			):
             lowercase_	:				Union[str, Any]													=	(prefix + args[0],)
             lowercase_	:				Union[str, Any]													=	False
        else:
             raise ValueError(
                 f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''			)
        lowercase_	:				List[Any]													=	self.tokenizer(*__UpperCamelCase     ,padding=__UpperCamelCase     ,truncation=__UpperCamelCase     ,return_tensors=self.framework			)
        # This is produced by tokenizers but is an invalid generate kwargs
        if "token_type_ids" in inputs:
             del inputs["token_type_ids"]
        return inputs
   def __call__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	super().__call__(*__UpperCamelCase     ,**__UpperCamelCase			)
        if (
            isinstance(args[0]     ,__UpperCamelCase			)
            and all(isinstance(__UpperCamelCase     ,__UpperCamelCase			) for el in args[0]			)
            and all(len(__UpperCamelCase			) == 1 for res in result			)
        ):
             return [res[0] for res in result]
        return result
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=TruncationStrategy.DO_NOT_TRUNCATE     ,**__UpperCamelCase			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				Any													=	self._parse_and_tokenize(__UpperCamelCase     ,truncation=__UpperCamelCase     ,**__UpperCamelCase			)
        return inputs
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,**__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        if self.framework == "pt":
             lowercase_							,  lowercase_	:				Optional[int]													=	model_inputs['input_ids'].shape
        elif self.framework == "tf":
             lowercase_							,  lowercase_	:				Union[str, Any]													=	tf.shape(model_inputs['input_ids']			).numpy()

        lowercase_	:				str													=	generate_kwargs.get('min_length'     ,self.model.config.min_length			)
        lowercase_	:				List[Any]													=	generate_kwargs.get('max_length'     ,self.model.config.max_length			)
        self.check_inputs(__UpperCamelCase     ,generate_kwargs['min_length']     ,generate_kwargs['max_length']			)
        lowercase_	:				Tuple													=	self.model.generate(**__UpperCamelCase     ,**__UpperCamelCase			)
        lowercase_	:				str													=	output_ids.shape[0]
        if self.framework == "pt":
             lowercase_	:				List[Any]													=	output_ids.reshape(__UpperCamelCase     ,out_b // in_b     ,*output_ids.shape[1:]			)
        elif self.framework == "tf":
             lowercase_	:				List[Any]													=	tf.reshape(__UpperCamelCase     ,(in_b, out_b // in_b, *output_ids.shape[1:])			)
        return {"output_ids": output_ids}







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=ReturnType.TEXT     ,__UpperCamelCase=False			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Dict													=	[]
        for output_ids in model_outputs["output_ids"][0]:
             if return_type == ReturnType.TENSORS:
                  lowercase_	:				List[Any]													=	{f'''{self.return_name}_token_ids''': output_ids}
             elif return_type == ReturnType.TEXT:
                  lowercase_	:				str													=	{
                      f'''{self.return_name}_text''': self.tokenizer.decode(
                          __UpperCamelCase     ,skip_special_tokens=__UpperCamelCase     ,clean_up_tokenization_spaces=__UpperCamelCase     ,)
                  }
             records.append(__UpperCamelCase			)
        return records







@add_end_docstrings(lowercase_  )
class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  'summary'
   def __call__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        return super().__call__(*__UpperCamelCase     ,**__UpperCamelCase			)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      bool:


        '''simple docstring'''


        if max_length < min_length:
             logger.warning(f'''Your min_length={min_length} must be inferior than your max_length={max_length}.'''			)

        if input_length < max_length:
             logger.warning(
                 f'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
                 'a summarization task, where outputs shorter than the input are typically wanted, you might '
                 f'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'''			)







@add_end_docstrings(lowercase_  )
class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  'translation'
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        if input_length > 0.9 * max_length:
             logger.warning(
                 f'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
                 'increasing your max_length manually, e.g. translator(\'...\', max_length=400)'			)
        return True
   def 			_UpperCAmelCase   (			self     ,*__UpperCamelCase     ,__UpperCamelCase=TruncationStrategy.DO_NOT_TRUNCATE     ,__UpperCamelCase=None     ,__UpperCamelCase=None			)	->      int:


        '''simple docstring'''


        if getattr(self.tokenizer     ,'_build_translation_inputs'     ,__UpperCamelCase			):
             return self.tokenizer._build_translation_inputs(
                 *__UpperCamelCase     ,return_tensors=self.framework     ,truncation=__UpperCamelCase     ,src_lang=__UpperCamelCase     ,tgt_lang=__UpperCamelCase			)
        else:
             return super()._parse_and_tokenize(*__UpperCamelCase     ,truncation=__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,**__UpperCamelCase			)	->      str:


        '''simple docstring'''


        lowercase_							,  lowercase_							,  lowercase_	:				int													=	super()._sanitize_parameters(**__UpperCamelCase			)
        if src_lang is not None:
             lowercase_	:				str													=	src_lang
        if tgt_lang is not None:
             lowercase_	:				Optional[Any]													=	tgt_lang
        if src_lang is None and tgt_lang is None:
             # Backward compatibility, direct arguments use is preferred.
             lowercase_	:				Tuple													=	kwargs.get('task'     ,self.task			)
             lowercase_	:				List[str]													=	task.split('_'			)
             if task and len(__UpperCamelCase			) == 4:
                  # translation, XX, to YY
                  lowercase_	:				Union[str, Any]													=	items[1]
                  lowercase_	:				Tuple													=	items[3]
        return preprocess_params, forward_params, postprocess_params







   def __call__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        return super().__call__(*__UpperCamelCase     ,**__UpperCamelCase			)

