





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

A_												=     {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "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": "lm_head",
    "mask_emb": "masked_spec_embed",
}
A_												=     [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def    __UpperCamelCase      (    a,     a,     a,     a,     a) ->Dict:
					for attribute in key.split("."):
										lowerCamelCase__       =       getattr(a,     a)

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

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





def    __UpperCamelCase      (    a,     a,     a) ->Optional[int]:
					lowerCamelCase__       =       []
					lowerCamelCase__       =       fairseq_model.state_dict()

					lowerCamelCase__       =       hf_model.wavaveca_conformer.feature_extractor

					for name, value in fairseq_dict.items():
										lowerCamelCase__       =       False
										if "conv_layers" in name:
															load_conv_layer(
															    a,     a,     a,     a,     hf_model.config.feat_extract_norm == "group",     )
															lowerCamelCase__       =       True
										else:
															for key, mapped_key in MAPPING.items():
																				lowerCamelCase__       =       "wav2vec2_conformer." + 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]:
																									lowerCamelCase__       =       True
																									if "*" in mapped_key:
																														lowerCamelCase__       =       name.split(a)[0].split(".")[-2]
																														lowerCamelCase__       =       mapped_key.replace("*",     a)
																									if "pos_bias_u" in name:
																														lowerCamelCase__       =       None
																									elif "pos_bias_v" in name:
																														lowerCamelCase__       =       None
																									elif "weight_g" in name:
																														lowerCamelCase__       =       "weight_g"
																									elif "weight_v" in name:
																														lowerCamelCase__       =       "weight_v"
																									elif "bias" in name:
																														lowerCamelCase__       =       "bias"
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														lowerCamelCase__       =       "weight"
																									elif "running_mean" in name:
																														lowerCamelCase__       =       "running_mean"
																									elif "inv_freq" in name:
																														lowerCamelCase__       =       "inv_freq"
																									elif "running_var" in name:
																														lowerCamelCase__       =       "running_var"
																									elif "num_batches_tracked" in name:
																														lowerCamelCase__       =       "num_batches_tracked"
																									else:
																														lowerCamelCase__       =       None
																									set_recursively(a,     a,     a,     a,     a)
																				continue
										if not is_used:
															unused_weights.append(a)

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





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





@torch.no_grad()
def    __UpperCamelCase      (    a,     a,     a=None,     a=None,     a=True) ->Optional[Any]:
					if config_path is not None:
										lowerCamelCase__       =       WavaVecaConformerConfig.from_pretrained(a,     hidden_act="swish")
					else:
										lowerCamelCase__       =       WavaVecaConformerConfig()

					if "rope" in checkpoint_path:
										lowerCamelCase__       =       "rotary"

					if is_finetuned:
										if dict_path:
															lowerCamelCase__       =       Dictionary.load(a)

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

															# fairseq has the <pad> and <s> switched
															lowerCamelCase__       =       0
															lowerCamelCase__       =       1
															with open(a,     "w",     encoding="utf-8") as vocab_handle:
																				json.dump(a,     a)
															lowerCamelCase__       =       WavaVecaCTCTokenizer(
															    a,     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=a,     )
															lowerCamelCase__       =       True if config.feat_extract_norm == "layer" else False
															lowerCamelCase__       =       WavaVecaFeatureExtractor(
															    feature_size=1,     sampling_rate=16000,     padding_value=0,     do_normalize=a,     return_attention_mask=a,     )
															lowerCamelCase__       =       WavaVecaProcessor(feature_extractor=a,     tokenizer=a)
															processor.save_pretrained(a)

										lowerCamelCase__       =       WavaVecaConformerForCTC(a)
					else:
										lowerCamelCase__       =       WavaVecaConformerForPreTraining(a)

					if is_finetuned:
										lowerCamelCase__    ,		lowerCamelCase__    ,		lowerCamelCase__       =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path],     arg_overrides={"data": "/".join(dict_path.split("/")[:-1])})
					else:
										lowerCamelCase__       =       argparse.Namespace(task="audio_pretraining")
										lowerCamelCase__       =       fairseq.tasks.setup_task(a)

										lowerCamelCase__    ,		lowerCamelCase__    ,		lowerCamelCase__       =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path],     task=a)

					lowerCamelCase__       =       model[0].eval()

					recursively_load_weights(a,     a,     not is_finetuned)

					hf_wavavec.save_pretrained(a)


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









import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


A_												=     get_logger(__name__)


A_												=     r"\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					@add_start_docstrings(_lowerCAmelCase	)
					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										raise NotImplementedError(
										    F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."	)

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					@add_start_docstrings(_lowerCAmelCase	)
					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										raise NotImplementedError(
										    F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."	)

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					@add_start_docstrings(_lowerCAmelCase	)
					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  **_lowerCAmelCase	):
										for processor in self:
															lowerCamelCase__       =       inspect.signature(processor.__call__	).parameters
															if len(_lowerCAmelCase	) > 3:
																				if not all(arg in kwargs for arg in list(function_args.keys()	)[2:]	):
																									raise ValueError(
																									    F"Make sure that all the required parameters: {list(function_args.keys()	)} for "
																									    F"{processor.__class__} are passed to the logits processor."	)
																				lowerCamelCase__       =       processor(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  **_lowerCAmelCase	)
															else:
																				lowerCamelCase__       =       processor(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	)
										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	):
										if not isinstance(_lowerCAmelCase	,  _lowerCAmelCase	) or not (temperature > 0):
															raise ValueError(F"`temperature` has to be a strictly positive float, but is {temperature}"	)

										lowerCamelCase__       =       temperature






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       scores / self.temperature
										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase = -float("Inf"	)	,  _lowerCAmelCase = 1	):
										if not isinstance(_lowerCAmelCase	,  _lowerCAmelCase	) or (top_p < 0 or top_p > 1.0):
															raise ValueError(F"`top_p` has to be a float > 0 and < 1, but is {top_p}"	)
										if not isinstance(_lowerCAmelCase	,  _lowerCAmelCase	) or (min_tokens_to_keep < 1):
															raise ValueError(F"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"	)

										lowerCamelCase__       =       top_p
										lowerCamelCase__       =       filter_value
										lowerCamelCase__       =       min_tokens_to_keep






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__    ,		lowerCamelCase__       =       lax.top_k(_lowerCAmelCase	,  scores.shape[-1]	)

										lowerCamelCase__       =       jnp.full_like(_lowerCAmelCase	,  self.filter_value	)
										lowerCamelCase__       =       jax.nn.softmax(_lowerCAmelCase	,  axis=-1	).cumsum(axis=-1	)
										lowerCamelCase__       =       cumulative_probs < self.top_p

										# include the token that is higher than top_p as well
										lowerCamelCase__       =       jnp.roll(_lowerCAmelCase	,  1	)
										score_mask |= score_mask.at[:, 0].set(_lowerCAmelCase	)

										# min tokens to keep
										lowerCamelCase__       =       score_mask.at[:, : self.min_tokens_to_keep].set(_lowerCAmelCase	)

										lowerCamelCase__       =       jnp.where(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       jax.lax.sort_key_val(_lowerCAmelCase	,  _lowerCAmelCase	)[-1]

										return next_scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase = -float("Inf"	)	,  _lowerCAmelCase = 1	):
										if not isinstance(_lowerCAmelCase	,  _lowerCAmelCase	) or top_k <= 0:
															raise ValueError(F"`top_k` has to be a strictly positive integer, but is {top_k}"	)

										lowerCamelCase__       =       max(_lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       filter_value






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__    ,		lowerCamelCase__       =       scores.shape
										lowerCamelCase__       =       jnp.full(batch_size * vocab_size	,  self.filter_value	)

										lowerCamelCase__       =       min(self.top_k	,  scores.shape[-1]	)  # Safety check
										lowerCamelCase__    ,		lowerCamelCase__       =       lax.top_k(_lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       jnp.broadcast_to((jnp.arange(_lowerCAmelCase	) * vocab_size)[:, None]	,  (batch_size, topk)	).flatten()
										lowerCamelCase__       =       topk_scores.flatten()
										lowerCamelCase__       =       topk_indices.flatten() + shift

										lowerCamelCase__       =       next_scores_flat.at[topk_indices_flat].set(_lowerCAmelCase	)
										lowerCamelCase__       =       next_scores_flat.reshape(_lowerCAmelCase	,  _lowerCAmelCase	)
										return next_scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       bos_token_id






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       jnp.full(scores.shape	,  -float("inf"	)	)

										lowerCamelCase__       =       1 - jnp.bool_(cur_len - 1	)

										lowerCamelCase__       =       jnp.where(_lowerCAmelCase	,  new_scores.at[:, self.bos_token_id].set(0	)	,  _lowerCAmelCase	)

										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       max_length
										lowerCamelCase__       =       eos_token_id






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       jnp.full(scores.shape	,  -float("inf"	)	)

										lowerCamelCase__       =       1 - jnp.bool_(cur_len - self.max_length + 1	)

										lowerCamelCase__       =       jnp.where(_lowerCAmelCase	,  new_scores.at[:, self.eos_token_id].set(0	)	,  _lowerCAmelCase	)

										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										if not isinstance(_lowerCAmelCase	,  _lowerCAmelCase	) or min_length < 0:
															raise ValueError(F"`min_length` has to be a positive integer, but is {min_length}"	)

										if not isinstance(_lowerCAmelCase	,  _lowerCAmelCase	) or eos_token_id < 0:
															raise ValueError(F"`eos_token_id` has to be a positive integer, but is {eos_token_id}"	)

										lowerCamelCase__       =       min_length
										lowerCamelCase__       =       eos_token_id






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										# create boolean flag to decide if min length penalty should be applied
										lowerCamelCase__       =       1 - jnp.clip(cur_len - self.min_length	,  0	,  1	)

										lowerCamelCase__       =       jnp.where(_lowerCAmelCase	,  scores.at[:, self.eos_token_id].set(-float("inf"	)	)	,  _lowerCAmelCase	)

										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       list(_lowerCAmelCase	)
										lowerCamelCase__       =       begin_index






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       1 - jnp.bool_(cur_len - self.begin_index	)

										lowerCamelCase__       =       jnp.where(_lowerCAmelCase	,  scores.at[:, self.begin_suppress_tokens].set(-float("inf"	)	)	,  _lowerCAmelCase	)

										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       list(_lowerCAmelCase	)






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       scores.at[..., self.suppress_tokens].set(-float("inf"	)	)

										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       dict(_lowerCAmelCase	)
										# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
										# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
										# Indexes without forced tokens will have a negative value.
										lowerCamelCase__       =       jnp.ones((max(force_token_map.keys()	) + 1)	,  dtype=jnp.intaa	) * -1
										for index, token in force_token_map.items():
															if token is not None:
																				lowerCamelCase__       =       force_token_array.at[index].set(_lowerCAmelCase	)
										lowerCamelCase__       =       jnp.intaa(_lowerCAmelCase	)






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										def _force_token(_lowerCAmelCase	):
															lowerCamelCase__       =       scores.shape[0]
															lowerCamelCase__       =       self.force_token_array[generation_idx]

															lowerCamelCase__       =       jnp.ones_like(_lowerCAmelCase	,  dtype=scores.dtype	) * -float("inf"	)
															lowerCamelCase__       =       jnp.zeros((batch_size, 1)	,  dtype=scores.dtype	)
															lowerCamelCase__       =       lax.dynamic_update_slice(_lowerCAmelCase	,  _lowerCAmelCase	,  (0, current_token)	)
															return new_scores

										lowerCamelCase__       =       lax.cond(
										    cur_len >= self.force_token_array.shape[0]	,  lambda: scores	,  lambda: lax.cond(
										        self.force_token_array[cur_len] >= 0	,  lambda: _force_token(_lowerCAmelCase	)	,  lambda: scores	,  )	,  )
										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       generate_config.eos_token_id
										lowerCamelCase__       =       generate_config.no_timestamps_token_id
										lowerCamelCase__       =       generate_config.no_timestamps_token_id + 1

										lowerCamelCase__       =       decoder_input_length + 1

										if generate_config.is_multilingual:
															# room for language token and task token
															self.begin_index += 2
										if hasattr(_lowerCAmelCase	,  "max_initial_timestamp_index"	):
															lowerCamelCase__       =       generate_config.max_initial_timestamp_index
										else:
															lowerCamelCase__       =       model_config.vocab_size
										if self.max_initial_timestamp_index is None:
															lowerCamelCase__       =       model_config.vocab_size






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										# suppress <|notimestamps|> which is handled by without_timestamps
										lowerCamelCase__       =       scores.at[:, self.no_timestamps_token_id].set(-float("inf"	)	)

										def handle_pairs(_lowerCAmelCase	,  _lowerCAmelCase	):
															lowerCamelCase__       =       jnp.where((cur_len - self.begin_index) >= 1	,  _lowerCAmelCase	,  _lowerCAmelCase	)
															lowerCamelCase__       =       jnp.where(
															    input_ids_k[cur_len - 1] >= self.timestamp_begin	,  True and last_was_timestamp	,  _lowerCAmelCase	,  )

															lowerCamelCase__       =       jnp.where((cur_len - self.begin_index) < 2	,  _lowerCAmelCase	,  _lowerCAmelCase	)
															lowerCamelCase__       =       jnp.where(
															    input_ids_k[cur_len - 2] >= self.timestamp_begin	,  _lowerCAmelCase	,  _lowerCAmelCase	,  )

															return jnp.where(
															    _lowerCAmelCase	,  jnp.where(
															        penultimate_was_timestamp > 0	,  scores_k.at[self.timestamp_begin :].set(-float("inf"	)	)	,  scores_k.at[: self.eos_token_id].set(-float("inf"	)	)	,  )	,  _lowerCAmelCase	,  )

										lowerCamelCase__       =       jax.vmap(_lowerCAmelCase	)(_lowerCAmelCase	,  _lowerCAmelCase	)

										lowerCamelCase__       =       jnp.where(cur_len == self.begin_index	,  _lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       jnp.where(
										    self.max_initial_timestamp_index is not None	,  True and apply_max_initial_timestamp	,  _lowerCAmelCase	,  )

										lowerCamelCase__       =       self.timestamp_begin + self.max_initial_timestamp_index

										lowerCamelCase__       =       jnp.where(
										    _lowerCAmelCase	,  scores.at[:, last_allowed + 1 :].set(-float("inf"	)	)	,  _lowerCAmelCase	,  )

										# if sum of probability over timestamps is above any other token, sample timestamp
										lowerCamelCase__       =       jax.nn.log_softmax(_lowerCAmelCase	,  axis=-1	)

										def handle_cumulative_probs(_lowerCAmelCase	,  _lowerCAmelCase	):
															lowerCamelCase__       =       jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]	,  axis=-1	)
															lowerCamelCase__       =       jnp.max(logprobs_k[: self.timestamp_begin]	)
															return jnp.where(
															    timestamp_logprob > max_text_token_logprob	,  scores_k.at[: self.timestamp_begin].set(-float("inf"	)	)	,  _lowerCAmelCase	,  )

										lowerCamelCase__       =       jax.vmap(_lowerCAmelCase	)(_lowerCAmelCase	,  _lowerCAmelCase	)

										return scores

