





# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig





def    __UpperCamelCase      (    a,     a,     a=0) ->Optional[Any]:
					# Format the message.
					if name is None:
										lowerCamelCase__       =       None
					else:
										lowerCamelCase__       =       "." * max(0,     spaces - 2) + "# {:" + str(50 - spaces) + "s}"
										lowerCamelCase__       =       fmt.format(a)

					# Print and recurse (if needed).
					if isinstance(a,     a):
										if msg is not None:
															print(a)
										for k in val.keys():
															recursive_print(a,     val[k],     spaces + 2)
					elif isinstance(a,     torch.Tensor):
										print(a,     ":",     val.size())
					else:
										print(a,     ":",     a)





def    __UpperCamelCase      (    a,     a,     a,     a,     a) ->int:
					# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
					# for compatibility with later versions of NVIDIA Megatron-LM.
					# The inverse operation is performed inside Megatron-LM to read checkpoints:
					# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
					# If param is the weight tensor of the self-attention block, the returned tensor
					# will have to be transposed one more time to be read by HuggingFace GPT2.
					lowerCamelCase__       =       param.size()
					if checkpoint_version == 1.0:
										# version 1.0 stores [num_heads * hidden_size * num_splits, :]
										lowerCamelCase__       =       (num_heads, hidden_size, num_splits) + input_shape[1:]
										lowerCamelCase__       =       param.view(*a)
										lowerCamelCase__       =       param.transpose(0,     2)
										lowerCamelCase__       =       param.transpose(1,     2).contiguous()
					elif checkpoint_version >= 2.0:
										# other versions store [num_heads * num_splits * hidden_size, :]
										lowerCamelCase__       =       (num_heads, num_splits, hidden_size) + input_shape[1:]
										lowerCamelCase__       =       param.view(*a)
										lowerCamelCase__       =       param.transpose(0,     1).contiguous()
					lowerCamelCase__       =       param.view(*a)
					return param





def    __UpperCamelCase      (    a,     a,     a) ->List[str]:
					# The converted output model.
					lowerCamelCase__       =       {}

					# old versions did not store training args
					lowerCamelCase__       =       input_state_dict.get("args",     a)
					if ds_args is not None:
										# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
										# from pprint import pprint
										# pprint(vars(ds_args))

										lowerCamelCase__       =       ds_args.padded_vocab_size
										lowerCamelCase__       =       ds_args.max_position_embeddings
										lowerCamelCase__       =       ds_args.hidden_size
										lowerCamelCase__       =       ds_args.num_layers
										lowerCamelCase__       =       ds_args.num_attention_heads
										lowerCamelCase__       =       ds_args.ffn_hidden_size
										# pprint(config)

					# The number of heads.
					lowerCamelCase__       =       config.n_head
					# The hidden_size per head.
					lowerCamelCase__       =       config.n_embd // config.n_head
					# Megatron-LM checkpoint version
					if "checkpoint_version" in input_state_dict.keys():
										lowerCamelCase__       =       input_state_dict["checkpoint_version"]
					else:
										lowerCamelCase__       =       0.0

					# The model.
					lowerCamelCase__       =       input_state_dict["model"]
					# The language model.
					lowerCamelCase__       =       model["language_model"]
					# The embeddings.
					lowerCamelCase__       =       lm["embedding"]

					# The word embeddings.
					lowerCamelCase__       =       embeddings["word_embeddings"]["weight"]
					# Truncate the embedding table to vocab_size rows.
					lowerCamelCase__       =       word_embeddings[: config.vocab_size, :]
					lowerCamelCase__       =       word_embeddings

					# The position embeddings.
					lowerCamelCase__       =       embeddings["position_embeddings"]["weight"]
					# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
					lowerCamelCase__       =       pos_embeddings.size(0)
					if n_positions != config.n_positions:
										raise ValueError(
										    f"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match")
					# Store the position embeddings.
					lowerCamelCase__       =       pos_embeddings

					# The transformer.
					lowerCamelCase__       =       lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

					# The regex to extract layer names.
					lowerCamelCase__       =       re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)")

					# The simple map of names for "automated" rules.
					lowerCamelCase__       =       {
					    "attention.dense": ".attn.c_proj.",
					    "self_attention.dense": ".attn.c_proj.",
					    "mlp.dense_h_to_4h": ".mlp.c_fc.",
					    "mlp.dense_4h_to_h": ".mlp.c_proj.",
					}

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										lowerCamelCase__       =       layer_re.match(a)

										# Stop if that's not a layer
										if m is None:
															break

										# The index of the layer.
										lowerCamelCase__       =       int(m.group(1))
										# The name of the operation.
										lowerCamelCase__       =       m.group(2)
										# Is it a weight or a bias?
										lowerCamelCase__       =       m.group(3)

										# The name of the layer.
										lowerCamelCase__       =       f"transformer.h.{layer_idx}"

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith("layernorm"):
															lowerCamelCase__       =       "ln_1" if op_name.startswith("input") else "ln_2"
															lowerCamelCase__       =       val

										# Transpose the QKV matrix.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "weight":
															# Insert a tensor of 1x1xDxD bias.
															lowerCamelCase__       =       torch.tril(torch.ones((n_positions, n_positions),     dtype=torch.floataa)).view(
															    1,     1,     a,     a)
															lowerCamelCase__       =       causal_mask

															# Insert a "dummy" tensor for masked_bias.
															lowerCamelCase__       =       torch.tensor(-1E4,     dtype=torch.floataa)
															lowerCamelCase__       =       masked_bias

															lowerCamelCase__       =       fix_query_key_value_ordering(a,     a,     3,     a,     a)
															# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
															lowerCamelCase__       =       out_val.transpose(0,     1).contiguous()
															# Store.
															lowerCamelCase__       =       out_val

										# Transpose the bias.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "bias":
															lowerCamelCase__       =       fix_query_key_value_ordering(a,     a,     3,     a,     a)
															# Store. No change of shape.
															lowerCamelCase__       =       out_val

										# Transpose the weights.
										elif weight_or_bias == "weight":
															lowerCamelCase__       =       megatron_to_transformers[op_name]
															lowerCamelCase__       =       val.transpose(0,     1)

										# Copy the bias.
										elif weight_or_bias == "bias":
															lowerCamelCase__       =       megatron_to_transformers[op_name]
															lowerCamelCase__       =       val

    # DEBUG.
					assert config.n_layer == layer_idx + 1

					# The final layernorm.
					lowerCamelCase__       =       transformer["final_layernorm.weight"]
					lowerCamelCase__       =       transformer["final_layernorm.bias"]

					# For LM head, transformers' wants the matrix to weight embeddings.
					lowerCamelCase__       =       word_embeddings

					# It should be done!
					return output_state_dict





def    __UpperCamelCase      (    ) ->Dict:
					# Create the argument parser.
					lowerCamelCase__       =       argparse.ArgumentParser()
					parser.add_argument("--print-checkpoint-structure",     action="store_true")
					parser.add_argument(
					    "path_to_checkpoint",     type=a,     help="Path to the checkpoint file (.zip archive or direct .pt file)",     )
					parser.add_argument(
					    "--config_file",     default="",     type=a,     help="An optional config json file describing the pre-trained model.",     )
					lowerCamelCase__       =       parser.parse_args()

					# Extract the basename.
					lowerCamelCase__       =       os.path.dirname(args.path_to_checkpoint)

					# Load the model.
					# the .zip is very optional, let's keep it for backward compatibility
					print(f"Extracting PyTorch state dictionary from {args.path_to_checkpoint}")
					if args.path_to_checkpoint.endswith(".zip"):
										with zipfile.ZipFile(args.path_to_checkpoint,     "r") as checkpoint:
															with checkpoint.open("release/mp_rank_00/model_optim_rng.pt") as pytorch_dict:
																				lowerCamelCase__       =       torch.load(a,     map_location="cpu")
					else:
										lowerCamelCase__       =       torch.load(args.path_to_checkpoint,     map_location="cpu")

					lowerCamelCase__       =       input_state_dict.get("args",     a)

					# Read the config, or default to the model released by NVIDIA.
					if args.config_file == "":
										if ds_args is not None:
															if ds_args.bias_gelu_fusion:
																				lowerCamelCase__       =       "gelu_fast"
															elif ds_args.openai_gelu:
																				lowerCamelCase__       =       "gelu_new"
															else:
																				lowerCamelCase__       =       "gelu"
										else:
															# in the very early days this used to be "gelu_new"
															lowerCamelCase__       =       "gelu_new"

										# Spell out all parameters in case the defaults change.
										lowerCamelCase__       =       GPTaConfig(
										    vocab_size=50257,     n_positions=1024,     n_embd=1024,     n_layer=24,     n_head=16,     n_inner=4096,     activation_function=a,     resid_pdrop=0.1,     embd_pdrop=0.1,     attn_pdrop=0.1,     layer_norm_epsilon=1E-5,     initializer_range=0.0_2,     summary_type="cls_index",     summary_use_proj=a,     summary_activation=a,     summary_proj_to_labels=a,     summary_first_dropout=0.1,     scale_attn_weights=a,     use_cache=a,     bos_token_id=50256,     eos_token_id=50256,     )
					else:
										lowerCamelCase__       =       GPTaConfig.from_json_file(args.config_file)

					lowerCamelCase__       =       ["GPT2LMHeadModel"]

					# Convert.
					print("Converting")
					lowerCamelCase__       =       convert_megatron_checkpoint(a,     a,     a)

					# Print the structure of converted state dict.
					if args.print_checkpoint_structure:
										recursive_print(a,     a)

					# Add tokenizer class info to config
					# see https://github.com/huggingface/transformers/issues/13906)
					if ds_args is not None:
										lowerCamelCase__       =       ds_args.tokenizer_type
										if tokenizer_type == "GPT2BPETokenizer":
															lowerCamelCase__       =       "gpt2"
										elif tokenizer_type == "PretrainedFromHF":
															lowerCamelCase__       =       ds_args.tokenizer_name_or_path
										else:
															raise ValueError(f"Unrecognized tokenizer_type {tokenizer_type}")
					else:
										lowerCamelCase__       =       "gpt2"

					lowerCamelCase__       =       AutoTokenizer.from_pretrained(a)
					lowerCamelCase__       =       type(a).__name__
					lowerCamelCase__       =       tokenizer_class

					# Store the config to file.
					print("Saving config")
					config.save_pretrained(a)

					# Save tokenizer based on args
					print(f"Adding {tokenizer_class} tokenizer files")
					tokenizer.save_pretrained(a)

					# Store the state_dict to file.
					lowerCamelCase__       =       os.path.join(a,     "pytorch_model.bin")
					print(f"Saving checkpoint to \"{output_checkpoint_file}\"")
					torch.save(a,     a)


####################################################################################################

if __name__ == "__main__":
						main()

####################################################################################################








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

