import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


A_      =      trt.Logger(trt.Logger.WARNING)
A_      =      absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

A_      =      logging.getLogger(__name__)

A_      =      argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    "--onnx_model_path",
    default=None,
    type=str,
    required=True,
    help="Path to ONNX model: ",
)

parser.add_argument(
    "--output_dir",
    default=None,
    type=str,
    required=True,
    help="The output directory where the model checkpoints and predictions will be written.",
)

# Other parameters

parser.add_argument(
    "--tokenizer_name",
    default="",
    type=str,
    required=True,
    help="Pretrained tokenizer name or path if not the same as model_name",
)

parser.add_argument(
    "--version_2_with_negative",
    action="store_true",
    help="If true, the SQuAD examples contain some that do not have an answer.",
)
parser.add_argument(
    "--null_score_diff_threshold",
    type=float,
    default=0.0,
    help="If null_score - best_non_null is greater than the threshold predict null.",
)

parser.add_argument(
    "--max_seq_length",
    default=384,
    type=int,
    help=(
        "The maximum total input sequence length after WordPiece tokenization. Sequences "
        "longer than this will be truncated, and sequences shorter than this will be padded."
    ),
)
parser.add_argument(
    "--doc_stride",
    default=128,
    type=int,
    help="When splitting up a long document into chunks, how much stride to take between chunks.",
)

parser.add_argument("--per_device_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.")

parser.add_argument(
    "--n_best_size",
    default=20,
    type=int,
    help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
)
parser.add_argument(
    "--max_answer_length",
    default=30,
    type=int,
    help=(
        "The maximum length of an answer that can be generated. This is needed because the start "
        "and end predictions are not conditioned on one another."
    ),
)

parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")

parser.add_argument(
    "--dataset_name",
    type=str,
    default=None,
    required=True,
    help="The name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--dataset_config_name",
    type=str,
    default=None,
    help="The configuration name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--preprocessing_num_workers", type=int, default=4, help="A csv or a json file containing the training data."
)
parser.add_argument("--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets")
parser.add_argument(
    "--fp16",
    action="store_true",
    help="Whether to use 16-bit (mixed) precision instead of 32-bit",
)
parser.add_argument(
    "--int8",
    action="store_true",
    help="Whether to use INT8",
)

A_      =      parser.parse_args()

if args.tokenizer_name:
						A_      =      AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
						raise ValueError(
						    "You are instantiating a new tokenizer from scratch. This is not supported by this script."
						    "You can do it from another script, save it, and load it from here, using --tokenizer_name."
						)

logger.info("Training/evaluation parameters %s", args)

A_      =      args.per_device_eval_batch_size

A_      =      (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
A_      =      True

A_      =      "temp_engine/bert-fp32.engine"
if args.fpaa:
						A_      =      "temp_engine/bert-fp16.engine"
if args.inta:
						A_      =      "temp_engine/bert-int8.engine"

# import ONNX file
if not os.path.exists("temp_engine"):
						os.makedirs("temp_engine")

A_      =      1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
						with open(args.onnx_model_path, "rb") as model:
												if not parser.parse(model.read()):
																		for error in range(parser.num_errors):
																								print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
						A_      =      [network.get_input(i) for i in range(network.num_inputs)]
						A_      =      [_input.name for _input in network_inputs]  # ex: ["actual_input1"]

						with builder.create_builder_config() as config:
												A_      =      1 << 50
												if STRICT_TYPES:
																		config.set_flag(trt.BuilderFlag.STRICT_TYPES)
												if args.fpaa:
																		config.set_flag(trt.BuilderFlag.FPaa)
												if args.inta:
																		config.set_flag(trt.BuilderFlag.INTa)
												A_      =      builder.create_optimization_profile()
												config.add_optimization_profile(profile)
												for i in range(len(input_names)):
																		profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
												A_      =      builder.build_engine(network, config)

												# serialize_engine and store in file (can be directly loaded and deserialized):
												with open(engine_name, "wb") as f:
																		f.write(engine.serialize())






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







			lowercase							=		np.asarray(inputs['''input_ids'''],		dtype=np.intaa       )
			lowercase							=		np.asarray(inputs['''attention_mask'''],		dtype=np.intaa       )
			lowercase							=		np.asarray(inputs['''token_type_ids'''],		dtype=np.intaa       )

			# Copy inputs
			cuda.memcpy_htod_async(d_inputs[0],		input_ids.ravel(),		UpperCAmelCase       )
			cuda.memcpy_htod_async(d_inputs[1],		attention_mask.ravel(),		UpperCAmelCase       )
			cuda.memcpy_htod_async(d_inputs[2],		token_type_ids.ravel(),		UpperCAmelCase       )
			# start time
			lowercase							=		time.time()
			# Run inference
			context.execute_async(
			    bindings=[int(UpperCAmelCase       ) for d_inp in d_inputs] + [int(UpperCAmelCase       ), int(UpperCAmelCase       )],		stream_handle=stream.handle       )
			# Transfer predictions back from GPU
			cuda.memcpy_dtoh_async(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
			cuda.memcpy_dtoh_async(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
			# Synchronize the stream and take time
			stream.synchronize()
			# end time
			lowercase							=		time.time()
			lowercase							=		end_time - start_time
			lowercase							=		(h_outputa, h_outputa)
			# print(outputs)
			return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
A_      =      Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
						datasets.utils.logging.set_verbosity_warning()
						transformers.utils.logging.set_verbosity_info()
else:
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
						set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
						# Downloading and loading a dataset from the hub.
						A_      =      load_dataset(args.dataset_name, args.dataset_config_name)
else:
						raise ValueError("Evaluation requires a dataset name")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

A_      =      raw_datasets["validation"].column_names

A_      =      "question" if "question" in column_names else column_names[0]
A_      =      "context" if "context" in column_names else column_names[1]
A_      =      "answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
A_      =      tokenizer.padding_side == "right"

if args.max_seq_length > tokenizer.model_max_length:
						logger.warning(
						    F"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"
						    F"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
						)

A_      =      min(args.max_seq_length, tokenizer.model_max_length)






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Tuple:



			"""simple docstring"""







			lowercase							=		[q.lstrip() for q in examples[question_column_name]]

			# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
			# in one example possible giving several features when a context is long, each of those features having a
			# context that overlaps a bit the context of the previous feature.
			lowercase							=		tokenizer(
			    examples[question_column_name if pad_on_right else context_column_name],		examples[context_column_name if pad_on_right else question_column_name],		truncation='''only_second''' if pad_on_right else '''only_first''',		max_length=UpperCAmelCase,		stride=args.doc_stride,		return_overflowing_tokens=UpperCAmelCase,		return_offsets_mapping=UpperCAmelCase,		padding='''max_length''',		)

			# Since one example might give us several features if it has a long context, we need a map from a feature to
			# its corresponding example. This key gives us just that.
			lowercase							=		tokenized_examples.pop('''overflow_to_sample_mapping'''       )

			# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
			# corresponding example_id and we will store the offset mappings.
			lowercase							=		[]

			for i in range(len(tokenized_examples['''input_ids''']       )       ):
						# Grab the sequence corresponding to that example (to know what is the context and what is the question).
						lowercase							=		tokenized_examples.sequence_ids(UpperCAmelCase       )
						lowercase							=		1 if pad_on_right else 0

						# One example can give several spans, this is the index of the example containing this span of text.
						lowercase							=		sample_mapping[i]
						tokenized_examples["example_id"].append(examples['''id'''][sample_index]       )

						# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
						# position is part of the context or not.
						lowercase							=		[
						    (o if sequence_ids[k] == context_index else None)
						    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]       )
						]

			return tokenized_examples


A_      =      raw_datasets["validation"]
# Validation Feature Creation
A_      =      eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc="Running tokenizer on validation dataset",
)

A_      =      default_data_collator

A_      =      eval_dataset.remove_columns(["example_id", "offset_mapping"])
A_      =      DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase="eval"       )->       List[str]:



			"""simple docstring"""







			lowercase							=		postprocess_qa_predictions(
			    examples=UpperCAmelCase,		features=UpperCAmelCase,		predictions=UpperCAmelCase,		version_2_with_negative=args.version_2_with_negative,		n_best_size=args.n_best_size,		max_answer_length=args.max_answer_length,		null_score_diff_threshold=args.null_score_diff_threshold,		output_dir=args.output_dir,		prefix=UpperCAmelCase,		)
			# Format the result to the format the metric expects.
			if args.version_2_with_negative:
						lowercase							=		[
						    {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
						]
			else:
						lowercase							=		[{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

			lowercase							=		[{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
			return EvalPrediction(predictions=UpperCAmelCase,		label_ids=UpperCAmelCase       )


A_      =      load_metric("squad_v2" if args.version_2_with_negative else "squad")

# Evaluation!
logger.info("Loading ONNX model %s for evaluation", args.onnx_model_path)
with open(engine_name, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
						# setup for TRT inferrence
						for i in range(len(input_names)):
												context.set_binding_shape(i, INPUT_SHAPE)
						assert context.all_binding_shapes_specified






						def        __UpperCAmelCase    (       UpperCAmelCase       )->       List[Any]:



									"""simple docstring"""







									return trt.volume(engine.get_binding_shape(UpperCAmelCase       )       ) * engine.get_binding_dtype(UpperCAmelCase       ).itemsize

						# Allocate device memory for inputs and outputs.
						A_      =      [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

						# Allocate output buffer
						A_      =      cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
						A_      =      cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
						A_      =      cuda.mem_alloc(h_outputa.nbytes)
						A_      =      cuda.mem_alloc(h_outputa.nbytes)

						# Create a stream in which to copy inputs/outputs and run inference.
						A_      =      cuda.Stream()

						# Evaluation
						logger.info("***** Running Evaluation *****")
						logger.info(F"  Num examples = {len(eval_dataset)}")
						logger.info(F"  Batch size = {args.per_device_eval_batch_size}")

						A_      =      0.0
						A_      =      0
						A_      =      timeit.default_timer()

						A_      =      None
						for step, batch in enumerate(eval_dataloader):
												A_    ,    A_      =      model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
												total_time += infer_time
												niter += 1

												A_    ,    A_      =      outputs
												A_      =      torch.tensor(start_logits)
												A_      =      torch.tensor(end_logits)

												# necessary to pad predictions and labels for being gathered
												A_      =      accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
												A_      =      accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

												A_      =      (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
												A_      =      logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

						if all_preds is not None:
												A_      =      nested_truncate(all_preds, len(eval_dataset))

						A_      =      timeit.default_timer() - start_time
						logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(eval_dataset))
						# Inference time from TRT
						logger.info("Average Inference Time = {:.3f} ms".format(total_time * 1000 / niter))
						logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 1000))




						logger.info("Total Number of Inference =  %d", niter)

A_      =      post_processing_function(eval_examples, eval_dataset, all_preds)
A_      =      metric.compute(predictions=prediction.predictions, references=prediction.label_ids)




logger.info(F"Evaluation metrics: {eval_metric}")






import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


A_      =      {
    "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
    "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
    "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
    "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
    "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
    "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
    "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
    "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
    "large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
    "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Dict:



			"""simple docstring"""







			lowercase							=		['''layers''', '''blocks''']
			for k in ignore_keys:
						state_dict.pop(UpperCAmelCase,		UpperCAmelCase       )


A_      =      {
    "blocks": "layers",
    "mlp.0": "fc1",
    "mlp.2": "fc2",
    "mlp_ln": "final_layer_norm",
    ".attn.query": ".self_attn.q_proj",
    ".attn.key": ".self_attn.k_proj",
    ".attn.value": ".self_attn.v_proj",
    ".attn_ln": ".self_attn_layer_norm",
    ".attn.out": ".self_attn.out_proj",
    ".cross_attn.query": ".encoder_attn.q_proj",
    ".cross_attn.key": ".encoder_attn.k_proj",
    ".cross_attn.value": ".encoder_attn.v_proj",
    ".cross_attn_ln": ".encoder_attn_layer_norm",
    ".cross_attn.out": ".encoder_attn.out_proj",
    "decoder.ln.": "decoder.layer_norm.",
    "encoder.ln.": "encoder.layer_norm.",
    "token_embedding": "embed_tokens",
    "encoder.positional_embedding": "encoder.embed_positions.weight",
    "decoder.positional_embedding": "decoder.embed_positions.weight",
    "ln_post": "layer_norm",
}






def        __UpperCAmelCase    (       UpperCAmelCase       )->       List[str]:



			"""simple docstring"""







			lowercase							=		list(s_dict.keys()       )
			for key in keys:
						lowercase							=		key
						for k, v in WHISPER_MAPPING.items():
									if k in key:
												lowercase							=		new_key.replace(UpperCAmelCase,		UpperCAmelCase       )

						print(f'{key} -> {new_key}'       )

						lowercase							=		s_dict.pop(UpperCAmelCase       )
			return s_dict






def        __UpperCAmelCase    (       UpperCAmelCase       )->       str:



			"""simple docstring"""







			lowercase		,lowercase							=		emb.weight.shape
			lowercase							=		nn.Linear(UpperCAmelCase,		UpperCAmelCase,		bias=UpperCAmelCase       )
			lowercase							=		emb.weight.data
			return lin_layer






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       bytes:



			"""simple docstring"""







			os.makedirs(UpperCAmelCase,		exist_ok=UpperCAmelCase       )
			lowercase							=		os.path.basename(UpperCAmelCase       )

			lowercase							=		url.split('''/'''       )[-2]
			lowercase							=		os.path.join(UpperCAmelCase,		UpperCAmelCase       )

			if os.path.exists(UpperCAmelCase       ) and not os.path.isfile(UpperCAmelCase       ):
						raise RuntimeError(f'{download_target} exists and is not a regular file'       )

			if os.path.isfile(UpperCAmelCase       ):
						lowercase							=		open(UpperCAmelCase,		'''rb'''       ).read()
						if hashlib.shaaaa(UpperCAmelCase       ).hexdigest() == expected_shaaaa:
									return model_bytes
						else:
									warnings.warn(f'{download_target} exists, but the SHA256 checksum does not match; re-downloading the file'       )

			with urllib.request.urlopen(UpperCAmelCase       ) as source, open(UpperCAmelCase,		'''wb'''       ) as output:
						with tqdm(
						    total=int(source.info().get('''Content-Length'''       )       ),		ncols=80,		unit='''iB''',		unit_scale=UpperCAmelCase,		unit_divisor=1024       ) as loop:
									while True:
												lowercase							=		source.read(8192       )
												if not buffer:
															break

												output.write(UpperCAmelCase       )
												loop.update(len(UpperCAmelCase       )       )

			lowercase							=		open(UpperCAmelCase,		'''rb'''       ).read()
			if hashlib.shaaaa(UpperCAmelCase       ).hexdigest() != expected_shaaaa:
						raise RuntimeError(
						    '''Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.'''       )

			return model_bytes






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







			if ".pt" not in checkpoint_path:
						lowercase							=		_download(_MODELS[checkpoint_path]       )
			else:
						lowercase							=		torch.load(UpperCAmelCase,		map_location='''cpu'''       )
			lowercase							=		original_checkpoint['''dims''']
			lowercase							=		original_checkpoint['''model_state_dict''']
			lowercase							=		state_dict['''decoder.token_embedding.weight''']
			remove_ignore_keys_(UpperCAmelCase       )
			rename_keys(UpperCAmelCase       )
			lowercase							=		True
			lowercase							=		state_dict['''decoder.layers.0.fc1.weight'''].shape[0]

			lowercase							=		WhisperConfig(
			    vocab_size=dimensions['''n_vocab'''],		encoder_ffn_dim=UpperCAmelCase,		decoder_ffn_dim=UpperCAmelCase,		num_mel_bins=dimensions['''n_mels'''],		d_model=dimensions['''n_audio_state'''],		max_target_positions=dimensions['''n_text_ctx'''],		encoder_layers=dimensions['''n_audio_layer'''],		encoder_attention_heads=dimensions['''n_audio_head'''],		decoder_layers=dimensions['''n_text_layer'''],		decoder_attention_heads=dimensions['''n_text_state'''],		max_source_positions=dimensions['''n_audio_ctx'''],		)

			lowercase							=		WhisperForConditionalGeneration(UpperCAmelCase       )
			lowercase		,lowercase							=		model.model.load_state_dict(UpperCAmelCase,		strict=UpperCAmelCase       )
			if len(UpperCAmelCase       ) > 0 and not set(UpperCAmelCase       ) <= {
			    "encoder.embed_positions.weights",
			    "decoder.embed_positions.weights",
			}:
						raise ValueError(
						    '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
						    f' but all the following weights are missing {missing}'       )

			if tie_embeds:
						lowercase							=		make_linear_from_emb(model.model.decoder.embed_tokens       )
			else:
						lowercase							=		proj_out_weights

			model.save_pretrained(UpperCAmelCase       )


if __name__ == "__main__":
						A_      =      argparse.ArgumentParser()
						# # Required parameters
						parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
						parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						A_      =      parser.parse_args()

						convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)





