
"""simple docstring"""





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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						Tuple:


					"""simple docstring"""





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

					# Copy inputs
					cuda.memcpy_htod_async(d_inputs[0],	input_ids.ravel(),	__snake_case      )
					cuda.memcpy_htod_async(d_inputs[1],	attention_mask.ravel(),	__snake_case      )
					cuda.memcpy_htod_async(d_inputs[2],	token_type_ids.ravel(),	__snake_case      )
					# start time
					_UpperCamelCase									=    time.time()
					# Run inference
					context.execute_async(
					    bindings=[int(__snake_case      ) for d_inp in d_inputs] + [int(__snake_case      ), int(__snake_case      )],	stream_handle=stream.handle      )
					# Transfer predictions back from GPU
					cuda.memcpy_dtoh_async(__snake_case,	__snake_case,	__snake_case      )
					cuda.memcpy_dtoh_async(__snake_case,	__snake_case,	__snake_case      )
					# Synchronize the stream and take time
					stream.synchronize()
					# end time
					_UpperCamelCase									=    time.time()
					_UpperCamelCase									=    end_time - start_time
					_UpperCamelCase									=    (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  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    [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.
					_UpperCamelCase									=    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=__snake_case,	stride=args.doc_stride,	return_overflowing_tokens=__snake_case,	return_offsets_mapping=__snake_case,	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.
					_UpperCamelCase									=    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.
					_UpperCamelCase									=    []

					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).
										_UpperCamelCase									=    tokenized_examples.sequence_ids(__snake_case      )
										_UpperCamelCase									=    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.
										_UpperCamelCase									=    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.
										_UpperCamelCase									=    [
										    (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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case="eval"      )				->						str:


					"""simple docstring"""





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

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


_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  lowerCamelCase__						(     __snake_case      )				->						List[str]:


									"""simple docstring"""





									return trt.volume(engine.get_binding_shape(__snake_case      )      ) * engine.get_binding_dtype(__snake_case      ).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}""")








"""simple docstring"""





import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    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.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.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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						Tuple:


					"""simple docstring"""





					for attribute in key.split('''.'''      ):
										_UpperCamelCase									=    getattr(__snake_case,	__snake_case      )

					if weight_type is not None:
										_UpperCamelCase									=    getattr(__snake_case,	__snake_case      ).shape
					else:
										_UpperCamelCase									=    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":
										_UpperCamelCase									=    value
					elif weight_type == "weight_g":
										_UpperCamelCase									=    value
					elif weight_type == "weight_v":
										_UpperCamelCase									=    value
					elif weight_type == "bias":
										_UpperCamelCase									=    value
					else:
										_UpperCamelCase									=    value

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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						List[str]:


					"""simple docstring"""





					_UpperCamelCase									=    []
					_UpperCamelCase									=    fairseq_model.state_dict()

					_UpperCamelCase									=    hf_model.feature_extractor
					_UpperCamelCase									=    hf_model.adapter

					for name, value in fairseq_dict.items():
										_UpperCamelCase									=    False
										if "conv_layers" in name:
															load_conv_layer(
															    __snake_case,	__snake_case,	__snake_case,	__snake_case,	hf_model.config.feat_extract_norm == '''group''',	)
															_UpperCamelCase									=    True
										elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']      ):
															load_adapter(__snake_case,	__snake_case,	__snake_case,	__snake_case      )
															_UpperCamelCase									=    True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split('''w2v_model.'''      )[-1] == name.split('''.'''      )[0]:
																									_UpperCamelCase									=    True
																									if "*" in mapped_key:
																														_UpperCamelCase									=    name.split(__snake_case      )[0].split('''.'''      )[-2]
																														_UpperCamelCase									=    mapped_key.replace('''*''',	__snake_case      )
																									if "weight_g" in name:
																														_UpperCamelCase									=    '''weight_g'''
																									elif "weight_v" in name:
																														_UpperCamelCase									=    '''weight_v'''
																									elif "bias" in name:
																														_UpperCamelCase									=    '''bias'''
																									elif "weight" in name:
																														_UpperCamelCase									=    '''weight'''
																									else:
																														_UpperCamelCase									=    None
																									set_recursively(__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )
																				continue
										if not is_used:
															unused_weights.append(__snake_case      )

					logger.warning(F'''Unused weights: {unused_weights}'''      )



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    full_name.split('''conv_layers.'''      )[-1]
					_UpperCamelCase									=    name.split('''.'''      )
					_UpperCamelCase									=    int(items[0]      )
					_UpperCamelCase									=    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.'''
															)
															_UpperCamelCase									=    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.'''
															)
															_UpperCamelCase									=    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."
															)
															_UpperCamelCase									=    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.'''
															)
															_UpperCamelCase									=    value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
					else:
										unused_weights.append(__snake_case      )



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						Dict:


					"""simple docstring"""





					_UpperCamelCase									=    full_name.split('''adaptor.'''      )[-1]
					_UpperCamelCase									=    name.split('''.'''      )

					if items[1].isdigit():
										_UpperCamelCase									=    int(items[1]      )
					else:
										_UpperCamelCase									=    None

					if "adaptor" not in full_name:
										if "proj_ln" in full_name:
															# has to be layer norm
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.bias.data.shape
																				), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
																				_UpperCamelCase									=    value
																				logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.'''      )
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.weight.data.shape
																				), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
																				_UpperCamelCase									=    value
										else:
															# has to be projection layer
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj.bias.data.shape
																				), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
																				_UpperCamelCase									=    value
																				logger.info(F'''Adapter proj layer bias was initialized from {full_name}.'''      )
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj.weight.data.shape
																				), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
																				_UpperCamelCase									=    value
																				logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''      )
					elif isinstance(__snake_case,	__snake_case      ):
										if "bias" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.bias.data.shape
															), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
															_UpperCamelCase									=    value
															logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''      )
										elif "weight" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.weight.data.shape
															), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
															_UpperCamelCase									=    value
															logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''      )
					else:
										unused_weights.append(__snake_case      )



def  lowerCamelCase__						(     __snake_case      )				->						str:


					"""simple docstring"""





					_UpperCamelCase					,    _UpperCamelCase									=    emb.weight.shape
					_UpperCamelCase									=    nn.Linear(__snake_case,	__snake_case,	bias=__snake_case      )
					_UpperCamelCase									=    emb.weight.data
					return lin_layer



@torch.no_grad()
def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	)				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    WavaVecaConfig.from_pretrained(
					    __snake_case,	add_adapter=__snake_case,	adapter_stride=__snake_case,	adapter_kernel_size=__snake_case,	use_auth_token=__snake_case,	output_hidden_size=__snake_case,	)
					_UpperCamelCase									=    MBartConfig.from_pretrained(__snake_case      )

					# load model
					_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path],	arg_overrides={
					        '''config_yaml''': config_yaml_path,
					        '''data''': '''/'''.join(dict_path.split('''/'''      )[:-1]      ),
					        '''w2v_path''': checkpoint_path,
					        '''load_pretrained_decoder_from''': None,
					    },	)
					_UpperCamelCase									=    model[0].eval()

					# load feature extractor
					_UpperCamelCase									=    WavaVecaFeatureExtractor.from_pretrained(__snake_case,	use_auth_token=__snake_case      )

					# set weights for wav2vec2 encoder
					_UpperCamelCase									=    WavaVecaModel(__snake_case      )

					recursively_load_weights_wavaveca(model.encoder,	__snake_case      )

					# load decoder weights
					_UpperCamelCase									=    MBartForCausalLM(__snake_case      )
					_UpperCamelCase					,    _UpperCamelCase									=    hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),	strict=__snake_case      )
					logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}'''      )
					logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''      )

					_UpperCamelCase									=    SpeechEncoderDecoderModel(encoder=__snake_case,	decoder=__snake_case      )
					_UpperCamelCase									=    False

					_UpperCamelCase									=    MBartaaTokenizer(__snake_case      )
					tokenizer.save_pretrained(__snake_case      )

					_UpperCamelCase									=    hf_wavavec.config.to_dict()
					_UpperCamelCase									=    tokenizer.pad_token_id
					_UpperCamelCase									=    tokenizer.bos_token_id
					_UpperCamelCase									=    tokenizer.eos_token_id
					_UpperCamelCase									=    '''mbart50'''
					_UpperCamelCase									=    '''wav2vec2'''

					_UpperCamelCase									=    tokenizer.eos_token_id
					_UpperCamelCase									=    25_00_04
					_UpperCamelCase									=    tokenizer.eos_token_id

					_UpperCamelCase									=    SpeechEncoderDecoderConfig.from_dict(__snake_case      )

					hf_wavavec.save_pretrained(__snake_case      )
					feature_extractor.save_pretrained(__snake_case      )


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_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
				parser.add_argument(
				    """--encoder_config_path""",
				    default="""facebook/wav2vec2-xls-r-1b""",
				    type=str,
				    help="""Path to hf encoder wav2vec2 checkpoint config""",
				)
				parser.add_argument(
				    """--decoder_config_path""",
				    default="""facebook/mbart-large-50-one-to-many-mmt""",
				    type=str,
				    help="""Path to hf decoder checkpoint config""",
				)
				parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
				parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
				parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
				parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""")
				parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""")

				_a								=						parser.parse_args()
				convert_wavaveca_checkpoint(
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.dict_path,
				    args.config_yaml_path,
				    encoder_config_path=args.encoder_config_path,
				    decoder_config_path=args.decoder_config_path,
				    add_adapter=args.add_adapter,
				    adapter_kernel_size=args.adapter_kernel_size,
				    adapter_stride=args.adapter_stride,
				    decoder_start_token_id=args.start_token_id,
				    encoder_output_dim=args.encoder_output_dim,
				)






