"""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


__lowerCAmelCase    :							Optional[Any]													=						trt.Logger(trt.Logger.WARNING)
__lowerCAmelCase    :							int													=						absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

__lowerCAmelCase    :							Dict													=						logging.getLogger(__name__)

__lowerCAmelCase    :							Optional[Any]													=						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=3_84,
    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=1_28,
    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",
)

__lowerCAmelCase    :							Tuple													=						parser.parse_args()

if args.tokenizer_name:
			__lowerCAmelCase    :							List[str]													=						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)

__lowerCAmelCase    :							int													=						args.per_device_eval_batch_size

__lowerCAmelCase    :							Union[str, Any]													=						(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__lowerCAmelCase    :							Dict													=						True

__lowerCAmelCase    :							Dict													=						"temp_engine/bert-fp32.engine"
if args.fpaa:
			__lowerCAmelCase    :							Any													=						"temp_engine/bert-fp16.engine"
if args.inta:
			__lowerCAmelCase    :							Optional[Any]													=						"temp_engine/bert-int8.engine"

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

__lowerCAmelCase    :							Optional[int]													=						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
			__lowerCAmelCase    :							Optional[Any]													=						[network.get_input(i) for i in range(network.num_inputs)]
			__lowerCAmelCase    :							List[Any]													=						[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

			with builder.create_builder_config() as config:
						__lowerCAmelCase    :							str													=						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)
						__lowerCAmelCase    :							Any													=						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)
						__lowerCAmelCase    :							Any													=						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		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   np.asarray(inputs["""input_ids"""]			,  dtype=np.intaa     )
		lowerCAmelCase__        =   np.asarray(inputs["""attention_mask"""]			,  dtype=np.intaa     )
		lowerCAmelCase__        =   np.asarray(inputs["""token_type_ids"""]			,  dtype=np.intaa     )

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

__lowerCAmelCase    :							Dict													=						raw_datasets["validation"].column_names

__lowerCAmelCase    :							Any													=						"question" if "question" in column_names else column_names[0]
__lowerCAmelCase    :							List[str]													=						"context" if "context" in column_names else column_names[1]
__lowerCAmelCase    :							Any													=						"answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
__lowerCAmelCase    :							Optional[Any]													=						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}."
			)

__lowerCAmelCase    :							int													=						min(args.max_seq_length, tokenizer.model_max_length)







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   [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.
		lowerCAmelCase__        =   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=lowerCamelCase__			,  stride=args.doc_stride			,  return_overflowing_tokens=lowerCamelCase__			,  return_offsets_mapping=lowerCamelCase__			,  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.
		lowerCAmelCase__        =   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.
		lowerCAmelCase__        =   []

		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).
				lowerCAmelCase__        =   tokenized_examples.sequence_ids(lowerCamelCase__     )
				lowerCAmelCase__        =   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.
				lowerCAmelCase__        =   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.
				lowerCAmelCase__        =   [
				    (o if sequence_ids[k] == context_index else None)
				    for k, o in enumerate(tokenized_examples["""offset_mapping"""][i]     )
				]

		return tokenized_examples


__lowerCAmelCase    :							List[str]													=						raw_datasets["validation"]
# Validation Feature Creation
__lowerCAmelCase    :							str													=						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",
)

__lowerCAmelCase    :							Optional[Any]													=						default_data_collator

__lowerCAmelCase    :							int													=						eval_dataset.remove_columns(["example_id", "offset_mapping"])
__lowerCAmelCase    :							Any													=						DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__="eval"     ):





		"""simple docstring"""




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

		lowerCAmelCase__        =   [{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
		return EvalPrediction(predictions=lowerCamelCase__			,  label_ids=lowerCamelCase__     )


__lowerCAmelCase    :							List[Any]													=						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		(       lowerCamelCase__     ):





					"""simple docstring"""




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

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

			# Allocate output buffer
			__lowerCAmelCase    :							int													=						cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
			__lowerCAmelCase    :							List[str]													=						cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
			__lowerCAmelCase    :							Optional[Any]													=						cuda.mem_alloc(h_outputa.nbytes)
			__lowerCAmelCase    :							Union[str, Any]													=						cuda.mem_alloc(h_outputa.nbytes)

			# Create a stream in which to copy inputs/outputs and run inference.
			__lowerCAmelCase    :							Any													=						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}")

			__lowerCAmelCase    :							List[str]													=						0.0
			__lowerCAmelCase    :							str													=						0
			__lowerCAmelCase    :							Dict													=						timeit.default_timer()

			__lowerCAmelCase    :							List[str]													=						None
			for step, batch in enumerate(eval_dataloader):
						__lowerCAmelCase							,	__lowerCAmelCase    :							Tuple													=						model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
						total_time += infer_time
						niter += 1

						__lowerCAmelCase							,	__lowerCAmelCase    :							List[str]													=						outputs
						__lowerCAmelCase    :							Dict													=						torch.tensor(start_logits)
						__lowerCAmelCase    :							List[str]													=						torch.tensor(end_logits)

						# necessary to pad predictions and labels for being gathered
						__lowerCAmelCase    :							List[str]													=						accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
						__lowerCAmelCase    :							int													=						accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

						__lowerCAmelCase    :							Union[str, Any]													=						(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
						__lowerCAmelCase    :							Union[str, Any]													=						logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

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

			__lowerCAmelCase    :							str													=						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 * 10_00 / niter))
			logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 10_00))






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

__lowerCAmelCase    :							Any													=						post_processing_function(eval_examples, eval_dataset, all_preds)
__lowerCAmelCase    :							List[Any]													=						metric.compute(predictions=prediction.predictions, references=prediction.label_ids)






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





"""simple docstring"""




import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=None     ):





		"""simple docstring"""




		assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
		lowerCAmelCase__        =   nn.Parameter(lowerCamelCase__     )
		if bias is not None:
				assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
				lowerCAmelCase__        =   nn.Parameter(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   np.asarray(weights[0]     )
		lowerCAmelCase__        =   np.asarray(weights[1]     )
		lowerCAmelCase__        =   np.asarray(weights[2]     )

		set_param(
		    torch_layer.self_attention.query_key			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.self_attention.value			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.output.dense			,  torch.tensor(lowerCamelCase__     ).view(-1			,  lowerCamelCase__     ).contiguous().transpose(0			,  1     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   np.asarray(weights[0]     )
		lowerCAmelCase__        =   np.asarray(weights[1]     )
		lowerCAmelCase__        =   np.asarray(weights[2]     )
		lowerCAmelCase__        =   np.asarray(weights[3]     )

		set_param(
		    torch_layer.self_attention.query			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.self_attention.key			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.self_attention.value			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.output.dense			,  torch.tensor(lowerCamelCase__     ).view(-1			,  lowerCamelCase__     ).contiguous().transpose(0			,  1     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   weights[0][0][0]
		lowerCAmelCase__        =   np.asarray(layer_norm_a[0]     )
		lowerCAmelCase__        =   np.asarray(layer_norm_a[1]     )
		set_param(
		    torch_block.attention.layer_norm			,  torch.tensor(lowerCamelCase__     )			,  torch.tensor(lowerCamelCase__     )			,  )

		# lsh weights + output
		lowerCAmelCase__        =   weights[0][1]
		if len(lowerCamelCase__     ) < 4:
				set_layer_weights_in_torch_lsh(lowerCamelCase__			,  torch_block.attention			,  lowerCamelCase__     )
		else:
				set_layer_weights_in_torch_local(lowerCamelCase__			,  torch_block.attention			,  lowerCamelCase__     )

		# intermediate weighs
		lowerCAmelCase__        =   weights[2][0][1][2]

		# Chunked Feed Forward
		if len(lowerCamelCase__     ) == 4:
				lowerCAmelCase__        =   intermediate_weights[2]

		# layernorm 2
		lowerCAmelCase__        =   np.asarray(intermediate_weights[0][0]     )
		lowerCAmelCase__        =   np.asarray(intermediate_weights[0][1]     )
		set_param(
		    torch_block.feed_forward.layer_norm			,  torch.tensor(lowerCamelCase__     )			,  torch.tensor(lowerCamelCase__     )			,  )

		# intermediate dense
		lowerCAmelCase__        =   np.asarray(intermediate_weights[1][0]     )
		lowerCAmelCase__        =   np.asarray(intermediate_weights[1][1]     )
		set_param(
		    torch_block.feed_forward.dense.dense			,  torch.tensor(lowerCamelCase__     ).transpose(0			,  1     ).contiguous()			,  torch.tensor(lowerCamelCase__     )			,  )

		# intermediate out
		lowerCAmelCase__        =   np.asarray(intermediate_weights[4][0]     )
		lowerCAmelCase__        =   np.asarray(intermediate_weights[4][1]     )
		set_param(
		    torch_block.feed_forward.output.dense			,  torch.tensor(lowerCamelCase__     ).transpose(0			,  1     ).contiguous()			,  torch.tensor(lowerCamelCase__     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   torch_model.reformer

		# word embeds
		lowerCAmelCase__        =   np.asarray(weights[1]     )
		set_param(
		    torch_model_reformer.embeddings.word_embeddings			,  torch.tensor(lowerCamelCase__     )			,  )

		if isinstance(weights[3]			,  lowerCamelCase__     ):
				lowerCAmelCase__        =   torch_model_reformer.embeddings.position_embeddings
				for emb_idx in range(len(position_embeddings.weights     )     ):
						lowerCAmelCase__        =   np.asarray(weights[3][emb_idx][0]     )
						assert (
						    position_embeddings.weights[emb_idx].shape == emb_weights.shape
						), f"""{position_embeddings[emb_idx]} emb does not match"""
						lowerCAmelCase__        =   nn.Parameter(torch.tensor(lowerCamelCase__     )     )

		lowerCAmelCase__        =   weights[5]
		assert len(torch_model_reformer.encoder.layers     ) * 4 == len(
		    lowerCamelCase__     ), "HF and trax model do not have the same number of layers"
		for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers     ):
				lowerCAmelCase__        =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
				set_block_weights_in_torch(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )

		# output layer norm
		lowerCAmelCase__        =   np.asarray(weights[7][0]     )
		lowerCAmelCase__        =   np.asarray(weights[7][1]     )
		set_param(
		    torch_model_reformer.encoder.layer_norm			,  torch.tensor(lowerCamelCase__     )			,  torch.tensor(lowerCamelCase__     )			,  )

		# output embeddings
		lowerCAmelCase__        =   np.asarray(weights[9][0]     )
		lowerCAmelCase__        =   np.asarray(weights[9][1]     )
		set_param(
		    torch_model.lm_head.decoder			,  torch.tensor(lowerCamelCase__     ).transpose(0			,  1     ).contiguous()			,  torch.tensor(lowerCamelCase__     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   ReformerConfig.from_json_file(lowerCamelCase__     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		lowerCAmelCase__        =   ReformerModelWithLMHead(lowerCamelCase__     )

		with open(lowerCamelCase__			,  """rb"""     ) as f:
				lowerCAmelCase__        =   pickle.load(lowerCamelCase__     )["""weights"""]

		set_model_weights_in_torch(lowerCamelCase__			,  lowerCamelCase__			,  config.hidden_size     )

		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
		torch.save(model.state_dict()			,  lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained Reformer model. \n"
			        "This specifies the model architecture."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__lowerCAmelCase    :							Union[str, Any]													=						parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




