"""simple docstring"""




import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
			import tensorflow as tf
			from tensorflow.python.framework.errors_impl import ResourceExhaustedError

			from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
			import pyanvml.pyanvml as nvml

__lowerCAmelCase    :							Optional[int]													=						logging.get_logger(__name__)







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		def run_func(lowerCamelCase__     ):
				@wraps(lowerCamelCase__     )
				def run_in_eager_mode(*lowerCamelCase__			,  **lowerCamelCase__     ):
						return func(*lowerCamelCase__			,  **lowerCamelCase__     )

				@wraps(lowerCamelCase__     )
				@tf.function(experimental_compile=lowerCamelCase__     )
				def run_in_graph_mode(*lowerCamelCase__			,  **lowerCamelCase__     ):
						return func(*lowerCamelCase__			,  **lowerCamelCase__     )

				if do_eager_mode is True:
						if use_xla is not False:
								raise ValueError(
								    """Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."""     )
						return run_in_eager_mode
				else:
						return run_in_graph_mode

		return run_func







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   random.Random()
		lowerCAmelCase__        =   [rng.randint(0			,  vocab_size - 1     ) for i in range(batch_size * sequence_length     )]
		return tf.constant(lowerCamelCase__			,  shape=(batch_size, sequence_length)			,  dtype=tf.intaa     )






class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	TensorFlowBenchmarkArguments
					UpperCamelCase_ :	PretrainedConfig
					UpperCamelCase_ :	str			=    "TensorFlow"




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							return tf.__version__




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     str  ,						snake_case__						:     int  ,						snake_case__						:     int     ):
							# initialize GPU on separate process
							lowerCAmelCase__        =   self.args.strategy
							if strategy is None:
									raise ValueError("""A device strategy has to be initialized before using TensorFlow."""     )
							lowerCAmelCase__        =   self._prepare_inference_func(snake_case__  ,						snake_case__  ,						snake_case__     )
							return self._measure_speed(_inference     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     str  ,						snake_case__						:     int  ,						snake_case__						:     int     ):
							lowerCAmelCase__        =   self.args.strategy
							if strategy is None:
									raise ValueError("""A device strategy has to be initialized before using TensorFlow."""     )
							lowerCAmelCase__        =   self._prepare_train_func(snake_case__  ,						snake_case__  ,						snake_case__     )
							return self._measure_speed(_train     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     str  ,						snake_case__						:     int  ,						snake_case__						:     int     ):
							# initialize GPU on separate process
							if self.args.is_gpu:
									tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]  ,						snake_case__     )
							lowerCAmelCase__        =   self.args.strategy
							if strategy is None:
									raise ValueError("""A device strategy has to be initialized before using TensorFlow."""     )
							lowerCAmelCase__        =   self._prepare_inference_func(snake_case__  ,						snake_case__  ,						snake_case__     )
							return self._measure_memory(_inference     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     str  ,						snake_case__						:     int  ,						snake_case__						:     int     ):
							if self.args.is_gpu:
									tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]  ,						snake_case__     )
							lowerCAmelCase__        =   self.args.strategy
							if strategy is None:
									raise ValueError("""A device strategy has to be initialized before using TensorFlow."""     )

							lowerCAmelCase__        =   self._prepare_train_func(snake_case__  ,						snake_case__  ,						snake_case__     )
							return self._measure_memory(_train     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     str  ,						snake_case__						:     int  ,						snake_case__						:     int     ):
							lowerCAmelCase__        =   self.config_dict[model_name]

							if self.args.fpaa:
									raise NotImplementedError("""Mixed precision is currently not supported."""     )

							lowerCAmelCase__        =   (
							    hasattr(snake_case__  ,						"""architectures"""     )
							    and isinstance(config.architectures  ,						snake_case__     )
							    and len(config.architectures     ) > 0
							)
							if not self.args.only_pretrain_model and has_model_class_in_config:
									try:
											lowerCAmelCase__        =   """TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
											lowerCAmelCase__        =   __import__("""transformers"""  ,						fromlist=[model_class]     )
											lowerCAmelCase__        =   getattr(snake_case__  ,						snake_case__     )
											lowerCAmelCase__        =   model_cls(snake_case__     )
									except ImportError:
											raise ImportError(
											    F"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
											    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""     )
							else:
									lowerCAmelCase__        =   TF_MODEL_MAPPING[config.__class__](snake_case__     )

							# encoder-decoder has vocab size saved differently
							lowerCAmelCase__        =   config.vocab_size if hasattr(snake_case__  ,						"""vocab_size"""     ) else config.encoder.vocab_size
							lowerCAmelCase__        =   random_input_ids(snake_case__  ,						snake_case__  ,						snake_case__     )

							@run_with_tf_optimizations(self.args.eager_mode  ,						self.args.use_xla     )
							def encoder_decoder_forward():
									return model(snake_case__  ,						decoder_input_ids=snake_case__  ,						training=snake_case__     )

							@run_with_tf_optimizations(self.args.eager_mode  ,						self.args.use_xla     )
							def encoder_forward():
									return model(snake_case__  ,						training=snake_case__     )

							lowerCAmelCase__        =   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

							return _inference




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     str  ,						snake_case__						:     int  ,						snake_case__						:     int     ):
							lowerCAmelCase__        =   self.config_dict[model_name]

							if self.args.eager_mode is not False:
									raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."""     )

							if self.args.fpaa:
									raise NotImplementedError("""Mixed precision is currently not supported."""     )

							lowerCAmelCase__        =   (
							    hasattr(snake_case__  ,						"""architectures"""     )
							    and isinstance(config.architectures  ,						snake_case__     )
							    and len(config.architectures     ) > 0
							)
							if not self.args.only_pretrain_model and has_model_class_in_config:
									try:
											lowerCAmelCase__        =   """TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
											lowerCAmelCase__        =   __import__("""transformers"""  ,						fromlist=[model_class]     )
											lowerCAmelCase__        =   getattr(snake_case__  ,						snake_case__     )
											lowerCAmelCase__        =   model_cls(snake_case__     )
									except ImportError:
											raise ImportError(
											    F"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
											    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""     )
							else:
									lowerCAmelCase__        =   TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](snake_case__     )

							# encoder-decoder has vocab size saved differently
							lowerCAmelCase__        =   config.vocab_size if hasattr(snake_case__  ,						"""vocab_size"""     ) else config.encoder.vocab_size
							lowerCAmelCase__        =   random_input_ids(snake_case__  ,						snake_case__  ,						snake_case__     )

							@run_with_tf_optimizations(self.args.eager_mode  ,						self.args.use_xla     )
							def encoder_decoder_train():
									lowerCAmelCase__        =   model(snake_case__  ,						decoder_input_ids=snake_case__  ,						labels=snake_case__  ,						training=snake_case__     )[0]
									lowerCAmelCase__        =   tf.gradients(snake_case__  ,						model.trainable_variables     )
									return gradients

							@run_with_tf_optimizations(self.args.eager_mode  ,						self.args.use_xla     )
							def encoder_train():
									lowerCAmelCase__        =   model(snake_case__  ,						labels=snake_case__  ,						training=snake_case__     )[0]
									lowerCAmelCase__        =   tf.gradients(snake_case__  ,						model.trainable_variables     )
									return gradients

							lowerCAmelCase__        =   encoder_decoder_train if config.is_encoder_decoder else encoder_train

							return _train




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     Union[str, Any]     ):
							with self.args.strategy.scope():
									try:
											if self.args.is_tpu or self.args.use_xla:
													# run additional 10 times to stabilize compilation for tpu
													logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation"""     )
													timeit.repeat(snake_case__  ,						repeat=1  ,						number=5     )

											# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
											lowerCAmelCase__        =   timeit.repeat(
											    snake_case__  ,						repeat=self.args.repeat  ,						number=10  ,						)

											return min(snake_case__     ) / 10.0
									except ResourceExhaustedError as e:
											self.print_fn(F"""Doesn't fit on GPU. {e}"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Callable[[], None]     ):
							logger.info(
							    """Note that TensorFlow allocates more memory than """
							    """it might need to speed up computation. """
							    """The memory reported here corresponds to the memory """
							    """reported by `nvidia-smi`, which can vary depending """
							    """on total available memory on the GPU that is used."""     )
							with self.args.strategy.scope():
									try:
											if self.args.trace_memory_line_by_line:
													if not self.args.eager_mode:
															raise ValueError(
															    """`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
															    """ consumption line by line."""     )
													lowerCAmelCase__        =   start_memory_tracing("""transformers"""     )

											if self.args.is_tpu:
													# tpu
													raise NotImplementedError(
													    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
													    """ with `args.memory=False`"""     )
											elif self.args.is_gpu:
													# gpu
													if not is_pyanvml_available():
															logger.warning(
															    """py3nvml not installed, we won't log GPU memory usage. """
															    """Install py3nvml (pip install py3nvml) to log information about GPU."""     )
															lowerCAmelCase__        =   """N/A"""
													else:
															logger.info(
															    """Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
															    """ running on the same GPU."""     )
															# init nvml
															nvml.nvmlInit()
															func()
															lowerCAmelCase__        =   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx     )
															lowerCAmelCase__        =   nvml.nvmlDeviceGetMemoryInfo(snake_case__     )
															lowerCAmelCase__        =   meminfo.used
															lowerCAmelCase__        =   Memory(snake_case__     )
															# shutdown nvml
															nvml.nvmlShutdown()
											else:
													# cpu
													if self.args.trace_memory_line_by_line:
															logger.info(
															    """When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
															    """ TensorFlow."""     )
															lowerCAmelCase__        =   None
													else:
															lowerCAmelCase__        =   measure_peak_memory_cpu(snake_case__     )
															lowerCAmelCase__        =   Memory(snake_case__     ) if isinstance(snake_case__  ,						snake_case__     ) else memory_bytes
											if self.args.trace_memory_line_by_line:
													lowerCAmelCase__        =   stop_memory_tracing(snake_case__     )
													if memory is None:
															lowerCAmelCase__        =   summary.total
											else:
													lowerCAmelCase__        =   None

											return memory, summary
									except ResourceExhaustedError as e:
											self.print_fn(F"""Doesn't fit on GPU. {e}"""     )
											return "N/A", None





"""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)




