






import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
	import torch
	from torch import nn

	from transformers import (
	    Adafactor,
	    AdamW,
	    get_constant_schedule,
	    get_constant_schedule_with_warmup,
	    get_cosine_schedule_with_warmup,
	    get_cosine_with_hard_restarts_schedule_with_warmup,
	    get_inverse_sqrt_schedule,
	    get_linear_schedule_with_warmup,
	    get_polynomial_decay_schedule_with_warmup,
	)

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	List[Any]=10 ):
							snake_case__	  =       []
							for _ in range(a ):
														lrs.append(scheduler.get_lr()[0] )
														scheduler.step()
							return lrs

def _UpperCAmelCase   (	a						:	Any  ,					a						:	Union[str, Any]=10 ):
							snake_case__	  =       []
							for step in range(a ):
														lrs.append(scheduler.get_lr()[0] )
														scheduler.step()
														if step == num_steps // 2:
																					with tempfile.TemporaryDirectory() as tmpdirname:
																												snake_case__	  =       os.path.join(a  ,					"""schedule.bin""" )
																												torch.save(scheduler.state_dict()  ,					a )

																												snake_case__	  =       torch.load(a )
																												scheduler.load_state_dict(a )
							return lrs


@require_torch
class    _lowerCAmelCase			(		unittest.TestCase      ):



				"""simple docstring"""



				def 					__magic_name__							(      self    :    Dict			,		UpperCamelCase__    :    List[str]			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    Optional[int]):






											'''simple docstring'''







											self.assertEqual(len(UpperCamelCase__)			,		len(UpperCamelCase__))
											for a, b in zip(UpperCamelCase__			,		UpperCamelCase__):
																		self.assertAlmostEqual(UpperCamelCase__			,		UpperCamelCase__			,		delta=UpperCamelCase__)



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       torch.tensor([0.1, -0.2, -0.1]			,		requires_grad=UpperCamelCase__)
											snake_case__	  =       torch.tensor([0.4, 0.2, -0.5])
											snake_case__	  =       nn.MSELoss()
											# No warmup, constant schedule, no gradient clipping
											snake_case__	  =       AdamW(params=[w]			,		lr=2E-1			,		weight_decay=0.0)
											for _ in range(1_0_0):
																		snake_case__	  =       criterion(UpperCamelCase__			,		UpperCamelCase__)
																		loss.backward()
																		optimizer.step()
																		w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																		w.grad.zero_()
											self.assertListAlmostEqual(w.tolist()			,		[0.4, 0.2, -0.5]			,		tol=1E-2)



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											snake_case__	  =       torch.tensor([0.1, -0.2, -0.1]			,		requires_grad=UpperCamelCase__)
											snake_case__	  =       torch.tensor([0.4, 0.2, -0.5])
											snake_case__	  =       nn.MSELoss()
											# No warmup, constant schedule, no gradient clipping
											snake_case__	  =       Adafactor(
											    params=[w]			,		lr=1E-2			,		eps=(1E-30, 1E-3)			,		clip_threshold=1.0			,		decay_rate=-0.8			,		betaa=UpperCamelCase__			,		weight_decay=0.0			,		relative_step=UpperCamelCase__			,		scale_parameter=UpperCamelCase__			,		warmup_init=UpperCamelCase__			,		)
											for _ in range(1_0_0_0):
																		snake_case__	  =       criterion(UpperCamelCase__			,		UpperCamelCase__)
																		loss.backward()
																		optimizer.step()
																		w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																		w.grad.zero_()
											self.assertListAlmostEqual(w.tolist()			,		[0.4, 0.2, -0.5]			,		tol=1E-2)


@require_torch
class    _lowerCAmelCase			(		unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   List[Any]      =  nn.Linear(50     ,      50      ) if is_torch_available() else None
				_lowercase				:   List[str]      =  AdamW(m.parameters()     ,      lr=10.0      ) if is_torch_available() else None
				_lowercase				:   Optional[int]      =  10



				def 					__magic_name__							(      self    :    int			,		UpperCamelCase__    :    List[Any]			,		UpperCamelCase__    :    Tuple			,		UpperCamelCase__    :    Any			,		UpperCamelCase__    :    Optional[Any]=None):






											'''simple docstring'''







											self.assertEqual(len(UpperCamelCase__)			,		len(UpperCamelCase__))
											for a, b in zip(UpperCamelCase__			,		UpperCamelCase__):
																		self.assertAlmostEqual(UpperCamelCase__			,		UpperCamelCase__			,		delta=UpperCamelCase__			,		msg=UpperCamelCase__)



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       {"""num_warmup_steps""": 2, """num_training_steps""": 1_0}
											# schedulers doct format
											# function: (sched_args_dict, expected_learning_rates)
											snake_case__	  =       {
											    get_constant_schedule: ({}, [10.0] * self.num_steps),
											    get_constant_schedule_with_warmup: (
											        {"""num_warmup_steps""": 4},
											        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
											    ),
											    get_linear_schedule_with_warmup: (
											        {**common_kwargs},
											        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
											    ),
											    get_cosine_schedule_with_warmup: (
											        {**common_kwargs},
											        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
											    ),
											    get_cosine_with_hard_restarts_schedule_with_warmup: (
											        {**common_kwargs, """num_cycles""": 2},
											        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
											    ),
											    get_polynomial_decay_schedule_with_warmup: (
											        {**common_kwargs, """power""": 2.0, """lr_end""": 1E-7},
											        [0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
											    ),
											    get_inverse_sqrt_schedule: (
											        {"""num_warmup_steps""": 2},
											        [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
											    ),
											}

											for scheduler_func, data in scheds.items():
																		snake_case__  , snake_case__	  =       data

																		snake_case__	  =       scheduler_func(self.optimizer			,		**UpperCamelCase__)
																		self.assertEqual(len([scheduler.get_lr()[0]])			,		1)
																		snake_case__	  =       unwrap_schedule(UpperCamelCase__			,		self.num_steps)
																		self.assertListAlmostEqual(
																		    UpperCamelCase__			,		UpperCamelCase__			,		tol=1E-2			,		msg=F'''failed for {scheduler_func} in normal scheduler'''			,		)

																		snake_case__	  =       scheduler_func(self.optimizer			,		**UpperCamelCase__)
																		if scheduler_func.__name__ != "get_constant_schedule":
																									LambdaScheduleWrapper.wrap_scheduler(UpperCamelCase__)  # wrap to test picklability of the schedule
																		snake_case__	  =       unwrap_and_save_reload_schedule(UpperCamelCase__			,		self.num_steps)
																		self.assertListEqual(UpperCamelCase__			,		UpperCamelCase__			,		msg=F'''failed for {scheduler_func} in save and reload''')


class    _lowerCAmelCase			:



				"""simple docstring"""



				def __init__(      self    :    List[str]			,		UpperCamelCase__    :    str):






											'''simple docstring'''







											snake_case__	  =       fn



				def __call__(      self    :    Dict			,		*UpperCamelCase__    :    Any			,		**UpperCamelCase__    :    Any):






											'''simple docstring'''







											return self.fn(*UpperCamelCase__			,		**UpperCamelCase__)



				@classmethod
				def 					__magic_name__							(      self    :    Optional[int]			,		UpperCamelCase__    :    Dict):






											'''simple docstring'''







											snake_case__	  =       list(map(self			,		scheduler.lr_lambdas))








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   (	a						:	Optional[Any]  ,					a						:	Dict  ,					a						:	Union[str, Any]=None ):
							# set parameter of one layer
							assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
							snake_case__	  =       nn.Parameter(a )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
														snake_case__	  =       nn.Parameter(a )

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	List[str]  ,					a						:	List[Any] ):
							# set torch weights for 1-to-1 comparison
							snake_case__	  =       np.asarray(weights[0] )
							snake_case__	  =       np.asarray(weights[1] )
							snake_case__	  =       np.asarray(weights[2] )

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

def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Dict  ,					a						:	Optional[int] ):
							# set torch weights for 1-to-1 comparison
							snake_case__	  =       np.asarray(weights[0] )
							snake_case__	  =       np.asarray(weights[1] )
							snake_case__	  =       np.asarray(weights[2] )
							snake_case__	  =       np.asarray(weights[3] )

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

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Tuple  ,					a						:	List[Any] ):
							# layernorm 1
							snake_case__	  =       weights[0][0][0]
							snake_case__	  =       np.asarray(layer_norm_a[0] )
							snake_case__	  =       np.asarray(layer_norm_a[1] )
							set_param(
							    torch_block.attention.layer_norm  ,					torch.tensor(a )  ,					torch.tensor(a )  ,					)

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

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

							# Chunked Feed Forward
							if len(a ) == 4:
														snake_case__	  =       intermediate_weights[2]

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

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

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

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Dict  ,					a						:	Union[str, Any] ):
							# reformer model
							snake_case__	  =       torch_model.reformer

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

							if isinstance(weights[3]  ,					a ):
														snake_case__	  =       torch_model_reformer.embeddings.position_embeddings
														for emb_idx in range(len(position_embeddings.weights ) ):
																					snake_case__	  =       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'''
																					snake_case__	  =       nn.Parameter(torch.tensor(a ) )

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

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

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

def _UpperCAmelCase   (	a						:	Any  ,					a						:	str  ,					a						:	str ):
							# Initialise PyTorch model
							snake_case__	  =       ReformerConfig.from_json_file(a )
							print(F'''Building PyTorch model from configuration: {config}''' )
							snake_case__	  =       ReformerModelWithLMHead(a )

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

							set_model_weights_in_torch(a  ,					a  ,					config.hidden_size )

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							torch.save(model.state_dict()  ,					a )


if __name__ == "__main__":
	a__       =     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."""
	)
	a__       =     parser.parse_args()
	convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
