





'''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 		__a(SCREAMING_SNAKE_CASE_     :    Optional[Any]						,				SCREAMING_SNAKE_CASE_     :    Optional[Any]						,				SCREAMING_SNAKE_CASE_     :    List[str]=None			):



    '''simple docstring'''





    assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
    _lowerCAmelCase			=					nn.Parameter(A_			)
    if bias is not None:
        assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
        _lowerCAmelCase			=					nn.Parameter(A_			)


def 		__a(SCREAMING_SNAKE_CASE_     :    Tuple						,				SCREAMING_SNAKE_CASE_     :    Any						,				SCREAMING_SNAKE_CASE_     :    Optional[Any]			):



    '''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(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 		__a(SCREAMING_SNAKE_CASE_     :    List[Any]						,				SCREAMING_SNAKE_CASE_     :    Union[str, Any]						,				SCREAMING_SNAKE_CASE_     :    List[str]			):



    '''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(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 		__a(SCREAMING_SNAKE_CASE_     :    Optional[int]						,				SCREAMING_SNAKE_CASE_     :    List[Any]						,				SCREAMING_SNAKE_CASE_     :    List[Any]			):



    '''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(A_			)						,				torch.tensor(A_			)						,				)

    # lsh weights + output
    _lowerCAmelCase			=					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
    _lowerCAmelCase			=					weights[2][0][1][2]

    # Chunked Feed Forward
    if len(A_			) == 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(A_			)						,				torch.tensor(A_			)						,				)

    # 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(A_			).transpose(0						,				1			).contiguous()						,				torch.tensor(A_			)						,				)

    # 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(A_			).transpose(0						,				1			).contiguous()						,				torch.tensor(A_			)						,				)


def 		__a(SCREAMING_SNAKE_CASE_     :    Any						,				SCREAMING_SNAKE_CASE_     :    str						,				SCREAMING_SNAKE_CASE_     :    Any			):



    '''simple docstring'''





    _lowerCAmelCase			=					torch_model.reformer

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

    if isinstance(weights[3]						,				A_			):
        _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(A_			)			)

    _lowerCAmelCase			=					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			):
        _lowerCAmelCase			=					trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
        set_block_weights_in_torch(A_						,				A_						,				A_			)

    # 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(A_			)						,				torch.tensor(A_			)						,				)

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


def 		__a(SCREAMING_SNAKE_CASE_     :    Tuple						,				SCREAMING_SNAKE_CASE_     :    str						,				SCREAMING_SNAKE_CASE_     :    List[Any]			):



    '''simple docstring'''





    _lowerCAmelCase			=					ReformerConfig.from_json_file(A_			)
    print(F'''Building PyTorch model from configuration: {config}'''			)
    _lowerCAmelCase			=					ReformerModelWithLMHead(A_			)

    with open(A_						,				"rb"			) as f:
        _lowerCAmelCase			=					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__":
       _SCREAMING_SNAKE_CASE		       =							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."
       )
       _SCREAMING_SNAKE_CASE		       =							parser.parse_args()
       convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)





"""simple docstring"""





import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				List[Any]   ,lowercase_			:				Tuple   ,lowercase_			:				Dict   ,lowercase_			:				str		):
						lowerCAmelCase__	:						int  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	process
						lowerCAmelCase__	:						Dict  				=	params







					def __len__(       self			:				Any		):
						return len(self.dataset		)







					def __getitem__(       self			:				Union[str, Any]   ,lowercase_			:				List[Any]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.dataset[i]
						lowerCAmelCase__	:						Optional[Any]  				=	self.process(lowercase_   ,**self.params		)
						return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Optional[int]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				List[Any]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				Tuple=None		):
						lowerCAmelCase__	:						List[Any]  				=	loader
						lowerCAmelCase__	:						int  				=	infer
						lowerCAmelCase__	:						List[str]  				=	params
						if loader_batch_size == 1:
							# Let's spare some time by deactivating altogether
							lowerCAmelCase__	:						int  				=	None
						lowerCAmelCase__	:						Dict  				=	loader_batch_size

						# Internal bookkeeping
						lowerCAmelCase__	:						Optional[Any]  				=	None
						lowerCAmelCase__	:						Optional[int]  				=	None







					def __len__(       self			:				Union[str, Any]		):
						return len(self.loader		)







					def __iter__(       self			:				List[Any]		):
						lowerCAmelCase__	:						List[Any]  				=	iter(self.loader		)
						return self







					def        __lowerCAmelCase (       self			:				Tuple		):
						if isinstance(self._loader_batch_data   ,torch.Tensor		):
							# Batch data is simple tensor, just fetch the slice
							lowerCAmelCase__	:						Tuple  				=	self._loader_batch_data[self._loader_batch_index]
						else:
							# Batch data is assumed to be BaseModelOutput (or dict)
							lowerCAmelCase__	:						int  				=	{}
							for k, element in self._loader_batch_data.items():
								if isinstance(lowercase_   ,lowercase_		):
									# Convert ModelOutput to tuple first
									lowerCAmelCase__	:						List[Any]  				=	element.to_tuple()
									if isinstance(element[0]   ,torch.Tensor		):
										lowerCAmelCase__	:						List[Any]  				=	tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
									elif isinstance(element[0]   ,np.ndarray		):
										lowerCAmelCase__	:						str  				=	tuple(np.expand_dims(el[self._loader_batch_index]   ,0		) for el in element		)
									continue
								if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(lowercase_   ,lowercase_		):
									# Those are stored as lists of tensors so need specific unbatching.
									if isinstance(element[0]   ,torch.Tensor		):
										lowerCAmelCase__	:						Dict  				=	tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
									elif isinstance(element[0]   ,np.ndarray		):
										lowerCAmelCase__	:						Optional[int]  				=	tuple(np.expand_dims(el[self._loader_batch_index]   ,0		) for el in element		)
									continue
								if element is None:
									# This can happen for optional data that get passed around
									lowerCAmelCase__	:						Dict  				=	None
								elif isinstance(element[self._loader_batch_index]   ,torch.Tensor		):
									# Take correct batch data, but make it looked like batch_size=1
									# For compatibility with other methods within transformers

									lowerCAmelCase__	:						str  				=	element[self._loader_batch_index].unsqueeze(0		)
								elif isinstance(element[self._loader_batch_index]   ,np.ndarray		):
									# Take correct batch data, but make it looked like batch_size=1
									# For compatibility with other methods within transformers
									lowerCAmelCase__	:						Tuple  				=	np.expand_dims(element[self._loader_batch_index]   ,0		)
								else:
									# This is typically a list, so no need to `unsqueeze`.
									lowerCAmelCase__	:						int  				=	element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
							lowerCAmelCase__	:						int  				=	self._loader_batch_data.__class__(lowercase_		)
						self._loader_batch_index += 1
						return result







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							# We are currently unrolling a batch so we just need to return
							# the current item within a batch
							return self.loader_batch_item()

						# We're out of items within a batch
						lowerCAmelCase__	:						Dict  				=	next(self.iterator		)
						lowerCAmelCase__	:						List[Any]  				=	self.infer(lowercase_   ,**self.params		)
						# We now have a batch of "inferred things".
						if self.loader_batch_size is not None:
							# Try to infer the size of the batch
							if isinstance(lowercase_   ,torch.Tensor		):
								lowerCAmelCase__	:						int  				=	processed
							else:
								lowerCAmelCase__	:						Union[str, Any]  				=	list(processed.keys()		)[0]
								lowerCAmelCase__	:						Union[str, Any]  				=	processed[key]
							if isinstance(lowercase_   ,lowercase_		):
								lowerCAmelCase__	:						List[Any]  				=	len(lowercase_		)
							else:
								lowerCAmelCase__	:						List[str]  				=	first_tensor.shape[0]
							if 0 < observed_batch_size < self.loader_batch_size:
								# could be last batch so we can't unroll as many
								# elements.
								lowerCAmelCase__	:						Optional[Any]  				=	observed_batch_size
							# Setting internal index to unwrap the batch
							lowerCAmelCase__	:						str  				=	processed
							lowerCAmelCase__	:						Any  				=	0
							return self.loader_batch_item()
						else:
							# We're not unrolling batches
							return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				str   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				int=None		):
						super().__init__(lowercase_   ,lowercase_   ,lowercase_		)







					def __iter__(       self			:				List[Any]		):
						lowerCAmelCase__	:						Dict  				=	iter(self.loader		)
						lowerCAmelCase__	:						Tuple  				=	None
						return self







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						if self.subiterator is None:
							lowerCAmelCase__	:						List[Any]  				=	self.infer(next(self.iterator		)   ,**self.params		)
						try:
							# Try to return next item
							lowerCAmelCase__	:						Optional[int]  				=	next(self.subiterator		)
						except StopIteration:
							# When a preprocess iterator ends, we can start lookig at the next item
							# ChunkIterator will keep feeding until ALL elements of iterator
							# all have created their subiterator and have been iterating against.
							#
							# Another way to look at it, is we're basically flattening lists of lists
							# into a single list, but with generators
							lowerCAmelCase__	:						Any  				=	self.infer(next(self.iterator		)   ,**self.params		)
							lowerCAmelCase__	:						int  				=	next(self.subiterator		)
						return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __iter__(       self			:				Tuple		):
						lowerCAmelCase__	:						int  				=	iter(self.loader		)
						return self







					def        __lowerCAmelCase (       self			:				List[Any]		):
						# Extremely similar to PipelineIterator in its unpacking mechanism
						# BUT, we have an extra required item which is the presence of `is_last`
						# That is because everything is flattened by `PipelineChunkIterator` we
						# need to keep track of how to regroup here in the original `process`
						# boundaries so that `process` and `postprocess` see the same data.

						# This iterator accumulates items (possibly while unbatching) until it
						# its a `is_last` and then just passes it on to the caller.
						lowerCAmelCase__	:						Tuple  				=	False
						lowerCAmelCase__	:						str  				=	[]
						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							while self._loader_batch_index < self.loader_batch_size:
								lowerCAmelCase__	:						Dict  				=	self.loader_batch_item()
								lowerCAmelCase__	:						Optional[Any]  				=	item.pop('''is_last'''		)
								accumulator.append(lowercase_		)
								if is_last:
									return accumulator

						while not is_last:
							lowerCAmelCase__	:						Any  				=	self.infer(next(self.iterator		)   ,**self.params		)
							if self.loader_batch_size is not None:
								if isinstance(lowercase_   ,torch.Tensor		):
									lowerCAmelCase__	:						Tuple  				=	processed
								else:
									lowerCAmelCase__	:						List[Any]  				=	list(processed.keys()		)[0]
									lowerCAmelCase__	:						Union[str, Any]  				=	processed[key]
								if isinstance(lowercase_   ,lowercase_		):
									lowerCAmelCase__	:						Tuple  				=	len(lowercase_		)
								else:
									lowerCAmelCase__	:						str  				=	first_tensor.shape[0]
								if 0 < observed_batch_size < self.loader_batch_size:
									# could be last batch so we can't unroll as many
									# elements.
									lowerCAmelCase__	:						Optional[int]  				=	observed_batch_size
								lowerCAmelCase__	:						Optional[int]  				=	processed
								lowerCAmelCase__	:						Optional[int]  				=	0
								while self._loader_batch_index < self.loader_batch_size:
									lowerCAmelCase__	:						Any  				=	self.loader_batch_item()
									lowerCAmelCase__	:						Optional[Any]  				=	item.pop('''is_last'''		)
									accumulator.append(lowercase_		)
									if is_last:
										return accumulator
							else:
								lowerCAmelCase__	:						Dict  				=	processed
								lowerCAmelCase__	:						Tuple  				=	item.pop('''is_last'''		)
								accumulator.append(lowercase_		)
						return accumulator







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				Dataset   ,lowercase_			:				str		):
						lowerCAmelCase__	:						List[Any]  				=	dataset
						lowerCAmelCase__	:						List[Any]  				=	key







					def __len__(       self			:				List[Any]		):
						return len(self.dataset		)







					def __getitem__(       self			:				str   ,lowercase_			:				Union[str, Any]		):
						return self.dataset[i][self.key]







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Dict   ,lowercase_			:				Dataset   ,lowercase_			:				str   ,lowercase_			:				str		):
						lowerCAmelCase__	:						str  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	keya
						lowerCAmelCase__	:						Optional[Any]  				=	keya







					def __len__(       self			:				str		):
						return len(self.dataset		)







					def __getitem__(       self			:				Optional[int]   ,lowercase_			:				Union[str, Any]		):
						return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}




