


import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map



def UpperCamelCase_				(				__a      )	->   List[Tuple[int, ...]]:
      a__	:  Any     =   []
      if isinstance(__a			,				__a      ):
            for v in tree.values():
                  shapes.extend(_fetch_dims(__a      )      )
      elif isinstance(__a			,				(list, tuple)      ):
            for t in tree:
                  shapes.extend(_fetch_dims(__a      )      )
      elif isinstance(__a			,				torch.Tensor      ):
            shapes.append(tree.shape      )
      else:
            raise ValueError("Not supported"      )

      return shapes



@torch.jit.ignore
def UpperCamelCase_				(				__a			,				__a      )	->   Tuple[int, ...]:
      a__	:  Tuple     =   []
      for d in reversed(__a      ):
            idx.append(flat_idx % d      )
            a__	:  Tuple     =   flat_idx // d

      return tuple(reversed(__a      )      )



@torch.jit.ignore
def UpperCamelCase_				(				__a			,				__a			,				__a			,				__a = None			,				__a = None			,				)	->   List[Tuple[slice, ...]]:

      # start_edges and end_edges both indicate whether, starting from any given
      # dimension, the start/end index is at the top/bottom edge of the
      # corresponding tensor, modeled as a tree
      def reduce_edge_list(__a      ) -> None:
            a__	:  List[str]     =   True
            for i in range(len(__a      )      ):
                  a__	:  Tuple     =   -1 * (i + 1)
                  l[reversed_idx] &= tally
                  a__	:  Optional[int]     =   l[reversed_idx]

      if start_edges is None:
            a__	:  Union[str, Any]     =   [s == 0 for s in start]
            reduce_edge_list(__a      )
      if end_edges is None:
            a__	:  List[Any]     =   [e == (d - 1) for e, d in zip(__a			,				__a      )]
            reduce_edge_list(__a      )

      # Base cases. Either start/end are empty and we're done, or the final,
      # one-dimensional tensor can be simply sliced
      if len(__a      ) == 0:
            return [()]
      elif len(__a      ) == 1:
            return [(slice(start[0]			,				end[0] + 1      ),)]

      a__	:  List[Tuple[slice, ...]]     =   []
      a__	:  List[slice]     =   []

      # Dimensions common to start and end can be selected directly
      for s, e in zip(__a			,				__a      ):
            if s == e:
                  path_list.append(slice(__a			,				s + 1      )      )
            else:
                  break

      a__	:  Tuple[slice, ...]     =   tuple(__a      )
      a__	:  Optional[int]     =   len(__a      )

      # start == end, and we're done
      if divergence_idx == len(__a      ):
            return [path]

      def upper() -> Tuple[Tuple[slice, ...], ...]:
            assert start_edges is not None
            assert end_edges is not None

            a__	:  str     =   start[divergence_idx]
            return tuple(
                path + (slice(__a			,				sdi + 1      ),) + s
                for s in _get_minimal_slice_set(
                    start[divergence_idx + 1 :]			,				[d - 1 for d in dims[divergence_idx + 1 :]]			,				dims[divergence_idx + 1 :]			,				start_edges=start_edges[divergence_idx + 1 :]			,				end_edges=[True for _ in end_edges[divergence_idx + 1 :]]			,				)      )

      def lower() -> Tuple[Tuple[slice, ...], ...]:
            assert start_edges is not None
            assert end_edges is not None

            a__	:  Dict     =   end[divergence_idx]
            return tuple(
                path + (slice(__a			,				edi + 1      ),) + s
                for s in _get_minimal_slice_set(
                    [0 for _ in start[divergence_idx + 1 :]]			,				end[divergence_idx + 1 :]			,				dims[divergence_idx + 1 :]			,				start_edges=[True for _ in start_edges[divergence_idx + 1 :]]			,				end_edges=end_edges[divergence_idx + 1 :]			,				)      )

      # If both start and end are at the edges of the subtree rooted at
      # divergence_idx, we can just select the whole subtree at once
      if start_edges[divergence_idx] and end_edges[divergence_idx]:
            slices.append(path + (slice(start[divergence_idx]			,				end[divergence_idx] + 1      ),)      )
      # If just start is at the edge, we can grab almost all of the subtree,
      # treating only the ragged bottom edge as an edge case
      elif start_edges[divergence_idx]:
            slices.append(path + (slice(start[divergence_idx]			,				end[divergence_idx]      ),)      )
            slices.extend(lower()      )
      # Analogous to the previous case, but the top is ragged this time
      elif end_edges[divergence_idx]:
            slices.extend(upper()      )
            slices.append(path + (slice(start[divergence_idx] + 1			,				end[divergence_idx] + 1      ),)      )
      # If both sides of the range are ragged, we need to handle both sides
      # separately. If there's contiguous meat in between them, we can index it
      # in one big chunk
      else:
            slices.extend(upper()      )
            a__	:  Any     =   end[divergence_idx] - start[divergence_idx]
            if middle_ground > 1:
                  slices.append(path + (slice(start[divergence_idx] + 1			,				end[divergence_idx]      ),)      )
            slices.extend(lower()      )

      return slices



@torch.jit.ignore
def UpperCamelCase_				(				__a			,				__a			,				__a			,				__a      )	->   torch.Tensor:
      a__	:  str     =   t.shape[:no_batch_dims]
      a__	:  List[str]     =   list(_flat_idx_to_idx(__a			,				__a      )      )
      # _get_minimal_slice_set is inclusive
      a__	:  Union[str, Any]     =   list(_flat_idx_to_idx(flat_end - 1			,				__a      )      )

      # Get an ordered list of slices to perform
      a__	:  Tuple     =   _get_minimal_slice_set(
          __a			,				__a			,				__a			,				)

      a__	:  Dict     =   [t[s] for s in slices]

      return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]      ) for s in sliced_tensors]      )



def UpperCamelCase_				(				__a			,				__a			,				__a			,				__a			,				__a = False			,				__a = None			,				__a = False			,				)	->   Any:
      if not (len(__a      ) > 0):
            raise ValueError("Must provide at least one input"      )

      a__	:  Optional[Any]     =   [shape[:no_batch_dims] for shape in _fetch_dims(__a      )]
      a__	:  Optional[int]     =   tuple([max(__a      ) for s in zip(*__a      )]      )

      def _prep_inputs(__a      ) -> torch.Tensor:
            if not low_mem:
                  if not sum(t.shape[:no_batch_dims]      ) == no_batch_dims:
                        a__	:  Optional[int]     =   t.expand(orig_batch_dims + t.shape[no_batch_dims:]      )
                  a__	:  List[str]     =   t.reshape(-1			,				*t.shape[no_batch_dims:]      )
            else:
                  a__	:  str     =   t.expand(orig_batch_dims + t.shape[no_batch_dims:]      )
            return t

      a__	:  Dict[str, Any]     =   tensor_tree_map(_prep_inputs			,				__a      )
      a__	:  Tuple     =   None
      if _out is not None:
            a__	:  Tuple     =   tensor_tree_map(lambda __a      : t.view([-1] + list(t.shape[no_batch_dims:]      )      )			,				_out      )

      a__	:  Optional[int]     =   1
      for d in orig_batch_dims:
            flat_batch_dim *= d

      a__	:  Optional[int]     =   flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

      def _select_chunk(__a      ) -> torch.Tensor:
            return t[i : i + chunk_size] if t.shape[0] != 1 else t

      a__	:  List[str]     =   0
      a__	:  Tuple     =   prepped_outputs
      for _ in range(__a      ):
            # Chunk the input
            if not low_mem:
                  a__	:  int     =   _select_chunk
            else:
                  a__	:  List[str]     =   partial(
                      _chunk_slice			,				flat_start=__a			,				flat_end=min(__a			,				i + chunk_size      )			,				no_batch_dims=len(__a      )			,				)

            a__	:  Dict[str, Any]     =   tensor_tree_map(__a			,				__a      )

            # Run the layer on the chunk
            a__	:  List[Any]     =   layer(**__a      )

            # Allocate space for the output
            if out is None:
                  a__	:  List[str]     =   tensor_tree_map(lambda __a      : t.new_zeros((flat_batch_dim,) + t.shape[1:]      )			,				__a      )

            # Put the chunk in its pre-allocated space
            if isinstance(__a			,				__a      ):

                  def assign(__a			,				__a      ) -> None:
                        for k, v in da.items():
                              if isinstance(__a			,				__a      ):
                                    assign(__a			,				da[k]      )
                              else:
                                    if _add_into_out:
                                          v[i : i + chunk_size] += da[k]
                                    else:
                                          a__	:  Optional[Any]     =   da[k]

                  assign(__a			,				__a      )
            elif isinstance(__a			,				__a      ):
                  for xa, xa in zip(__a			,				__a      ):
                        if _add_into_out:
                              xa[i : i + chunk_size] += xa
                        else:
                              a__	:  str     =   xa
            elif isinstance(__a			,				torch.Tensor      ):
                  if _add_into_out:
                        out[i : i + chunk_size] += output_chunk
                  else:
                        a__	:  Union[str, Any]     =   output_chunk
            else:
                  raise ValueError("Not supported"      )

            i += chunk_size

      a__	:  Any     =   tensor_tree_map(lambda __a      : t.view(orig_batch_dims + t.shape[1:]      )			,				__a      )

      return out



class A__     :



  """simple docstring"""



  def __init__( self			:  Optional[Any]				,				lowerCamelCase__			:  int = 512				,				):
        a__	:  Tuple     =   max_chunk_size
        a__	:  Optional[int]     =   None
        a__	:  Optional[tuple]     =   None

  def    _UpperCamelCase( self			:  str				,				lowerCamelCase__			:  Callable				,				lowerCamelCase__			:  tuple				,				lowerCamelCase__			:  int       ):
        logging.info("Tuning chunk size..."       )

        if min_chunk_size >= self.max_chunk_size:
              return min_chunk_size

        a__	:  List[int]     =   [2**l for l in range(int(math.log(self.max_chunk_size				,				2       )       ) + 1       )]
        a__	:  int     =   [c for c in candidates if c > min_chunk_size]
        a__	:  Dict     =   [min_chunk_size] + candidates
        candidates[-1] += 4

        def test_chunk_size(lowerCamelCase__			:  int       ) -> bool:
              try:
                    with torch.no_grad():
                          fn(*lowerCamelCase__				,				chunk_size=lowerCamelCase__       )
                    return True
              except RuntimeError:
                    return False

        a__	:  List[str]     =   0
        a__	:  Any     =   len(lowerCamelCase__       ) - 1
        while i > min_viable_chunk_size_index:
              a__	:  str     =   test_chunk_size(candidates[i]       )
              if not viable:
                    a__	:  List[str]     =   (min_viable_chunk_size_index + i) // 2
              else:
                    a__	:  Tuple     =   i
                    a__	:  str     =   (i + len(lowerCamelCase__       ) - 1) // 2

        return candidates[min_viable_chunk_size_index]

  def    _UpperCamelCase( self			:  List[Any]				,				lowerCamelCase__			:  Iterable				,				lowerCamelCase__			:  Iterable       ):
        a__	:  Union[str, Any]     =   True
        for aa, aa in zip(lowerCamelCase__				,				lowerCamelCase__       ):
              assert type(lowerCamelCase__       ) == type(lowerCamelCase__       )
              if isinstance(lowerCamelCase__				,				(list, tuple)       ):
                    consistent &= self._compare_arg_caches(lowerCamelCase__				,				lowerCamelCase__       )
              elif isinstance(lowerCamelCase__				,				lowerCamelCase__       ):
                    a__	:  int     =   [v for _, v in sorted(aa.items()				,				key=lambda lowerCamelCase__       : x[0]       )]
                    a__	:  List[str]     =   [v for _, v in sorted(aa.items()				,				key=lambda lowerCamelCase__       : x[0]       )]
                    consistent &= self._compare_arg_caches(lowerCamelCase__				,				lowerCamelCase__       )
              else:
                    consistent &= aa == aa

        return consistent

  def    _UpperCamelCase( self			:  Optional[Any]				,				lowerCamelCase__			:  Callable				,				lowerCamelCase__			:  tuple				,				lowerCamelCase__			:  int				,				):
        a__	:  List[str]     =   True
        a__	:  tuple     =   tree_map(lambda lowerCamelCase__       : a.shape if isinstance(lowerCamelCase__				,				torch.Tensor       ) else a				,				lowerCamelCase__				,				lowerCamelCase__       )
        if self.cached_arg_data is not None:
              # If args have changed shape/value, we need to re-tune
              assert len(self.cached_arg_data       ) == len(lowerCamelCase__       )
              a__	:  Optional[Any]     =   self._compare_arg_caches(self.cached_arg_data				,				lowerCamelCase__       )
        else:
              # Otherwise, we can reuse the precomputed value
              a__	:  Union[str, Any]     =   False

        if not consistent:
              a__	:  str     =   self._determine_favorable_chunk_size(
                  lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				)
              a__	:  List[str]     =   arg_data

        assert self.cached_chunk_size is not None

        return self.cached_chunk_size



"""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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=None						):
						# set parameter of one layer
						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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# layernorm 1
						__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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# reformer model
						__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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# Initialise PyTorch model
						__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__":
			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)
