




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 SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	List[Tuple[int, ...]]:



       """simple docstring"""

       A__							=   []
       if isinstance(lowercase_			,    lowercase_						):
              for v in tree.values():
                     shapes.extend(_fetch_dims(lowercase_						)						)
       elif isinstance(lowercase_			,    (list, tuple)						):
              for t in tree:
                     shapes.extend(_fetch_dims(lowercase_						)						)
       elif isinstance(lowercase_			,    torch.Tensor						):
              shapes.append(tree.shape						)
       else:
              raise ValueError('''Not supported'''						)

       return shapes







@torch.jit.ignore
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Tuple[int, ...]:



       """simple docstring"""

       A__							=   []
       for d in reversed(lowercase_						):
              idx.append(flat_idx % d						)
              A__							=   flat_idx // d

       return tuple(reversed(lowercase_						)						)







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



       """simple docstring"""

       def reduce_edge_list(lowercase_						) -> None:
              A__							=   True
              for i in range(len(lowercase_						)						):
                     A__							=   -1 * (i + 1)
                     l[reversed_idx] &= tally
                     A__							=   l[reversed_idx]

       if start_edges is None:
              A__							=   [s == 0 for s in start]
              reduce_edge_list(lowercase_						)
       if end_edges is None:
              A__							=   [e == (d - 1) for e, d in zip(lowercase_			,    lowercase_						)]
              reduce_edge_list(lowercase_						)

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

       A__							=   []
       A__							=   []

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

       A__							=   tuple(lowercase_						)
       A__							=   len(lowercase_						)

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

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

              A__							=   start[divergence_idx]
              return tuple(
                  path + (slice(lowercase_			,    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__							=   end[divergence_idx]
              return tuple(
                  path + (slice(lowercase_			,    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__							=   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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_						)       ->	torch.Tensor:



       """simple docstring"""

       A__							=   t.shape[:no_batch_dims]
       A__							=   list(_flat_idx_to_idx(lowercase_			,    lowercase_						)						)
       # _get_minimal_slice_set is inclusive
       A__							=   list(_flat_idx_to_idx(flat_end - 1			,    lowercase_						)						)

       # Get an ordered list of slices to perform
       A__							=   _get_minimal_slice_set(
           lowercase_			,    lowercase_			,    lowercase_			,    )

       A__							=   [t[s] for s in slices]

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_			,    lowercase_ = False			,    lowercase_ = None			,    lowercase_ = False			,    )       ->	Any:



       """simple docstring"""

       if not (len(lowercase_						) > 0):
              raise ValueError('''Must provide at least one input'''						)

       A__							=   [shape[:no_batch_dims] for shape in _fetch_dims(lowercase_						)]
       A__							=   tuple([max(lowercase_						) for s in zip(*lowercase_						)]						)

       def _prep_inputs(lowercase_						) -> torch.Tensor:
              if not low_mem:
                     if not sum(t.shape[:no_batch_dims]						) == no_batch_dims:
                            A__							=   t.expand(orig_batch_dims + t.shape[no_batch_dims:]						)
                     A__							=   t.reshape(-1			,    *t.shape[no_batch_dims:]						)
              else:
                     A__							=   t.expand(orig_batch_dims + t.shape[no_batch_dims:]						)
              return t

       A__							=   tensor_tree_map(_prep_inputs			,    lowercase_						)
       A__							=   None
       if _out is not None:
              A__							=   tensor_tree_map(lambda lowercase_						: t.view([-1] + list(t.shape[no_batch_dims:]						)						)			,    _out						)

       A__							=   1
       for d in orig_batch_dims:
              flat_batch_dim *= d

       A__							=   flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

       A__							=   0
       A__							=   prepped_outputs
       for _ in range(lowercase_						):
              # Chunk the input
              if not low_mem:
                     A__							=   _select_chunk
              else:
                     A__							=   partial(
                         _chunk_slice			,    flat_start=lowercase_			,    flat_end=min(lowercase_			,    i + chunk_size						)			,    no_batch_dims=len(lowercase_						)			,    )

              A__							=   tensor_tree_map(lowercase_			,    lowercase_						)

              # Run the layer on the chunk
              A__							=   layer(**lowercase_						)

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

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

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

                     assign(lowercase_			,    lowercase_						)
              elif isinstance(lowercase_			,    lowercase_						):
                     for xa, xa in zip(lowercase_			,    lowercase_						):
                            if _add_into_out:
                                   xa[i : i + chunk_size] += xa
                            else:
                                   A__							=   xa
              elif isinstance(lowercase_			,    torch.Tensor						):
                     if _add_into_out:
                            out[i : i + chunk_size] += output_chunk
                     else:
                            A__							=   output_chunk
              else:
                     raise ValueError('''Not supported'''						)

              i += chunk_size

       A__							=   tensor_tree_map(lambda lowercase_						: t.view(orig_batch_dims + t.shape[1:]						)			,    lowercase_						)

       return out


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     str ,			UpperCAmelCase__				:     int = 512 ,			)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   max_chunk_size
           A__							=   None
           A__							=   None


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     Callable ,			UpperCAmelCase__				:     tuple ,			UpperCAmelCase__				:     int)						->int:





           '''simple docstring'''

           logging.info('''Tuning chunk size...''')

           if min_chunk_size >= self.max_chunk_size:
                  return min_chunk_size

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

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

           A__							=   0
           A__							=   len(UpperCAmelCase__) - 1
           while i > min_viable_chunk_size_index:
                  A__							=   test_chunk_size(candidates[i])
                  if not viable:
                         A__							=   (min_viable_chunk_size_index + i) // 2
                  else:
                         A__							=   i
                         A__							=   (i + len(UpperCAmelCase__) - 1) // 2

           return candidates[min_viable_chunk_size_index]


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     Iterable ,			UpperCAmelCase__				:     Iterable)						->bool:





           '''simple docstring'''

           A__							=   True
           for aa, aa in zip(UpperCAmelCase__ ,			UpperCAmelCase__):
                  assert type(UpperCAmelCase__) == type(UpperCAmelCase__)
                  if isinstance(UpperCAmelCase__ ,			(list, tuple)):
                         consistent &= self._compare_arg_caches(UpperCAmelCase__ ,			UpperCAmelCase__)
                  elif isinstance(UpperCAmelCase__ ,			UpperCAmelCase__):
                         A__							=   [v for _, v in sorted(aa.items() ,			key=lambda UpperCAmelCase__: x[0])]
                         A__							=   [v for _, v in sorted(aa.items() ,			key=lambda UpperCAmelCase__: x[0])]
                         consistent &= self._compare_arg_caches(UpperCAmelCase__ ,			UpperCAmelCase__)
                  else:
                         consistent &= aa == aa

           return consistent





    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     Callable ,			UpperCAmelCase__				:     tuple ,			UpperCAmelCase__				:     int ,			)						->int:





           '''simple docstring'''

           A__							=   True
           A__							=   tree_map(lambda UpperCAmelCase__: a.shape if isinstance(UpperCAmelCase__ ,			torch.Tensor) else a ,			UpperCAmelCase__ ,			UpperCAmelCase__)
           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(UpperCAmelCase__)
                  A__							=   self._compare_arg_caches(self.cached_arg_data ,			UpperCAmelCase__)
           else:
                  # Otherwise, we can reuse the precomputed value
                  A__							=   False

           if not consistent:
                  A__							=   self._determine_favorable_chunk_size(
                      UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			)
                  A__							=   arg_data

           assert self.cached_chunk_size is not None

           return self.cached_chunk_size








import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

       A__							=   AutoConfig.from_pretrained(lowercase_						)
       A__							=   FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase_						)
       A__							=   checkpoints.load_tax_checkpoint(lowercase_						)

       A__							=   '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

       if config.model_type == "t5":
              A__							=   '''SelfAttention'''
       if config.model_type == "longt5" and config.encoder_attention_type == "local":
              A__							=   '''LocalSelfAttention'''
       elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              A__							=   '''TransientGlobalSelfAttention'''
       else:
              raise ValueError(
                  '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
                  ''' attribute with a value from [\'local\', \'transient-global].'''						)

       # Encoder
       for layer_index in range(config.num_layers						):
              A__							=   f"""layers_{str(lowercase_						)}"""

              # Self-Attention
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

              # Global input layer norm
              if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

              if split_mlp_wi:
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

              # Assigning
              A__							=   flax_model.params['''encoder''']['''block'''][str(lowercase_						)]['''layer''']
              A__							=   tax_attention_key
              A__							=   tax_attention_out
              A__							=   tax_attention_query
              A__							=   tax_attention_value

              A__							=   tax_attention_layer_norm

              # Global input layer norm
              if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                     A__							=   tax_global_layer_norm

              if split_mlp_wi:
                     A__							=   tax_mlp_wi_a
                     A__							=   tax_mlp_wi_a
              else:
                     A__							=   tax_mlp_wi

              A__							=   tax_mlp_wo
              A__							=   tax_mlp_layer_norm

              A__							=   flax_model_encoder_layer_block

       # Only for layer 0:
       A__							=   tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
       A__							=   tax_encoder_rel_embedding

       # Side/global relative position_bias + layer norm
       if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              A__							=   tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
              A__							=   tax_encoder_global_rel_embedding

       # Assigning
       A__							=   tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
       A__							=   tax_encoder_norm

       # Decoder
       for layer_index in range(config.num_layers						):
              A__							=   f"""layers_{str(lowercase_						)}"""

              # Self-Attention
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                  '''scale'''
              ]

              # Encoder-Decoder-Attention
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
              A__							=   tax_enc_dec_attention_module['''key''']['''kernel''']
              A__							=   tax_enc_dec_attention_module['''out''']['''kernel''']
              A__							=   tax_enc_dec_attention_module['''query''']['''kernel''']
              A__							=   tax_enc_dec_attention_module['''value''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

              # MLP
              if split_mlp_wi:
                     A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

              # Assigning
              A__							=   flax_model.params['''decoder''']['''block'''][str(lowercase_						)]['''layer''']
              A__							=   tax_attention_key
              A__							=   tax_attention_out
              A__							=   tax_attention_query
              A__							=   tax_attention_value

              A__							=   tax_pre_attention_layer_norm

              A__							=   tax_enc_dec_attention_key
              A__							=   tax_enc_dec_attention_out
              A__							=   tax_enc_dec_attention_query
              A__							=   tax_enc_dec_attention_value

              A__							=   tax_cross_layer_norm

              if split_mlp_wi:
                     A__							=   tax_mlp_wi_a
                     A__							=   tax_mlp_wi_a
              else:
                     A__							=   tax_mlp_wi

              A__							=   tax_mlp_wo

              A__							=   txa_mlp_layer_norm

              A__							=   flax_model_decoder_layer_block

       # Decoder Normalization
       A__							=   tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
       A__							=   txa_decoder_norm

       # Only for layer 0:
       A__							=   tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
       A__							=   tax_decoder_rel_embedding

       # Token Embeddings
       A__							=   tax_model['''target''']['''token_embedder''']['''embedding''']
       A__							=   txa_token_embeddings

       # LM Head (only in v1.1 and LongT5 checkpoints)
       if "logits_dense" in tax_model["target"]["decoder"]:
              A__							=   tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

       flax_model.save_pretrained(lowercase_						)
       print('''T5X Model was sucessfully converted!'''						)


if __name__ == "__main__":
  _lowerCamelCase     :		Any								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
  )
  parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
  parser.add_argument(
      """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
  )
  _lowerCamelCase     :		Tuple								=       parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)


