


import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')




def  a   (lowerCAmelCase__   ):
   __a					=  test_file.split(os.path.sep   )
   if components[0:2] != ["tests", "models"]:
      raise ValueError(
          """`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got """
          f'''{test_file} instead.'''   )
   __a					=  components[-1]
   if not test_fn.endswith("""py"""   ):
      raise ValueError(f'''`test_file` should be a python file. Got {test_fn} instead.'''   )
   if not test_fn.startswith("""test_modeling_"""   ):
      raise ValueError(
          f'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'''   )

   __a					=  components[:-1] + [test_fn.replace(""".py"""		,				""""""   )]
   __a					=  """.""".join(lowerCAmelCase__   )

   return test_module_path




def  a   (lowerCAmelCase__   ):
   __a					=  get_module_path(lowerCAmelCase__   )
   __a					=  importlib.import_module(lowerCAmelCase__   )

   return test_module




def  a   (lowerCAmelCase__   ):
   __a					=  []
   __a					=  get_test_module(lowerCAmelCase__   )
   for attr in dir(lowerCAmelCase__   ):
      if attr.endswith("""ModelTester"""   ):
         tester_classes.append(getattr(lowerCAmelCase__		,				lowerCAmelCase__   )   )

    # sort with class names
   return sorted(lowerCAmelCase__		,				key=lambda lowerCAmelCase__   : x.__name__   )




def  a   (lowerCAmelCase__   ):
   __a					=  []
   __a					=  get_test_module(lowerCAmelCase__   )
   for attr in dir(lowerCAmelCase__   ):
      __a					=  getattr(lowerCAmelCase__		,				lowerCAmelCase__   )
      # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
      # `all_model_classes` is not empty (which also excludes other special classes).
      __a					=  getattr(lowerCAmelCase__		,				"""all_model_classes"""		,				[]   )
      if len(lowerCAmelCase__   ) > 0:
         test_classes.append(lowerCAmelCase__   )

    # sort with class names
   return sorted(lowerCAmelCase__		,				key=lambda lowerCAmelCase__   : x.__name__   )




def  a   (lowerCAmelCase__   ):
   __a					=  get_test_classes(lowerCAmelCase__   )
   __a					=  set()
   for test_class in test_classes:
      model_classes.update(test_class.all_model_classes   )

   # sort with class names
   return sorted(lowerCAmelCase__		,				key=lambda lowerCAmelCase__   : x.__name__   )




def  a   (lowerCAmelCase__   ):
   __a					=  test_class()
   if hasattr(lowerCAmelCase__		,				"""setUp"""   ):
      test.setUp()

   __a					=  None
   if hasattr(lowerCAmelCase__		,				"""model_tester"""   ):
      # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
      if test.model_tester is not None:
         __a					=  test.model_tester.__class__

   return model_tester




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  get_test_classes(lowerCAmelCase__   )

   __a					=  []
   for test_class in test_classes:
      if model_class in test_class.all_model_classes:
         target_test_classes.append(lowerCAmelCase__   )

    # sort with class names
   return sorted(lowerCAmelCase__		,				key=lambda lowerCAmelCase__   : x.__name__   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  get_test_classes_for_model(lowerCAmelCase__		,				lowerCAmelCase__   )

   __a					=  []
   for test_class in test_classes:
      __a					=  get_model_tester_from_test_class(lowerCAmelCase__   )
      if tester_class is not None:
         tester_classes.append(lowerCAmelCase__   )

    # sort with class names
   return sorted(lowerCAmelCase__		,				key=lambda lowerCAmelCase__   : x.__name__   )




def  a   (lowerCAmelCase__   ):
   __a					=  get_test_classes(lowerCAmelCase__   )
   __a					=  {test_class: get_model_tester_from_test_class(lowerCAmelCase__   ) for test_class in test_classes}
   return test_tester_mapping




def  a   (lowerCAmelCase__   ):
   __a					=  get_model_classes(lowerCAmelCase__   )
   __a					=  {
       model_class: get_test_classes_for_model(lowerCAmelCase__		,				lowerCAmelCase__   ) for model_class in model_classes
   }
   return model_test_mapping




def  a   (lowerCAmelCase__   ):
   __a					=  get_model_classes(lowerCAmelCase__   )
   __a					=  {
       model_class: get_tester_classes_for_model(lowerCAmelCase__		,				lowerCAmelCase__   ) for model_class in model_classes
   }
   return model_to_tester_mapping




def  a   (lowerCAmelCase__   ):
   if isinstance(lowerCAmelCase__		,				lowerCAmelCase__   ):
      return o
   elif isinstance(lowerCAmelCase__		,				lowerCAmelCase__   ):
      return o.__name__
   elif isinstance(lowerCAmelCase__		,				(list, tuple)   ):
      return [to_json(lowerCAmelCase__   ) for x in o]
   elif isinstance(lowerCAmelCase__		,				lowerCAmelCase__   ):
      return {to_json(lowerCAmelCase__   ): to_json(lowerCAmelCase__   ) for k, v in o.items()}
   else:
      return o







"""simple docstring"""




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       a_ (	lowercase__ :Union[dict, list, tuple, torch.Tensor]     ):
			__lowerCamelCase          =      []
			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       a_ (	lowercase__ :int,			lowercase__ :Tuple[int, ...]     ):
			__lowerCamelCase          =      []
			for d in reversed(lowercase__     ):
						idx.append(flat_idx % d     )
						__lowerCamelCase          =      flat_idx // d

			return tuple(reversed(lowercase__     )     )





@torch.jit.ignore
def       a_ (	lowercase__ :Sequence[int],			lowercase__ :Sequence[int],			lowercase__ :Sequence[int],			lowercase__ :Optional[Sequence[bool]] = None,			lowercase__ :Optional[Sequence[bool]] = None,			):

			# 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(lowercase__ :List[bool]     ) -> None:
						__lowerCamelCase          =      True
						for i in range(len(lowercase__     )     ):
									__lowerCamelCase          =      -1 * (i + 1)
									l[reversed_idx] &= tally
									__lowerCamelCase          =      l[reversed_idx]

			if start_edges is None:
						__lowerCamelCase          =      [s == 0 for s in start]
						reduce_edge_list(lowercase__     )
			if end_edges is None:
						__lowerCamelCase          =      [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     ),)]

			__lowerCamelCase          =      []
			__lowerCamelCase          =      []

			# 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

			__lowerCamelCase          =      tuple(lowercase__     )
			__lowerCamelCase          =      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

						__lowerCamelCase          =      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

						__lowerCamelCase          =      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()     )
						__lowerCamelCase          =      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       a_ (	lowercase__ :torch.Tensor,			lowercase__ :int,			lowercase__ :int,			lowercase__ :int     ):
			__lowerCamelCase          =      t.shape[:no_batch_dims]
			__lowerCamelCase          =      list(_flat_idx_to_idx(lowercase__,			lowercase__     )     )
			# _get_minimal_slice_set is inclusive
			__lowerCamelCase          =      list(_flat_idx_to_idx(flat_end - 1,			lowercase__     )     )

			# Get an ordered list of slices to perform
			__lowerCamelCase          =      _get_minimal_slice_set(
			    lowercase__,			lowercase__,			lowercase__,			)

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

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





def       a_ (	lowercase__ :Callable,			lowercase__ :Dict[str, Any],			lowercase__ :int,			lowercase__ :int,			lowercase__ :bool = False,			lowercase__ :Any = None,			lowercase__ :bool = False,			):
			if not (len(lowercase__     ) > 0):
						raise ValueError("""Must provide at least one input"""     )

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

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

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

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

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

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

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

						__lowerCamelCase          =      tensor_tree_map(lowercase__,			lowercase__     )

						# Run the layer on the chunk
						__lowerCamelCase          =      layer(**lowercase__     )

						# Allocate space for the output
						if out is None:
									__lowerCamelCase          =      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__ :dict,			lowercase__ :dict     ) -> 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:
																					__lowerCamelCase          =      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:
															__lowerCamelCase          =      xa
						elif isinstance(lowercase__,			torch.Tensor     ):
									if _add_into_out:
												out[i : i + chunk_size] += output_chunk
									else:
												__lowerCamelCase          =      output_chunk
						else:
									raise ValueError("""Not supported"""     )

						i += chunk_size

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

			return out




class 			__snake_case   :




							def __init__(     self:    Union[str, Any]     ,							A_:    int = 5_12     ,							):
										__lowerCamelCase          =      max_chunk_size
										__lowerCamelCase          =      None
										__lowerCamelCase          =      None




							def   __a   (     self:    Tuple     ,							A_:    Callable     ,							A_:    tuple     ,							A_:    int   ):
										logging.info("""Tuning chunk size..."""   )

										if min_chunk_size >= self.max_chunk_size:
													return min_chunk_size

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

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

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

										return candidates[min_viable_chunk_size_index]




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

										return consistent




							def   __a   (     self:    str     ,							A_:    Callable     ,							A_:    tuple     ,							A_:    int     ,							):
										__lowerCamelCase          =      True
										__lowerCamelCase          =      tree_map(lambda A_   : a.shape if isinstance(A_     ,							torch.Tensor   ) else a     ,							A_     ,							A_   )
										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(A_   )
													__lowerCamelCase          =      self._compare_arg_caches(self.cached_arg_data     ,							A_   )
										else:
													# Otherwise, we can reuse the precomputed value
													__lowerCamelCase          =      False

										if not consistent:
													__lowerCamelCase          =      self._determine_favorable_chunk_size(
													    A_     ,							A_     ,							A_     ,							)
													__lowerCamelCase          =      arg_data

										assert self.cached_chunk_size is not None

										return self.cached_chunk_size




