


"""simple docstring"""




import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers.models.esm.modeling_esmfold import EsmForProteinFolding




class 			__snake_case   :




							def __init__(     self:    Tuple     ,							A_:    str     ,							A_:    Optional[int]=13     ,							A_:    Any=7     ,							A_:    Any=False     ,							A_:    Union[str, Any]=True     ,							A_:    Union[str, Any]=False     ,							A_:    Union[str, Any]=False     ,							A_:    Optional[Any]=19     ,							A_:    Tuple=32     ,							A_:    Optional[Any]=5     ,							A_:    Tuple=4     ,							A_:    List[str]=37     ,							A_:    Dict="gelu"     ,							A_:    List[str]=0.1     ,							A_:    str=0.1     ,							A_:    Tuple=5_12     ,							A_:    Dict=16     ,							A_:    Tuple=2     ,							A_:    str=0.02     ,							A_:    Dict=3     ,							A_:    Optional[Any]=4     ,							A_:    List[str]=None     ,							):
										__lowerCamelCase          =      parent
										__lowerCamelCase          =      batch_size
										__lowerCamelCase          =      seq_length
										__lowerCamelCase          =      is_training
										__lowerCamelCase          =      use_input_mask
										__lowerCamelCase          =      use_token_type_ids
										__lowerCamelCase          =      use_labels
										__lowerCamelCase          =      vocab_size
										__lowerCamelCase          =      hidden_size
										__lowerCamelCase          =      num_hidden_layers
										__lowerCamelCase          =      num_attention_heads
										__lowerCamelCase          =      intermediate_size
										__lowerCamelCase          =      hidden_act
										__lowerCamelCase          =      hidden_dropout_prob
										__lowerCamelCase          =      attention_probs_dropout_prob
										__lowerCamelCase          =      max_position_embeddings
										__lowerCamelCase          =      type_vocab_size
										__lowerCamelCase          =      type_sequence_label_size
										__lowerCamelCase          =      initializer_range
										__lowerCamelCase          =      num_labels
										__lowerCamelCase          =      num_choices
										__lowerCamelCase          =      scope




							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size   )

										__lowerCamelCase          =      None
										if self.use_input_mask:
													__lowerCamelCase          =      random_attention_mask([self.batch_size, self.seq_length]   )

										__lowerCamelCase          =      None
										__lowerCamelCase          =      None
										__lowerCamelCase          =      None
										if self.use_labels:
													__lowerCamelCase          =      ids_tensor([self.batch_size]     ,							self.type_sequence_label_size   )
													__lowerCamelCase          =      ids_tensor([self.batch_size, self.seq_length]     ,							self.num_labels   )
													__lowerCamelCase          =      ids_tensor([self.batch_size]     ,							self.num_choices   )

										__lowerCamelCase          =      self.get_config()

										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      EsmConfig(
										    vocab_size=33     ,							hidden_size=self.hidden_size     ,							pad_token_id=1     ,							num_hidden_layers=self.num_hidden_layers     ,							num_attention_heads=self.num_attention_heads     ,							intermediate_size=self.intermediate_size     ,							hidden_act=self.hidden_act     ,							hidden_dropout_prob=self.hidden_dropout_prob     ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,							max_position_embeddings=self.max_position_embeddings     ,							type_vocab_size=self.type_vocab_size     ,							initializer_range=self.initializer_range     ,							is_folding_model=A_     ,							esmfold_config={"""trunk""": {"""num_blocks""": 2}, """fp16_esm""": False}     ,							)
										return config




							def   __a   (     self:    Optional[Any]     ,							A_:    str     ,							A_:    List[str]     ,							A_:    Optional[Any]     ,							A_:    int     ,							A_:    List[Any]     ,							A_:    int   ):
										__lowerCamelCase          =      EsmForProteinFolding(config=A_   ).float()
										model.to(A_   )
										model.eval()
										__lowerCamelCase          =      model(A_     ,							attention_mask=A_   )
										__lowerCamelCase          =      model(A_   )
										__lowerCamelCase          =      model(A_   )

										self.parent.assertEqual(result.positions.shape     ,							(8, self.batch_size, self.seq_length, 14, 3)   )
										self.parent.assertEqual(result.angles.shape     ,							(8, self.batch_size, self.seq_length, 7, 2)   )




							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      self.prepare_config_and_inputs()
										(
										    (
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,
										)          =      config_and_inputs
										__lowerCamelCase          =      {"""input_ids""": input_ids, """attention_mask""": input_mask}
										return config, inputs_dict




@require_torch
class 			__snake_case   (lowerCamelCase			,		lowerCamelCase			,		unittest.TestCase  ):
							__a					 =       False

							__a					 =       (EsmForProteinFolding,) if is_torch_available() else ()
							__a					 =       ()
							__a					 =       {} if is_torch_available() else {}
							__a					 =       False




							def   __a   (     self:    Any   ):
										__lowerCamelCase          =      EsmFoldModelTester(self   )
										__lowerCamelCase          =      ConfigTester(self     ,							config_class=A_     ,							hidden_size=37   )




							def   __a   (     self:    Optional[int]   ):
										self.config_tester.run_common_tests()




							def   __a   (     self:    Optional[int]   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_   )




							@unittest.skip("""Does not support attention outputs"""   )
							def   __a   (     self:    Optional[Any]   ):
										pass




							@unittest.skip
							def   __a   (     self:    Tuple   ):
										pass




							@unittest.skip("""Esm does not support embedding resizing"""   )
							def   __a   (     self:    int   ):
										pass




							@unittest.skip("""Esm does not support embedding resizing"""   )
							def   __a   (     self:    List[Any]   ):
										pass




							@unittest.skip("""ESMFold does not support passing input embeds!"""   )
							def   __a   (     self:    Dict   ):
										pass




							@unittest.skip("""ESMFold does not support head pruning."""   )
							def   __a   (     self:    Optional[Any]   ):
										pass




							@unittest.skip("""ESMFold does not support head pruning."""   )
							def   __a   (     self:    Tuple   ):
										pass




							@unittest.skip("""ESMFold does not support head pruning."""   )
							def   __a   (     self:    Optional[Any]   ):
										pass




							@unittest.skip("""ESMFold does not support head pruning."""   )
							def   __a   (     self:    Dict   ):
										pass




							@unittest.skip("""ESMFold does not support head pruning."""   )
							def   __a   (     self:    Any   ):
										pass




							@unittest.skip("""ESMFold does not output hidden states in the normal way."""   )
							def   __a   (     self:    List[str]   ):
										pass




							@unittest.skip("""ESMfold does not output hidden states in the normal way."""   )
							def   __a   (     self:    str   ):
										pass




							@unittest.skip("""ESMFold only has one output format."""   )
							def   __a   (     self:    Optional[Any]   ):
										pass




							@unittest.skip("""This test doesn't work for ESMFold and doesn't test core functionality"""   )
							def   __a   (     self:    str   ):
										pass




							@unittest.skip("""ESMFold does not support input chunking."""   )
							def   __a   (     self:    str   ):
										pass




							@unittest.skip("""ESMFold doesn't respect you and it certainly doesn't respect your initialization arguments."""   )
							def   __a   (     self:    List[str]   ):
										pass




							@unittest.skip("""ESMFold doesn't support torchscript compilation."""   )
							def   __a   (     self:    List[str]   ):
										pass




							@unittest.skip("""ESMFold doesn't support torchscript compilation."""   )
							def   __a   (     self:    Dict   ):
										pass




							@unittest.skip("""ESMFold doesn't support torchscript compilation."""   )
							def   __a   (     self:    Dict   ):
										pass




							@unittest.skip("""ESMFold doesn't support data parallel."""   )
							def   __a   (     self:    List[Any]   ):
										pass




							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""   )
							def   __a   (     self:    str   ):
										pass




@require_torch
class 			__snake_case   (lowerCamelCase  ):




							@slow
							def   __a   (     self:    Optional[int]   ):
										__lowerCamelCase          =      EsmForProteinFolding.from_pretrained("""facebook/esmfold_v1"""   ).float()
										model.eval()
										__lowerCamelCase          =      torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]   )
										__lowerCamelCase          =      model(A_   )["""positions"""]
										__lowerCamelCase          =      torch.tensor([2.5_828, 0.7_993, -10.9_334]     ,							dtype=torch.floataa   )
										self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]     ,							A_     ,							atol=1E-4   )   )








"""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




