




"""simple docstring"""







from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__A   :  str					=      logging.get_logger(__name__)

__A   :  Optional[Any]					=      {
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}

class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     Dict       =      """umt5"""
				__UpperCAmelCase :     List[Any]       =      ["""past_key_values"""]



				def __init__(     self					:	Union[str, Any]    ,   lowercase__					:	List[Any]=2_5_0_1_1_2    ,   lowercase__					:	Optional[int]=5_1_2    ,   lowercase__					:	Optional[int]=6_4    ,   lowercase__					:	Any=1_0_2_4    ,   lowercase__					:	Tuple=8    ,   lowercase__					:	Any=None    ,   lowercase__					:	List[str]=6    ,   lowercase__					:	Tuple=3_2    ,   lowercase__					:	Optional[int]=1_2_8    ,   lowercase__					:	List[str]=0.1    ,   lowercase__					:	Optional[Any]=1e-6    ,   lowercase__					:	List[Any]=1.0    ,   lowercase__					:	Dict="gated-gelu"    ,   lowercase__					:	Any=True    ,   lowercase__					:	Optional[Any]=True    ,   lowercase__					:	Union[str, Any]="T5Tokenizer"    ,   lowercase__					:	Optional[Any]=True    ,   lowercase__					:	Any=0    ,   lowercase__					:	Union[str, Any]=1    ,   lowercase__					:	List[str]=0    ,   **lowercase__					:	str    ,   ):
						super().__init__(
						    is_encoder_decoder=lowercase__    ,   tokenizer_class=lowercase__    ,   tie_word_embeddings=lowercase__    ,   pad_token_id=lowercase__    ,   eos_token_id=lowercase__    ,   decoder_start_token_id=lowercase__    ,   **lowercase__    ,   )
						__lowercase				:		List[str]												=				vocab_size
						__lowercase				:		Any												=				d_model
						__lowercase				:		Tuple												=				d_kv
						__lowercase				:		List[Any]												=				d_ff
						__lowercase				:		Union[str, Any]												=				num_layers
						__lowercase				:		Tuple												=				(
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						__lowercase				:		Optional[Any]												=				num_heads
						__lowercase				:		Dict												=				relative_attention_num_buckets
						__lowercase				:		Any												=				relative_attention_max_distance
						__lowercase				:		Dict												=				dropout_rate
						__lowercase				:		Any												=				layer_norm_epsilon
						__lowercase				:		Optional[int]												=				initializer_factor
						__lowercase				:		List[str]												=				feed_forward_proj
						__lowercase				:		Dict												=				use_cache

						__lowercase				:		List[str]												=				self.feed_forward_proj.split("-"						)
						__lowercase				:		int												=				act_info[-1]
						__lowercase				:		str												=				act_info[0] == "gated"

						if len(lowercase__						) > 1 and act_info[0] != "gated" or len(lowercase__						) > 2:
								raise ValueError(
								    f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
								    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
								    "\'gated-gelu\' or \'relu\'"						)

						if feed_forward_proj == "gated-gelu":
								__lowercase				:		int												=				"gelu_new"



				@property
				def       snake_case    (     self					:	Union[str, Any]						):
						return self.d_model



				@property
				def       snake_case    (     self					:	List[str]						):
						return self.num_heads



				@property
				def       snake_case    (     self					:	Union[str, Any]						):
						return self.num_layers





class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Any												=				{
						    "input_ids": {0: "batch", 1: "encoder_sequence"},
						    "attention_mask": {0: "batch", 1: "encoder_sequence"},
						}
						if self.use_past:
								__lowercase				:		Dict												=				"past_encoder_sequence + sequence"
								__lowercase				:		Optional[Any]												=				{0: "batch"}
								__lowercase				:		Dict												=				{0: "batch", 1: "past_decoder_sequence + sequence"}
						else:
								__lowercase				:		str												=				{0: "batch", 1: "decoder_sequence"}
								__lowercase				:		Optional[int]												=				{0: "batch", 1: "decoder_sequence"}

						if self.use_past:
								self.fill_with_past_key_values_(lowercase__    ,   direction="inputs"						)

						return common_inputs



				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
				def       snake_case    (     self					:	int						):
						return 1_3



				@property
				def       snake_case    (     self					:	int						):
						return 5e-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 						snake_case__       (					_lowerCamelCase				)			->List[Tuple[int, ...]]:






		"""simple docstring"""


		__lowercase				:		Union[str, Any]												=				[]
		if isinstance(_lowerCamelCase,  _lowerCamelCase				):
				for v in tree.values():
						shapes.extend(_fetch_dims(_lowerCamelCase				)				)
		elif isinstance(_lowerCamelCase,  (list, tuple)				):
				for t in tree:
						shapes.extend(_fetch_dims(_lowerCamelCase				)				)
		elif isinstance(_lowerCamelCase,  torch.Tensor				):
				shapes.append(tree.shape				)
		else:
				raise ValueError("Not supported"				)

		return shapes

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






		"""simple docstring"""


		__lowercase				:		List[Any]												=				[]
		for d in reversed(_lowerCamelCase				):
				idx.append(flat_idx % d				)
				__lowercase				:		List[Any]												=				flat_idx // d

		return tuple(reversed(_lowerCamelCase				)				)

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






		"""simple docstring"""


		def reduce_edge_list(_lowerCamelCase				) -> None:
				__lowercase				:		Optional[Any]												=				True
				for i in range(len(_lowerCamelCase				)				):
						__lowercase				:		Any												=				-1 * (i + 1)
						l[reversed_idx] &= tally
						__lowercase				:		List[str]												=				l[reversed_idx]

		if start_edges is None:
				__lowercase				:		Optional[Any]												=				[s == 0 for s in start]
				reduce_edge_list(_lowerCamelCase				)
		if end_edges is None:
				__lowercase				:		Dict												=				[e == (d - 1) for e, d in zip(_lowerCamelCase,  _lowerCamelCase				)]
				reduce_edge_list(_lowerCamelCase				)

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

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

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

		__lowercase				:		Tuple[slice, ...]												=				tuple(_lowerCamelCase				)
		__lowercase				:		Any												=				len(_lowerCamelCase				)

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

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

				__lowercase				:		Any												=				start[divergence_idx]
				return tuple(
				    path + (slice(_lowerCamelCase,  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

				__lowercase				:		int												=				end[divergence_idx]
				return tuple(
				    path + (slice(_lowerCamelCase,  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()				)
				__lowercase				:		int												=				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 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->torch.Tensor:






		"""simple docstring"""


		__lowercase				:		Union[str, Any]												=				t.shape[:no_batch_dims]
		__lowercase				:		Dict												=				list(_flat_idx_to_idx(_lowerCamelCase,  _lowerCamelCase				)				)
		# _get_minimal_slice_set is inclusive
		__lowercase				:		List[str]												=				list(_flat_idx_to_idx(flat_end - 1,  _lowerCamelCase				)				)

		# Get an ordered list of slices to perform
		__lowercase				:		Dict												=				_get_minimal_slice_set(
		    _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  )

		__lowercase				:		Optional[Any]												=				[t[s] for s in slices]

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

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase = False,  _lowerCamelCase = None,  _lowerCamelCase = False,  )			->Any:






		"""simple docstring"""


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

		__lowercase				:		Dict												=				[shape[:no_batch_dims] for shape in _fetch_dims(_lowerCamelCase				)]
		__lowercase				:		List[Any]												=				tuple([max(_lowerCamelCase				) for s in zip(*_lowerCamelCase				)]				)

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

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

		__lowercase				:		int												=				1
		for d in orig_batch_dims:
				flat_batch_dim *= d

		__lowercase				:		Dict												=				flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

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

				__lowercase				:		Dict[str, Any]												=				tensor_tree_map(_lowerCamelCase,  _lowerCamelCase				)

				# Run the layer on the chunk
				__lowercase				:		int												=				layer(**_lowerCamelCase				)

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

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

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

						assign(_lowerCamelCase,  _lowerCamelCase				)
				elif isinstance(_lowerCamelCase,  _lowerCamelCase				):
						for xa, xa in zip(_lowerCamelCase,  _lowerCamelCase				):
								if _add_into_out:
										xa[i : i + chunk_size] += xa
								else:
										__lowercase				:		Optional[int]												=				xa
				elif isinstance(_lowerCamelCase,  torch.Tensor				):
						if _add_into_out:
								out[i : i + chunk_size] += output_chunk
						else:
								__lowercase				:		int												=				output_chunk
				else:
						raise ValueError("Not supported"				)

				i += chunk_size

		__lowercase				:		int												=				tensor_tree_map(lambda _lowerCamelCase				: t.view(orig_batch_dims + t.shape[1:]				),  _lowerCamelCase				)

		return out





class       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	int    ,   lowercase__					:	int = 5_1_2    ,   ):
						__lowercase				:		str												=				max_chunk_size
						__lowercase				:		Optional[int]												=				None
						__lowercase				:		Optional[tuple]												=				None



				def       snake_case    (     self					:	List[str]    ,   lowercase__					:	Callable    ,   lowercase__					:	tuple    ,   lowercase__					:	int						):
						logging.info("Tuning chunk size..."						)

						if min_chunk_size >= self.max_chunk_size:
								return min_chunk_size

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

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

						__lowercase				:		Union[str, Any]												=				0
						__lowercase				:		Optional[Any]												=				len(lowercase__						) - 1
						while i > min_viable_chunk_size_index:
								__lowercase				:		List[Any]												=				test_chunk_size(candidates[i]						)
								if not viable:
										__lowercase				:		Tuple												=				(min_viable_chunk_size_index + i) // 2
								else:
										__lowercase				:		Optional[Any]												=				i
										__lowercase				:		List[Any]												=				(i + len(lowercase__						) - 1) // 2

						return candidates[min_viable_chunk_size_index]



				def       snake_case    (     self					:	Dict    ,   lowercase__					:	Iterable    ,   lowercase__					:	Iterable						):
						__lowercase				:		List[Any]												=				True
						for aa, aa in zip(lowercase__    ,   lowercase__						):
								assert type(lowercase__						) == type(lowercase__						)
								if isinstance(lowercase__    ,   (list, tuple)						):
										consistent &= self._compare_arg_caches(lowercase__    ,   lowercase__						)
								elif isinstance(lowercase__    ,   lowercase__						):
										__lowercase				:		Dict												=				[v for _, v in sorted(aa.items()    ,   key=lambda lowercase__						: x[0]						)]
										__lowercase				:		Optional[int]												=				[v for _, v in sorted(aa.items()    ,   key=lambda lowercase__						: x[0]						)]
										consistent &= self._compare_arg_caches(lowercase__    ,   lowercase__						)
								else:
										consistent &= aa == aa

						return consistent



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	Callable    ,   lowercase__					:	tuple    ,   lowercase__					:	int    ,   ):
						__lowercase				:		Tuple												=				True
						__lowercase				:		tuple												=				tree_map(lambda lowercase__						: a.shape if isinstance(lowercase__    ,   torch.Tensor						) else a    ,   lowercase__    ,   lowercase__						)
						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(lowercase__						)
								__lowercase				:		Optional[int]												=				self._compare_arg_caches(self.cached_arg_data    ,   lowercase__						)
						else:
								# Otherwise, we can reuse the precomputed value
								__lowercase				:		int												=				False

						if not consistent:
								__lowercase				:		List[Any]												=				self._determine_favorable_chunk_size(
								    lowercase__    ,   lowercase__    ,   lowercase__    ,   )
								__lowercase				:		Union[str, Any]												=				arg_data

						assert self.cached_chunk_size is not None

						return self.cached_chunk_size
