





"""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 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






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

	return shapes

@torch.jit.ignore
def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		[]
	for d in reversed(_UpperCAmelCase	):
		idx.append(flat_idx % d	)
		A_  :				Tuple	   =		flat_idx // d

	return tuple(reversed(_UpperCAmelCase	)	)

@torch.jit.ignore
def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase = None  ,      _UpperCAmelCase = None  ,      ):




	"""simple docstring"""






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

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

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

	A_  :				Tuple[slice, ...]	   =		tuple(_UpperCAmelCase	)
	A_  :				str	   =		len(_UpperCAmelCase	)

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

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

		A_  :				Dict	   =		start[divergence_idx]
		return tuple(
		    path + (slice(_UpperCAmelCase  ,      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_  :				Optional[Any]	   =		end[divergence_idx]
		return tuple(
		    path + (slice(_UpperCAmelCase  ,      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_  :				Optional[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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Dict	   =		t.shape[:no_batch_dims]
	A_  :				int	   =		list(_flat_idx_to_idx(_UpperCAmelCase  ,      _UpperCAmelCase	)	)
	# _get_minimal_slice_set is inclusive
	A_  :				Tuple	   =		list(_flat_idx_to_idx(flat_end - 1  ,      _UpperCAmelCase	)	)

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

	A_  :				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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase = False  ,      _UpperCAmelCase = None  ,      _UpperCAmelCase = False  ,      ):




	"""simple docstring"""






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

	A_  :				Tuple	   =		[shape[:no_batch_dims] for shape in _fetch_dims(_UpperCAmelCase	)]
	A_  :				Any	   =		tuple([max(_UpperCAmelCase	) for s in zip(*_UpperCAmelCase	)]	)

	def _prep_inputs(_UpperCAmelCase	) -> torch.Tensor:
		if not low_mem:
			if not sum(t.shape[:no_batch_dims]	) == no_batch_dims:
				A_  :				List[Any]	   =		t.expand(orig_batch_dims + t.shape[no_batch_dims:]	)
			A_  :				Dict	   =		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  ,      _UpperCAmelCase	)
	A_  :				Dict	   =		None
	if _out is not None:
		A_  :				Union[str, Any]	   =		tensor_tree_map(lambda _UpperCAmelCase	: t.view([-1] + list(t.shape[no_batch_dims:]	)	)  ,      _out	)

	A_  :				Optional[Any]	   =		1
	for d in orig_batch_dims:
		flat_batch_dim *= d

	A_  :				int	   =		flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

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

		A_  :				Dict[str, Any]	   =		tensor_tree_map(_UpperCAmelCase  ,      _UpperCAmelCase	)

		# Run the layer on the chunk
		A_  :				Optional[int]	   =		layer(**_UpperCAmelCase	)

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

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

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

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

		i += chunk_size

	A_  :				Union[str, Any]	   =		tensor_tree_map(lambda _UpperCAmelCase	: t.view(orig_batch_dims + t.shape[1:]	)  ,      _UpperCAmelCase	)

	return out

class        lowercase      :







				def __init__(							self   :					str      ,     _lowerCamelCase   :					int = 5_12      ,     ):



					"""simple docstring"""

					A_  :				Dict	   =		max_chunk_size
					A_  :				Optional[int]	   =		None
					A_  :				Optional[tuple]	   =		None







				def 					a_  (							self   :					Any      ,     _lowerCamelCase   :					Callable      ,     _lowerCamelCase   :					tuple      ,     _lowerCamelCase   :					int       ):



					"""simple docstring"""

					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_  :				Tuple	   =		[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_  :				Any	   =		0
					A_  :				Optional[Any]	   =		len(_lowerCamelCase       ) - 1
					while i > min_viable_chunk_size_index:
						A_  :				int	   =		test_chunk_size(candidates[i]       )
						if not viable:
							A_  :				Tuple	   =		(min_viable_chunk_size_index + i) // 2
						else:
							A_  :				int	   =		i
							A_  :				str	   =		(i + len(_lowerCamelCase       ) - 1) // 2

					return candidates[min_viable_chunk_size_index]







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					Iterable      ,     _lowerCamelCase   :					Iterable       ):



					"""simple docstring"""

					A_  :				Optional[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_  :				Tuple	   =		[v for _, v in sorted(aa.items()      ,     key=lambda _lowerCamelCase       : x[0]       )]
							A_  :				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 					a_  (							self   :					Dict      ,     _lowerCamelCase   :					Callable      ,     _lowerCamelCase   :					tuple      ,     _lowerCamelCase   :					int      ,     ):



					"""simple docstring"""

					A_  :				int	   =		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_  :				Union[str, Any]	   =		self._compare_arg_caches(self.cached_arg_data      ,     _lowerCamelCase       )
					else:
						# Otherwise, we can reuse the precomputed value
						A_  :				Dict	   =		False

					if not consistent:
						A_  :				List[str]	   =		self._determine_favorable_chunk_size(
						    _lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase      ,     )
						A_  :				Dict	   =		arg_data

					assert self.cached_chunk_size is not None

					return self.cached_chunk_size











"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase  :			List[Any]        =       logging.get_logger(__name__)

_lowerCamelCase  :			Tuple        =       {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Optional[Any]         =   """camembert"""







				def __init__(							self   :					List[Any]      ,     _lowerCamelCase   :					Optional[Any]=3_05_22      ,     _lowerCamelCase   :					List[str]=7_68      ,     _lowerCamelCase   :					Optional[Any]=12      ,     _lowerCamelCase   :					List[str]=12      ,     _lowerCamelCase   :					Any=30_72      ,     _lowerCamelCase   :					List[str]="gelu"      ,     _lowerCamelCase   :					Optional[Any]=0.1      ,     _lowerCamelCase   :					Optional[int]=0.1      ,     _lowerCamelCase   :					Union[str, Any]=5_12      ,     _lowerCamelCase   :					str=2      ,     _lowerCamelCase   :					Optional[Any]=0.02      ,     _lowerCamelCase   :					int=1E-12      ,     _lowerCamelCase   :					str=1      ,     _lowerCamelCase   :					Union[str, Any]=0      ,     _lowerCamelCase   :					Optional[int]=2      ,     _lowerCamelCase   :					List[str]="absolute"      ,     _lowerCamelCase   :					str=True      ,     _lowerCamelCase   :					List[Any]=None      ,     **_lowerCamelCase   :					Union[str, Any]      ,     ):



					"""simple docstring"""

					super().__init__(pad_token_id=_lowerCamelCase      ,     bos_token_id=_lowerCamelCase      ,     eos_token_id=_lowerCamelCase      ,     **_lowerCamelCase       )

					A_  :				Any	   =		vocab_size
					A_  :				Optional[Any]	   =		hidden_size
					A_  :				Any	   =		num_hidden_layers
					A_  :				List[Any]	   =		num_attention_heads
					A_  :				List[Any]	   =		hidden_act
					A_  :				Optional[int]	   =		intermediate_size
					A_  :				Tuple	   =		hidden_dropout_prob
					A_  :				Union[str, Any]	   =		attention_probs_dropout_prob
					A_  :				int	   =		max_position_embeddings
					A_  :				Tuple	   =		type_vocab_size
					A_  :				Dict	   =		initializer_range
					A_  :				Tuple	   =		layer_norm_eps
					A_  :				List[Any]	   =		position_embedding_type
					A_  :				Any	   =		use_cache
					A_  :				Dict	   =		classifier_dropout

class        lowercase      (    __UpperCAmelCase):







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					if self.task == "multiple-choice":
						A_  :				Union[str, Any]	   =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
						A_  :				Dict	   =		{0: '''batch''', 1: '''sequence'''}
					return OrderedDict(
					    [
					        ('''input_ids''', dynamic_axis),
					        ('''attention_mask''', dynamic_axis),
					    ]       )





