
from abc import ABC, abstractmethod
from typing import List, Optional





class       _snake_case					(     snake_case				):







			def __init__(				self		):
				# test for the above condition
				self.test()







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Union[str, Any]	   =     0
				__magic_name__		:		Dict	   =     False
				while not completed:
					if counter == 1:
						self.reset()
					__magic_name__		:		Dict	   =     self.advance()
					if not self.does_advance(_a		):
						raise Exception(
						    "Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true."		)

					__magic_name__   ,    __magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     self.update(_a		)
					counter += 1

					if counter > 10_000:
						raise Exception("update() does not fulfill the constraint."		)

				if self.remaining() != 0:
					raise Exception("Custom Constraint is not defined correctly."		)







			@abstractmethod
			def 			SCREAMING_SNAKE_CASE    (				self		):
				raise NotImplementedError(
				    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''		)







			@abstractmethod
			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				raise NotImplementedError(
				    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''		)







			@abstractmethod
			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				raise NotImplementedError(
				    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''		)







			@abstractmethod
			def 			SCREAMING_SNAKE_CASE    (				self		):
				raise NotImplementedError(
				    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''		)







			@abstractmethod
			def 			SCREAMING_SNAKE_CASE    (				self		):
				raise NotImplementedError(
				    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''		)







			@abstractmethod
			def 			SCREAMING_SNAKE_CASE    (				self						,   _a=False		):
				raise NotImplementedError(
				    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''		)







class       _snake_case					(     snake_case				):







			def __init__(				self						,   _a		):
				super(_a						,   self		).__init__()

				if not isinstance(_a						,   _a		) or len(_a		) == 0:
					raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.'''		)
				if any((not isinstance(_a						,   _a		) or token_id < 0) for token_id in token_ids		):
					raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.'''		)

				__magic_name__		:		List[str]	   =     token_ids

				__magic_name__		:		List[str]	   =     len(self.token_ids		)
				__magic_name__		:		Dict	   =     -1  # the index of the currently fulfilled step
				__magic_name__		:		Optional[int]	   =     False







			def 			SCREAMING_SNAKE_CASE    (				self		):
				if self.completed:
					return None
				return self.token_ids[self.fulfilled_idx + 1]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if not isinstance(_a						,   _a		):
					raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_a		)}'''		)

				if self.completed:
					return False

				return token_id == self.token_ids[self.fulfilled_idx + 1]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if not isinstance(_a						,   _a		):
					raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_a		)}'''		)

				__magic_name__		:		Tuple	   =     False
				__magic_name__		:		List[str]	   =     False
				__magic_name__		:		Any	   =     False

				if self.does_advance(_a		):
					self.fulfilled_idx += 1
					__magic_name__		:		Optional[int]	   =     True
					if self.fulfilled_idx == (self.seqlen - 1):
						__magic_name__		:		Any	   =     True
					__magic_name__		:		Optional[int]	   =     completed
				else:
					# failed to make progress.
					__magic_name__		:		Any	   =     True
					self.reset()
				return stepped, completed, reset







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     False
				__magic_name__		:		str	   =     0







			def 			SCREAMING_SNAKE_CASE    (				self		):
				return self.seqlen - (self.fulfilled_idx + 1)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a=False		):
				__magic_name__		:		str	   =     PhrasalConstraint(self.token_ids		)

				if stateful:
					__magic_name__		:		str	   =     self.seqlen
					__magic_name__		:		Any	   =     self.fulfilled_idx
					__magic_name__		:		Tuple	   =     self.completed

				return new_constraint







class       _snake_case					:







			def __init__(				self						,   _a						,   _a=True		):
				__magic_name__		:		Union[str, Any]	   =     max([len(_a		) for one in nested_token_ids]		)

				__magic_name__		:		List[Any]	   =     {}
				for token_ids in nested_token_ids:
					__magic_name__		:		Optional[int]	   =     root
					for tidx, token_id in enumerate(_a		):
						if token_id not in level:
							__magic_name__		:		List[Any]	   =     {}

						__magic_name__		:		Optional[int]	   =     level[token_id]

				if no_subsets and self.has_subsets(_a						,   _a		):
					raise ValueError(
					    "Each list in `nested_token_ids` can't be a complete subset of another list, but is"
					    f''' {nested_token_ids}.'''		)

				__magic_name__		:		int	   =     root







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		int	   =     self.trie

				for current_token in current_seq:
					__magic_name__		:		Union[str, Any]	   =     start[current_token]

				__magic_name__		:		int	   =     list(start.keys()		)

				return next_tokens







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		int	   =     self.next_tokens(_a		)

				return len(_a		) == 0







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Tuple	   =     list(root.values()		)
				if len(_a		) == 0:
					return 1
				else:
					return sum([self.count_leaves(_a		) for nn in next_nodes]		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				__magic_name__		:		List[Any]	   =     self.count_leaves(_a		)
				return len(_a		) != leaf_count







class       _snake_case					(     snake_case				):







			def __init__(				self						,   _a		):
				super(_a						,   self		).__init__()

				if not isinstance(_a						,   _a		) or len(_a		) == 0:
					raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.'''		)
				if any(not isinstance(_a						,   _a		) for token_ids in nested_token_ids		):
					raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.'''		)
				if any(
				    any((not isinstance(_a						,   _a		) or token_id < 0) for token_id in token_ids		)
				    for token_ids in nested_token_ids		):
					raise ValueError(
					    f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.'''		)

				__magic_name__		:		Any	   =     DisjunctiveTrie(_a		)
				__magic_name__		:		str	   =     nested_token_ids

				__magic_name__		:		List[str]	   =     self.trie.max_height
				__magic_name__		:		Optional[Any]	   =     []
				__magic_name__		:		Union[str, Any]	   =     False







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     self.trie.next_tokens(self.current_seq		)

				if len(_a		) == 0:
					return None
				else:
					return token_list







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if not isinstance(_a						,   _a		):
					raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_a		)}'''		)

				__magic_name__		:		List[Any]	   =     self.trie.next_tokens(self.current_seq		)

				return token_id in next_tokens







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if not isinstance(_a						,   _a		):
					raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_a		)}'''		)

				__magic_name__		:		str	   =     False
				__magic_name__		:		List[str]	   =     False
				__magic_name__		:		Tuple	   =     False

				if self.does_advance(_a		):
					self.current_seq.append(_a		)
					__magic_name__		:		Optional[int]	   =     True
				else:
					__magic_name__		:		Optional[int]	   =     True
					self.reset()

				__magic_name__		:		str	   =     self.trie.reached_leaf(self.current_seq		)
				__magic_name__		:		int	   =     completed

				return stepped, completed, reset







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     False
				__magic_name__		:		Dict	   =     []







			def 			SCREAMING_SNAKE_CASE    (				self		):
				if self.completed:
					# since this can be completed without reaching max height
					return 0
				else:
					return self.seqlen - len(self.current_seq		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a=False		):
				__magic_name__		:		Union[str, Any]	   =     DisjunctiveConstraint(self.token_ids		)

				if stateful:
					__magic_name__		:		Any	   =     self.seqlen
					__magic_name__		:		Dict	   =     self.current_seq
					__magic_name__		:		Optional[int]	   =     self.completed

				return new_constraint







class       _snake_case					:







			def __init__(				self						,   _a		):
				__magic_name__		:		Optional[int]	   =     constraints

				# max # of steps required to fulfill a given constraint
				__magic_name__		:		int	   =     max([c.seqlen for c in constraints]		)
				__magic_name__		:		List[Any]	   =     len(_a		)
				__magic_name__		:		Tuple	   =     False

				self.init_state()







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     []
				__magic_name__		:		Any	   =     None
				__magic_name__		:		Optional[Any]	   =     [constraint.copy(stateful=_a		) for constraint in self.constraints]







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     0
				if self.inprogress_constraint:
					# extra points for having a constraint mid-fulfilled
					add += self.max_seqlen - self.inprogress_constraint.remaining()

				return (len(self.complete_constraints		) * self.max_seqlen) + add







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     []
				if self.inprogress_constraint is None:
					for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
						__magic_name__		:		Optional[Any]	   =     constraint.advance()
						if isinstance(_a						,   _a		):
							token_list.append(_a		)
						elif isinstance(_a						,   _a		):
							token_list.extend(_a		)
				else:
					__magic_name__		:		List[str]	   =     self.inprogress_constraint.advance()
					if isinstance(_a						,   _a		):
						token_list.append(_a		)
					elif isinstance(_a						,   _a		):
						token_list.extend(_a		)

				if len(_a		) == 0:
					return None
				else:
					return token_list







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				self.init_state()

				if token_ids is not None:
					for token in token_ids:
						# completes or steps **one** constraint
						__magic_name__   ,    __magic_name__		:		Optional[Any]	   =     self.add(_a		)

						# the entire list of constraints are fulfilled
						if self.completed:
							break







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if not isinstance(_a						,   _a		):
					raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.'''		)

				__magic_name__   ,    __magic_name__		:		int	   =     False, False

				if self.completed:
					__magic_name__		:		str	   =     True
					__magic_name__		:		List[Any]	   =     False
					return complete, stepped

				if self.inprogress_constraint is not None:
					# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
					# job, simply update the state

					__magic_name__   ,    __magic_name__   ,    __magic_name__		:		List[str]	   =     self.inprogress_constraint.update(_a		)
					if reset:
						# 1. If the next token breaks the progress, then we must restart.
						#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

						#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
						#     constraint, not the full list of constraints.

						self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_a		)		)
						__magic_name__		:		Optional[int]	   =     None

					if complete:
						# 2. If the next token completes the constraint, move it to completed list, set
						#     inprogress to None. If there are no pending constraints either, then this full list of constraints
						#     is complete.

						self.complete_constraints.append(self.inprogress_constraint		)
						__magic_name__		:		Dict	   =     None

						if len(self.pending_constraints		) == 0:
							# we're done!
							__magic_name__		:		Union[str, Any]	   =     True

				else:
					# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
					# of constraints?

					for cidx, pending_constraint in enumerate(self.pending_constraints		):
						if pending_constraint.does_advance(_a		):
							__magic_name__   ,    __magic_name__   ,    __magic_name__		:		str	   =     pending_constraint.update(_a		)

							if not stepped:
								raise Exception(
								    "`constraint.update(token_id)` is not yielding incremental progress, "
								    "even though `constraint.does_advance(token_id)` is true."		)

							if complete:
								self.complete_constraints.append(_a		)
								__magic_name__		:		Any	   =     None

							if not complete and stepped:
								__magic_name__		:		str	   =     pending_constraint

							if complete or stepped:
								# If we made any progress at all, then it's at least not a "pending constraint".

								__magic_name__		:		Optional[int]	   =     (
								    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
								)

								if len(self.pending_constraints		) == 0 and self.inprogress_constraint is None:
									# If there's no longer any pending after this and no inprogress either, then we must be
									# complete.

									__magic_name__		:		Optional[Any]	   =     True

								break  # prevent accidentally stepping through multiple constraints with just one token.

				return complete, stepped







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a=True		):
				__magic_name__		:		Optional[Any]	   =     ConstraintListState(self.constraints		)  # we actually never though self.constraints objects
				# throughout this process. So it's at initialization state.

				if stateful:
					__magic_name__		:		Optional[Any]	   =     [
					    constraint.copy(stateful=_a		) for constraint in self.complete_constraints
					]
					if self.inprogress_constraint is not None:
						__magic_name__		:		Optional[int]	   =     self.inprogress_constraint.copy(stateful=_a		)
					__magic_name__		:		str	   =     [constraint.copy() for constraint in self.pending_constraints]

				return new_state


import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
				import jax.numpy as jnp

				from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
				    FlaxRobertaPreLayerNormForCausalLM,
				    FlaxRobertaPreLayerNormForMaskedLM,
				    FlaxRobertaPreLayerNormForMultipleChoice,
				    FlaxRobertaPreLayerNormForQuestionAnswering,
				    FlaxRobertaPreLayerNormForSequenceClassification,
				    FlaxRobertaPreLayerNormForTokenClassification,
				    FlaxRobertaPreLayerNormModel,
				)





class       _snake_case					(     unittest.TestCase				):







			def __init__(				self						,   _a						,   _a=13						,   _a=7						,   _a=True						,   _a=True						,   _a=True						,   _a=True						,   _a=99						,   _a=32						,   _a=5						,   _a=4						,   _a=37						,   _a="gelu"						,   _a=0.1						,   _a=0.1						,   _a=512						,   _a=16						,   _a=2						,   _a=0.02						,   _a=4						,   ):
				__magic_name__		:		List[Any]	   =     parent
				__magic_name__		:		Optional[Any]	   =     batch_size
				__magic_name__		:		Dict	   =     seq_length
				__magic_name__		:		Union[str, Any]	   =     is_training
				__magic_name__		:		Optional[Any]	   =     use_attention_mask
				__magic_name__		:		Optional[Any]	   =     use_token_type_ids
				__magic_name__		:		int	   =     use_labels
				__magic_name__		:		List[Any]	   =     vocab_size
				__magic_name__		:		Union[str, Any]	   =     hidden_size
				__magic_name__		:		Optional[Any]	   =     num_hidden_layers
				__magic_name__		:		int	   =     num_attention_heads
				__magic_name__		:		Any	   =     intermediate_size
				__magic_name__		:		List[Any]	   =     hidden_act
				__magic_name__		:		List[Any]	   =     hidden_dropout_prob
				__magic_name__		:		Optional[int]	   =     attention_probs_dropout_prob
				__magic_name__		:		List[Any]	   =     max_position_embeddings
				__magic_name__		:		Tuple	   =     type_vocab_size
				__magic_name__		:		List[str]	   =     type_sequence_label_size
				__magic_name__		:		Dict	   =     initializer_range
				__magic_name__		:		List[Any]	   =     num_choices







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size		)

				__magic_name__		:		List[Any]	   =     None
				if self.use_attention_mask:
					__magic_name__		:		List[str]	   =     random_attention_mask([self.batch_size, self.seq_length]		)

				__magic_name__		:		str	   =     None
				if self.use_token_type_ids:
					__magic_name__		:		str	   =     ids_tensor([self.batch_size, self.seq_length]						,   self.type_vocab_size		)

				__magic_name__		:		List[str]	   =     RobertaPreLayerNormConfig(
				    vocab_size=self.vocab_size						,   hidden_size=self.hidden_size						,   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						,   is_decoder=_a						,   initializer_range=self.initializer_range						,   )

				return config, input_ids, token_type_ids, attention_mask







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     self.prepare_config_and_inputs()
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		List[Any]	   =     config_and_inputs
				__magic_name__		:		List[str]	   =     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
				return config, inputs_dict







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     self.prepare_config_and_inputs()
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     config_and_inputs

				__magic_name__		:		Tuple	   =     True
				__magic_name__		:		int	   =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
				__magic_name__		:		Optional[Any]	   =     ids_tensor([self.batch_size, self.seq_length]						,   vocab_size=2		)

				return (
				    config,
				    input_ids,
				    token_type_ids,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class       _snake_case					(     snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    True

			UpperCamelCase__          =    (
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     FlaxRobertaPreLayerNormModelTester(self		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				for model_class_name in self.all_model_classes:
					__magic_name__		:		Optional[Any]	   =     model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"						,   from_pt=_a		)
					__magic_name__		:		Dict	   =     model(np.ones((1, 1)		)		)
					self.assertIsNotNone(_a		)







@require_flax
class       _snake_case					(     unittest.TestCase				):







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"						,   from_pt=_a		)

				__magic_name__		:		Union[str, Any]	   =     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						,   dtype=jnp.intaa		)
				__magic_name__		:		List[str]	   =     model(_a		)[0]
				__magic_name__		:		str	   =     [1, 11, 50_265]
				self.assertEqual(list(output.shape		)						,   _a		)
				# compare the actual values for a slice.
				__magic_name__		:		List[str]	   =     np.array(
				    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]						,   dtype=np.floataa		)
				self.assertTrue(np.allclose(output[:, :3, :3]						,   _a						,   atol=1e-4		)		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"						,   from_pt=_a		)

				__magic_name__		:		Tuple	   =     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						,   dtype=jnp.intaa		)
				__magic_name__		:		Tuple	   =     model(_a		)[0]
				# compare the actual values for a slice.
				__magic_name__		:		Dict	   =     np.array(
				    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]						,   dtype=np.floataa		)
				self.assertTrue(np.allclose(output[:, :3, :3]						,   _a						,   atol=1e-4		)		)
