

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







class 			lowercase    (	lowercase_      ):




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




				def    a							( self	):
											snake_case_          =   0
											snake_case_          =   False
											while not completed:
																		if counter == 1:
																									self.reset()
																		snake_case_          =   self.advance()
																		if not self.does_advance(snake_case	):
																									raise Exception(
																									    'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'	)

																		snake_case_    ,   snake_case_    ,   snake_case_          =   self.update(snake_case	)
																		counter += 1

																		if counter > 1_0000:
																									raise Exception('update() does not fulfill the constraint.'	)

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




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




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




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




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




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




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







class 			lowercase    (	lowercase_      ):




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

											if not isinstance(snake_case				,       snake_case	) or len(snake_case	) == 0:
																		raise ValueError(F'''`token_ids` has to be a non-empty list, but is {token_ids}.'''	)
											if any((not isinstance(snake_case				,       snake_case	) 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}.'''	)

											snake_case_          =   token_ids

											snake_case_          =   len(self.token_ids	)
											snake_case_          =   -1  # the index of the currently fulfilled step
											snake_case_          =   False




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




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

											if self.completed:
																		return False

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




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

											snake_case_          =   False
											snake_case_          =   False
											snake_case_          =   False

											if self.does_advance(snake_case	):
																		self.fulfilled_idx += 1
																		snake_case_          =   True
																		if self.fulfilled_idx == (self.seqlen - 1):
																									snake_case_          =   True
																		snake_case_          =   completed
											else:
																		# failed to make progress.
																		snake_case_          =   True
																		self.reset()
											return stepped, completed, reset




				def    a							( self	):
											snake_case_          =   False
											snake_case_          =   0




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




				def    a							( self				,       snake_case=False	):
											snake_case_          =   PhrasalConstraint(self.token_ids	)

											if stateful:
																		snake_case_          =   self.seqlen
																		snake_case_          =   self.fulfilled_idx
																		snake_case_          =   self.completed

											return new_constraint







class 			lowercase    :




				def __init__( self				,       snake_case				,       snake_case=True	):
											snake_case_          =   max([len(snake_case	) for one in nested_token_ids]	)

											snake_case_          =   {}
											for token_ids in nested_token_ids:
																		snake_case_          =   root
																		for tidx, token_id in enumerate(snake_case	):
																									if token_id not in level:
																																snake_case_          =   {}

																									snake_case_          =   level[token_id]

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

											snake_case_          =   root




				def    a							( self				,       snake_case	):
											snake_case_          =   self.trie

											for current_token in current_seq:
																		snake_case_          =   start[current_token]

											snake_case_          =   list(start.keys()	)

											return next_tokens




				def    a							( self				,       snake_case	):
											snake_case_          =   self.next_tokens(snake_case	)

											return len(snake_case	) == 0




				def    a							( self				,       snake_case	):
											snake_case_          =   list(root.values()	)
											if len(snake_case	) == 0:
																		return 1
											else:
																		return sum([self.count_leaves(snake_case	) for nn in next_nodes]	)




				def    a							( self				,       snake_case				,       snake_case	):
											snake_case_          =   self.count_leaves(snake_case	)
											return len(snake_case	) != leaf_count







class 			lowercase    (	lowercase_      ):




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

											if not isinstance(snake_case				,       snake_case	) or len(snake_case	) == 0:
																		raise ValueError(F'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.'''	)
											if any(not isinstance(snake_case				,       snake_case	) 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(snake_case				,       snake_case	) 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}.'''	)

											snake_case_          =   DisjunctiveTrie(snake_case	)
											snake_case_          =   nested_token_ids

											snake_case_          =   self.trie.max_height
											snake_case_          =   []
											snake_case_          =   False




				def    a							( self	):
											snake_case_          =   self.trie.next_tokens(self.current_seq	)

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




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

											snake_case_          =   self.trie.next_tokens(self.current_seq	)

											return token_id in next_tokens




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

											snake_case_          =   False
											snake_case_          =   False
											snake_case_          =   False

											if self.does_advance(snake_case	):
																		self.current_seq.append(snake_case	)
																		snake_case_          =   True
											else:
																		snake_case_          =   True
																		self.reset()

											snake_case_          =   self.trie.reached_leaf(self.current_seq	)
											snake_case_          =   completed

											return stepped, completed, reset




				def    a							( self	):
											snake_case_          =   False
											snake_case_          =   []




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




				def    a							( self				,       snake_case=False	):
											snake_case_          =   DisjunctiveConstraint(self.token_ids	)

											if stateful:
																		snake_case_          =   self.seqlen
																		snake_case_          =   self.current_seq
																		snake_case_          =   self.completed

											return new_constraint







class 			lowercase    :




				def __init__( self				,       snake_case	):
											snake_case_          =   constraints

											# max # of steps required to fulfill a given constraint
											snake_case_          =   max([c.seqlen for c in constraints]	)
											snake_case_          =   len(snake_case	)
											snake_case_          =   False

											self.init_state()




				def    a							( self	):
											snake_case_          =   []
											snake_case_          =   None
											snake_case_          =   [constraint.copy(stateful=snake_case	) for constraint in self.constraints]




				def    a							( self	):
											snake_case_          =   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    a							( self	):
											snake_case_          =   []
											if self.inprogress_constraint is None:
																		for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
																									snake_case_          =   constraint.advance()
																									if isinstance(snake_case				,       snake_case	):
																																token_list.append(snake_case	)
																									elif isinstance(snake_case				,       snake_case	):
																																token_list.extend(snake_case	)
											else:
																		snake_case_          =   self.inprogress_constraint.advance()
																		if isinstance(snake_case				,       snake_case	):
																									token_list.append(snake_case	)
																		elif isinstance(snake_case				,       snake_case	):
																									token_list.extend(snake_case	)

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




				def    a							( self				,       snake_case	):
											self.init_state()

											if token_ids is not None:
																		for token in token_ids:
																									# completes or steps **one** constraint
																									snake_case_    ,   snake_case_          =   self.add(snake_case	)

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




				def    a							( self				,       snake_case	):
											if not isinstance(snake_case				,       snake_case	):
																		raise ValueError(F'''`token_id` should be an `int`, but is `{token_id}`.'''	)

											snake_case_    ,   snake_case_          =   False, False

											if self.completed:
																		snake_case_          =   True
																		snake_case_          =   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

																		snake_case_    ,   snake_case_    ,   snake_case_          =   self.inprogress_constraint.update(snake_case	)
																		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=snake_case	)	)
																									snake_case_          =   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	)
																									snake_case_          =   None

																									if len(self.pending_constraints	) == 0:
																																# we're done!
																																snake_case_          =   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(snake_case	):
																																snake_case_    ,   snake_case_    ,   snake_case_          =   pending_constraint.update(snake_case	)

																																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(snake_case	)
																																							snake_case_          =   None

																																if not complete and stepped:
																																							snake_case_          =   pending_constraint

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

																																							snake_case_          =   (
																																							    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.

																																														snake_case_          =   True

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

											return complete, stepped




				def    a							( self				,       snake_case=True	):
											snake_case_          =   ConstraintListState(self.constraints	)  # we actually never though self.constraints objects
											# throughout this process. So it's at initialization state.

											if stateful:
																		snake_case_          =   [
																		    constraint.copy(stateful=snake_case	) for constraint in self.complete_constraints
																		]
																		if self.inprogress_constraint is not None:
																									snake_case_          =   self.inprogress_constraint.copy(stateful=snake_case	)
																		snake_case_          =   [constraint.copy() for constraint in self.pending_constraints]

											return new_state







import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 import (
					    XLMForMultipleChoice,
					    XLMForQuestionAnswering,
					    XLMForQuestionAnsweringSimple,
					    XLMForSequenceClassification,
					    XLMForTokenClassification,
					    XLMModel,
					    XLMWithLMHeadModel,
					)
					from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST







class 			lowercase    :




				def __init__( self				,       snake_case				,       snake_case=13				,       snake_case=7				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=False				,       snake_case=False				,       snake_case=False				,       snake_case=2				,       snake_case=99				,       snake_case=0				,       snake_case=32				,       snake_case=5				,       snake_case=4				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=512				,       snake_case=2				,       snake_case=0.02				,       snake_case=2				,       snake_case=4				,       snake_case="last"				,       snake_case=True				,       snake_case=None				,       snake_case=0				,       ):
											snake_case_          =   parent
											snake_case_          =   batch_size
											snake_case_          =   seq_length
											snake_case_          =   is_training
											snake_case_          =   use_input_lengths
											snake_case_          =   use_token_type_ids
											snake_case_          =   use_labels
											snake_case_          =   gelu_activation
											snake_case_          =   sinusoidal_embeddings
											snake_case_          =   causal
											snake_case_          =   asm
											snake_case_          =   n_langs
											snake_case_          =   vocab_size
											snake_case_          =   n_special
											snake_case_          =   hidden_size
											snake_case_          =   num_hidden_layers
											snake_case_          =   num_attention_heads
											snake_case_          =   hidden_dropout_prob
											snake_case_          =   attention_probs_dropout_prob
											snake_case_          =   max_position_embeddings
											snake_case_          =   type_sequence_label_size
											snake_case_          =   initializer_range
											snake_case_          =   num_labels
											snake_case_          =   num_choices
											snake_case_          =   summary_type
											snake_case_          =   use_proj
											snake_case_          =   scope
											snake_case_          =   bos_token_id




				def    a							( self	):
											snake_case_          =   ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size	)
											snake_case_          =   random_attention_mask([self.batch_size, self.seq_length]	)

											snake_case_          =   None
											if self.use_input_lengths:
																		snake_case_          =   (
																		    ids_tensor([self.batch_size]				,       vocab_size=2	) + self.seq_length - 2
																		)  # small variation of seq_length

											snake_case_          =   None
											if self.use_token_type_ids:
																		snake_case_          =   ids_tensor([self.batch_size, self.seq_length]				,       self.n_langs	)

											snake_case_          =   None
											snake_case_          =   None
											snake_case_          =   None
											if self.use_labels:
																		snake_case_          =   ids_tensor([self.batch_size]				,       self.type_sequence_label_size	)
																		snake_case_          =   ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels	)
																		snake_case_          =   ids_tensor([self.batch_size]				,       2	).float()
																		snake_case_          =   ids_tensor([self.batch_size]				,       self.num_choices	)

											snake_case_          =   self.get_config()

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    input_lengths,
											    sequence_labels,
											    token_labels,
											    is_impossible_labels,
											    choice_labels,
											    input_mask,
											)




				def    a							( self	):
											return XLMConfig(
											    vocab_size=self.vocab_size				,       n_special=self.n_special				,       emb_dim=self.hidden_size				,       n_layers=self.num_hidden_layers				,       n_heads=self.num_attention_heads				,       dropout=self.hidden_dropout_prob				,       attention_dropout=self.attention_probs_dropout_prob				,       gelu_activation=self.gelu_activation				,       sinusoidal_embeddings=self.sinusoidal_embeddings				,       asm=self.asm				,       causal=self.causal				,       n_langs=self.n_langs				,       max_position_embeddings=self.max_position_embeddings				,       initializer_range=self.initializer_range				,       summary_type=self.summary_type				,       use_proj=self.use_proj				,       num_labels=self.num_labels				,       bos_token_id=self.bos_token_id				,       )




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMModel(config=snake_case	)
											model.to(snake_case	)
											model.eval()
											snake_case_          =   model(snake_case				,       lengths=snake_case				,       langs=snake_case	)
											snake_case_          =   model(snake_case				,       langs=snake_case	)
											snake_case_          =   model(snake_case	)
											self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMWithLMHeadModel(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case				,       token_type_ids=snake_case				,       labels=snake_case	)
											self.parent.assertEqual(result.loss.shape				,       ()	)
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMForQuestionAnsweringSimple(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case	)

											snake_case_          =   model(snake_case				,       start_positions=snake_case				,       end_positions=snake_case	)
											snake_case_          =   outputs
											self.parent.assertEqual(result.start_logits.shape				,       (self.batch_size, self.seq_length)	)
											self.parent.assertEqual(result.end_logits.shape				,       (self.batch_size, self.seq_length)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMForQuestionAnswering(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case	)

											snake_case_          =   model(
											    snake_case				,       start_positions=snake_case				,       end_positions=snake_case				,       cls_index=snake_case				,       is_impossible=snake_case				,       p_mask=snake_case				,       )

											snake_case_          =   model(
											    snake_case				,       start_positions=snake_case				,       end_positions=snake_case				,       cls_index=snake_case				,       is_impossible=snake_case				,       )

											((snake_case_)    ,   )          =   result_with_labels.to_tuple()

											snake_case_          =   model(snake_case				,       start_positions=snake_case				,       end_positions=snake_case	)

											((snake_case_)    ,   )          =   result_with_labels.to_tuple()

											self.parent.assertEqual(result_with_labels.loss.shape				,       ()	)
											self.parent.assertEqual(result.start_top_log_probs.shape				,       (self.batch_size, model.config.start_n_top)	)
											self.parent.assertEqual(result.start_top_index.shape				,       (self.batch_size, model.config.start_n_top)	)
											self.parent.assertEqual(
											    result.end_top_log_probs.shape				,       (self.batch_size, model.config.start_n_top * model.config.end_n_top)	)
											self.parent.assertEqual(
											    result.end_top_index.shape				,       (self.batch_size, model.config.start_n_top * model.config.end_n_top)	)
											self.parent.assertEqual(result.cls_logits.shape				,       (self.batch_size,)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMForSequenceClassification(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case	)
											snake_case_          =   model(snake_case				,       labels=snake_case	)
											self.parent.assertEqual(result.loss.shape				,       ()	)
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.type_sequence_label_size)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   self.num_labels
											snake_case_          =   XLMForTokenClassification(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case				,       attention_mask=snake_case				,       labels=snake_case	)
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   self.num_choices
											snake_case_          =   XLMForMultipleChoice(config=snake_case	)
											model.to(snake_case	)
											model.eval()
											snake_case_          =   input_ids.unsqueeze(1	).expand(-1				,       self.num_choices				,       -1	).contiguous()
											snake_case_          =   token_type_ids.unsqueeze(1	).expand(-1				,       self.num_choices				,       -1	).contiguous()
											snake_case_          =   input_mask.unsqueeze(1	).expand(-1				,       self.num_choices				,       -1	).contiguous()
											snake_case_          =   model(
											    snake_case				,       attention_mask=snake_case				,       token_type_ids=snake_case				,       labels=snake_case				,       )
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_choices)	)




				def    a							( self	):
											snake_case_          =   self.prepare_config_and_inputs()
											(
											    (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   
											)          =   config_and_inputs
											snake_case_          =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
											return config, inputs_dict







@require_torch
class 			lowercase    (	lowercase_     ,  lowercase_     ,  lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      (
				    (
				        XLMModel,
				        XLMWithLMHeadModel,
				        XLMForQuestionAnswering,
				        XLMForSequenceClassification,
				        XLMForQuestionAnsweringSimple,
				        XLMForTokenClassification,
				        XLMForMultipleChoice,
				    )
				    if is_torch_available()
				    else ()
				)
				__SCREAMING_SNAKE_CASE :			Tuple	      =      (
				    (XLMWithLMHeadModel,) if is_torch_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				__SCREAMING_SNAKE_CASE :			int	      =      (
				    {
				        '''feature-extraction''': XLMModel,
				        '''fill-mask''': XLMWithLMHeadModel,
				        '''question-answering''': XLMForQuestionAnsweringSimple,
				        '''text-classification''': XLMForSequenceClassification,
				        '''text-generation''': XLMWithLMHeadModel,
				        '''token-classification''': XLMForTokenClassification,
				        '''zero-shot''': XLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case	):
											if (
											    pipeline_test_casse_name == "QAPipelineTests"
											    and tokenizer_name is not None
											    and not tokenizer_name.endswith('Fast'	)
											):
																		# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																		# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																		# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																		return True

											return False




				def    a							( self				,       snake_case				,       snake_case				,       snake_case=False	):
											snake_case_          =   super()._prepare_for_class(snake_case				,       snake_case				,       return_labels=snake_case	)

											if return_labels:
																		if model_class.__name__ == "XLMForQuestionAnswering":
																									snake_case_          =   torch.zeros(
																									    self.model_tester.batch_size				,       dtype=torch.long				,       device=snake_case	)
																									snake_case_          =   torch.zeros(
																									    self.model_tester.batch_size				,       dtype=torch.long				,       device=snake_case	)

											return inputs_dict




				def    a							( self	):
											snake_case_          =   XLMModelTester(self	)
											snake_case_          =   ConfigTester(self				,       config_class=snake_case				,       emb_dim=37	)




				def    a							( self	):
											self.config_tester.run_common_tests()




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_model(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_lm_head(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_simple_qa(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_qa(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_sequence_classif(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_token_classif(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case=False				,       snake_case=1	):
											self.assertIsInstance(snake_case				,       snake_case	)
											self.assertListEqual(
											    [isinstance(snake_case				,       snake_case	) for iter_attentions in attentions]				,       [True] * len(snake_case	)	)
											self.assertEqual(len(snake_case	)				,       (max_length - min_length) * num_beam_groups	)

											for idx, iter_attentions in enumerate(snake_case	):
																		# adds PAD dummy token
																		snake_case_          =   min_length + idx + 1
																		snake_case_          =   min_length + idx + 1

																		snake_case_          =   (
																		    batch_size * num_beam_groups,
																		    config.num_attention_heads,
																		    tgt_len,
																		    src_len,
																		)
																		# check attn size
																		self.assertListEqual(
																		    [layer_attention.shape for layer_attention in iter_attentions]				,       [expected_shape] * len(snake_case	)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case=False				,       snake_case=1	):
											self.assertIsInstance(snake_case				,       snake_case	)
											self.assertListEqual(
											    [isinstance(snake_case				,       snake_case	) for iter_hidden_states in hidden_states]				,       [True] * len(snake_case	)				,       )
											self.assertEqual(len(snake_case	)				,       (max_length - min_length) * num_beam_groups	)

											for idx, iter_hidden_states in enumerate(snake_case	):
																		# adds PAD dummy token
																		snake_case_          =   min_length + idx + 1
																		snake_case_          =   (batch_size * num_beam_groups, seq_len, config.hidden_size)
																		# check hidden size
																		self.assertListEqual(
																		    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]				,       [expected_shape] * len(snake_case	)				,       )
											pass




				@slow
				def    a							( self	):
											for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_          =   XLMModel.from_pretrained(snake_case	)
																		self.assertIsNotNone(snake_case	)







@require_torch
class 			lowercase    (	unittest.TestCase      ):




				@slow
				def    a							( self	):
											snake_case_          =   XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'	)
											model.to(snake_case	)
											snake_case_          =   torch.tensor([[14, 447]]				,       dtype=torch.long				,       device=snake_case	)  # the president
											snake_case_          =   [
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											]  # the president the president the president the president the president the president the president the president the president the president
											# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
											snake_case_          =   model.generate(snake_case				,       do_sample=snake_case	)
											self.assertListEqual(output_ids[0].cpu().numpy().tolist()				,       snake_case	)




