


'''simple docstring'''



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 							__UpperCamelCase				:




	def __init__(   self						,				__a						,				__a=13						,				__a=7						,				__a=True						,				__a=True						,				__a=True						,				__a=True						,				__a=True						,				__a=False						,				__a=False						,				__a=False						,				__a=2						,				__a=99						,				__a=0						,				__a=32						,				__a=5						,				__a=4						,				__a=0.1						,				__a=0.1						,				__a=512						,				__a=2						,				__a=0.02						,				__a=2						,				__a=4						,				__a="last"						,				__a=True						,				__a=None						,				__a=0						,				):

						'''simple docstring'''





						__a			:       List[str]					    =  parent
						__a			:       int					    =  batch_size
						__a			:       str					    =  seq_length
						__a			:       Optional[int]					    =  is_training
						__a			:       Any					    =  use_input_lengths
						__a			:       Optional[int]					    =  use_token_type_ids
						__a			:       int					    =  use_labels
						__a			:       Dict					    =  gelu_activation
						__a			:       int					    =  sinusoidal_embeddings
						__a			:       str					    =  causal
						__a			:       Union[str, Any]					    =  asm
						__a			:       Optional[Any]					    =  n_langs
						__a			:       Union[str, Any]					    =  vocab_size
						__a			:       List[str]					    =  n_special
						__a			:       Tuple					    =  hidden_size
						__a			:       Any					    =  num_hidden_layers
						__a			:       Dict					    =  num_attention_heads
						__a			:       Tuple					    =  hidden_dropout_prob
						__a			:       Dict					    =  attention_probs_dropout_prob
						__a			:       Tuple					    =  max_position_embeddings
						__a			:       Optional[Any]					    =  type_sequence_label_size
						__a			:       List[Any]					    =  initializer_range
						__a			:       Tuple					    =  num_labels
						__a			:       List[str]					    =  num_choices
						__a			:       str					    =  summary_type
						__a			:       int					    =  use_proj
						__a			:       Dict					    =  scope
						__a			:       Optional[int]					    =  bos_token_id




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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

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

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

						__a			:       Optional[Any]					    =  None
						__a			:       Optional[int]					    =  None
						__a			:       List[Any]					    =  None
						if self.use_labels:
											__a			:       Tuple					    =  ids_tensor([self.batch_size]						,				self.type_sequence_label_size							)
											__a			:       List[Any]					    =  ids_tensor([self.batch_size, self.seq_length]						,				self.num_labels							)
											__a			:       List[Any]					    =  ids_tensor([self.batch_size]						,				2							).float()
											__a			:       Any					    =  ids_tensor([self.batch_size]						,				self.num_choices							)

						__a			:       List[str]					    =  self.get_config()

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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						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       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       Any					    =  XLMModel(config=__a							)
						model.to(__a							)
						model.eval()
						__a			:       Optional[Any]					    =  model(__a						,				lengths=__a						,				langs=__a							)
						__a			:       Union[str, Any]					    =  model(__a						,				langs=__a							)
						__a			:       Optional[int]					    =  model(__a							)
						self.parent.assertEqual(result.last_hidden_state.shape						,				(self.batch_size, self.seq_length, self.hidden_size)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  XLMWithLMHeadModel(__a							)
						model.to(__a							)
						model.eval()

						__a			:       Optional[Any]					    =  model(__a						,				token_type_ids=__a						,				labels=__a							)
						self.parent.assertEqual(result.loss.shape						,				()							)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.seq_length, self.vocab_size)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       List[str]					    =  XLMForQuestionAnsweringSimple(__a							)
						model.to(__a							)
						model.eval()

						__a			:       List[str]					    =  model(__a							)

						__a			:       Optional[int]					    =  model(__a						,				start_positions=__a						,				end_positions=__a							)
						__a			:       Optional[Any]					    =  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       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       Any					    =  XLMForQuestionAnswering(__a							)
						model.to(__a							)
						model.eval()

						__a			:       Dict					    =  model(__a							)

						__a			:       Optional[Any]					    =  model(
						    __a						,				start_positions=__a						,				end_positions=__a						,				cls_index=__a						,				is_impossible=__a						,				p_mask=__a						,				)

						__a			:       List[Any]					    =  model(
						    __a						,				start_positions=__a						,				end_positions=__a						,				cls_index=__a						,				is_impossible=__a						,				)

						((__a)    ,				)			:       Union[str, Any]					    =  result_with_labels.to_tuple()

						__a			:       Union[str, Any]					    =  model(__a						,				start_positions=__a						,				end_positions=__a							)

						((__a)    ,				)			:       Dict					    =  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       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       List[Any]					    =  XLMForSequenceClassification(__a							)
						model.to(__a							)
						model.eval()

						__a			:       Optional[int]					    =  model(__a							)
						__a			:       Union[str, Any]					    =  model(__a						,				labels=__a							)
						self.parent.assertEqual(result.loss.shape						,				()							)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.type_sequence_label_size)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       List[Any]					    =  self.num_labels
						__a			:       Any					    =  XLMForTokenClassification(__a							)
						model.to(__a							)
						model.eval()

						__a			:       Optional[int]					    =  model(__a						,				attention_mask=__a						,				labels=__a							)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.seq_length, self.num_labels)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       str					    =  self.num_choices
						__a			:       Union[str, Any]					    =  XLMForMultipleChoice(config=__a							)
						model.to(__a							)
						model.eval()
						__a			:       int					    =  input_ids.unsqueeze(1							).expand(-1						,				self.num_choices						,				-1							).contiguous()
						__a			:       Any					    =  token_type_ids.unsqueeze(1							).expand(-1						,				self.num_choices						,				-1							).contiguous()
						__a			:       Optional[Any]					    =  input_mask.unsqueeze(1							).expand(-1						,				self.num_choices						,				-1							).contiguous()
						__a			:       str					    =  model(
						    __a						,				attention_mask=__a						,				token_type_ids=__a						,				labels=__a						,				)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_choices)							)





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  self.prepare_config_and_inputs()
						(
						    (
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				
						)			:       Optional[int]					    =  config_and_inputs
						__a			:       Tuple					    =  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
						return config, inputs_dict





@require_torch
class 							__UpperCamelCase				(		lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    (
	    (
	        XLMModel,
	        XLMWithLMHeadModel,
	        XLMForQuestionAnswering,
	        XLMForSequenceClassification,
	        XLMForQuestionAnsweringSimple,
	        XLMForTokenClassification,
	        XLMForMultipleChoice,
	    )
	    if is_torch_available()
	    else ()
	)
	A_				=    (
	    (XLMWithLMHeadModel,) if is_torch_available() else ()
	)  # TODO (PVP): Check other models whether language generation is also applicable
	A_				=    (
	    {
	        "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       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						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       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a=False							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  super()._prepare_for_class(__a						,				__a						,				return_labels=__a							)

						if return_labels:
											if model_class.__name__ == "XLMForQuestionAnswering":
																__a			:       List[Any]					    =  torch.zeros(
																    self.model_tester.batch_size						,				dtype=torch.long						,				device=__a							)
																__a			:       Any					    =  torch.zeros(
																    self.model_tester.batch_size						,				dtype=torch.long						,				device=__a							)

						return inputs_dict




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  XLMModelTester(self							)
						__a			:       Union[str, Any]					    =  ConfigTester(self						,				config_class=__a						,				emb_dim=37							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.config_tester.run_common_tests()




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_model(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_lm_head(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_simple_qa(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_qa(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_sequence_classif(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_token_classif(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_for_multiple_choice(*__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a=False						,				__a=1							):

						'''simple docstring'''





						self.assertIsInstance(__a						,				__a							)
						self.assertListEqual(
						    [isinstance(__a						,				__a							) for iter_attentions in attentions]						,				[True] * len(__a							)							)
						self.assertEqual(len(__a							)						,				(max_length - min_length) * num_beam_groups							)

						for idx, iter_attentions in enumerate(__a							):
											# adds PAD dummy token
											__a			:       Optional[Any]					    =  min_length + idx + 1
											__a			:       List[str]					    =  min_length + idx + 1

											__a			:       List[Any]					    =  (
											    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(__a							)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a=False						,				__a=1							):

						'''simple docstring'''





						self.assertIsInstance(__a						,				__a							)
						self.assertListEqual(
						    [isinstance(__a						,				__a							) for iter_hidden_states in hidden_states]						,				[True] * len(__a							)						,				)
						self.assertEqual(len(__a							)						,				(max_length - min_length) * num_beam_groups							)

						for idx, iter_hidden_states in enumerate(__a							):
											# adds PAD dummy token
											__a			:       List[str]					    =  min_length + idx + 1
											__a			:       Dict					    =  (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(__a							)						,				)
						pass





	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__a			:       Optional[Any]					    =  XLMModel.from_pretrained(__a							)
											self.assertIsNotNone(__a							)





@require_torch
class 							__UpperCamelCase				(		unittest.TestCase					):




	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'							)
						model.to(__a							)
						__a			:       Union[str, Any]					    =  torch.tensor([[14, 447]]						,				dtype=torch.long						,				device=__a							)  # the president
						__a			:       int					    =  [
						    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
						__a			:       Optional[Any]					    =  model.generate(__a						,				do_sample=__a							)
						self.assertListEqual(output_ids[0].cpu().numpy().tolist()						,				__a							)









import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase	:		Any							       =   logging.get_logger(__name__)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''encoder-decoder'''
    UpperCAmelCase__							  =      True


    def __init__(			self      :	List[str]					, **UpperCAmelCase__      :	Union[str, Any])						->List[Any]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)
           assert (
               "encoder" in kwargs and "decoder" in kwargs
           ), "Config has to be initialized with encoder and decoder config"
           A__       =    kwargs.pop('''encoder''')
           A__       =    encoder_config.pop('''model_type''')
           A__       =    kwargs.pop('''decoder''')
           A__       =    decoder_config.pop('''model_type''')

           from ..auto.configuration_auto import AutoConfig

           A__       =    AutoConfig.for_model(UpperCAmelCase__					, **UpperCAmelCase__)
           A__       =    AutoConfig.for_model(UpperCAmelCase__					, **UpperCAmelCase__)
           A__       =    True


    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Union[str, Any]					, UpperCAmelCase__      :	PretrainedConfig					, UpperCAmelCase__      :	PretrainedConfig					, **UpperCAmelCase__      :	Union[str, Any])						->PretrainedConfig:





           '''simple docstring'''

           logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''')
           A__       =    True
           A__       =    True

           return cls(encoder=encoder_config.to_dict()					, decoder=decoder_config.to_dict()					, **UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Optional[Any]:





           '''simple docstring'''

           A__       =    copy.deepcopy(self.__dict__)
           A__       =    self.encoder.to_dict()
           A__       =    self.decoder.to_dict()
           A__       =    self.__class__.model_type
           return output


