





"""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 				lowercase__:
	'''simple docstring'''

	def __init__(						self    :List[str]		, lowerCamelCase_    :Dict		, lowerCamelCase_    :int=13		, lowerCamelCase_    :Optional[int]=7		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :Any=True		, lowerCamelCase_    :Union[str, Any]=True		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :Optional[int]=True		, lowerCamelCase_    :Optional[int]=False		, lowerCamelCase_    :List[Any]=False		, lowerCamelCase_    :Optional[int]=False		, lowerCamelCase_    :List[str]=2		, lowerCamelCase_    :Any=99		, lowerCamelCase_    :Tuple=0		, lowerCamelCase_    :List[Any]=32		, lowerCamelCase_    :Tuple=5		, lowerCamelCase_    :Union[str, Any]=4		, lowerCamelCase_    :Tuple=0.1		, lowerCamelCase_    :Optional[Any]=0.1		, lowerCamelCase_    :Any=5_12		, lowerCamelCase_    :Any=2		, lowerCamelCase_    :str=0.0_2		, lowerCamelCase_    :List[Any]=2		, lowerCamelCase_    :List[str]=4		, lowerCamelCase_    :Tuple="last"		, lowerCamelCase_    :Optional[Any]=True		, lowerCamelCase_    :List[Any]=None		, lowerCamelCase_    :Any=0		, )							->			List[str]:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :int					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= random_attention_mask([self.batch_size, self.seq_length]					)

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

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

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

			SCREAMING_SNAKE_CASE       :     int				= self.get_config()

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





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			int:







			'''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   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :Dict		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :str		, lowerCamelCase_    :Any		, lowerCamelCase_    :str		, )							->			str:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :Any		, lowerCamelCase_    :int		, lowerCamelCase_    :int		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :str		, lowerCamelCase_    :int		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :Any		, lowerCamelCase_    :List[str]		, )							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= XLMWithLMHeadModel(lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model(lowerCamelCase_		, token_type_ids=lowerCamelCase_		, labels=lowerCamelCase_					)
			self.parent.assertEqual(result.loss.shape		, ()					)
			self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.vocab_size)					)





	def   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :Any		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Dict		, lowerCamelCase_    :Any		, lowerCamelCase_    :str		, lowerCamelCase_    :int		, lowerCamelCase_    :List[Any]		, )							->			Optional[Any]:







			'''simple docstring'''




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

			SCREAMING_SNAKE_CASE       :     int				= model(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= model(lowerCamelCase_		, start_positions=lowerCamelCase_		, end_positions=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, 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   __lowerCAmelCase (						self    :Optional[Any]		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :Any		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :str		, lowerCamelCase_    :Optional[int]		, )							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= XLMForQuestionAnswering(lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     str				= model(
			    lowerCamelCase_		, start_positions=lowerCamelCase_		, end_positions=lowerCamelCase_		, cls_index=lowerCamelCase_		, is_impossible=lowerCamelCase_		, p_mask=lowerCamelCase_		, )

			SCREAMING_SNAKE_CASE       :     Tuple				= model(
			    lowerCamelCase_		, start_positions=lowerCamelCase_		, end_positions=lowerCamelCase_		, cls_index=lowerCamelCase_		, is_impossible=lowerCamelCase_		, )

			(SCREAMING_SNAKE_CASE					)       :     List[Any]				= result_with_labels.to_tuple()

			SCREAMING_SNAKE_CASE       :     Dict				= model(lowerCamelCase_		, start_positions=lowerCamelCase_		, end_positions=lowerCamelCase_					)

			(SCREAMING_SNAKE_CASE					)       :     Optional[Any]				= 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   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :int		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :int		, lowerCamelCase_    :Any		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Any		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Union[str, Any]		, )							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= XLMForSequenceClassification(lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()

			SCREAMING_SNAKE_CASE       :     Tuple				= model(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Any				= model(lowerCamelCase_		, labels=lowerCamelCase_					)
			self.parent.assertEqual(result.loss.shape		, ()					)
			self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.type_sequence_label_size)					)





	def   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :int		, lowerCamelCase_    :Any		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Any		, lowerCamelCase_    :Tuple		, )							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= self.num_labels
			SCREAMING_SNAKE_CASE       :     Optional[int]				= XLMForTokenClassification(lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()

			SCREAMING_SNAKE_CASE       :     List[str]				= model(lowerCamelCase_		, attention_mask=lowerCamelCase_		, labels=lowerCamelCase_					)
			self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.num_labels)					)





	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :int		, lowerCamelCase_    :int		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Any		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Optional[Any]		, )							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= self.num_choices
			SCREAMING_SNAKE_CASE       :     List[Any]				= XLMForMultipleChoice(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     Dict				= input_ids.unsqueeze(1					).expand(-1		, self.num_choices		, -1					).contiguous()
			SCREAMING_SNAKE_CASE       :     List[Any]				= token_type_ids.unsqueeze(1					).expand(-1		, self.num_choices		, -1					).contiguous()
			SCREAMING_SNAKE_CASE       :     Tuple				= input_mask.unsqueeze(1					).expand(-1		, self.num_choices		, -1					).contiguous()
			SCREAMING_SNAKE_CASE       :     int				= model(
			    lowerCamelCase_		, attention_mask=lowerCamelCase_		, token_type_ids=lowerCamelCase_		, labels=lowerCamelCase_		, )
			self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_choices)					)







	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			int:







			'''simple docstring'''




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





@require_torch
class 				lowercase__(				_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  (
	    (
	        XLMModel,
	        XLMWithLMHeadModel,
	        XLMForQuestionAnswering,
	        XLMForSequenceClassification,
	        XLMForQuestionAnsweringSimple,
	        XLMForTokenClassification,
	        XLMForMultipleChoice,
	    )
	    if is_torch_available()
	    else ()
	)
	UpperCamelCase				=  (
	    (XLMWithLMHeadModel,) if is_torch_available() else ()
	)  # TODO (PVP): Check other models whether language generation is also applicable
	UpperCamelCase				=  (
	    {
	        """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   __lowerCAmelCase (						self    :Optional[Any]		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :str		, lowerCamelCase_    :int		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Union[str, Any]					)							->			Tuple:







			'''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   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Any		, lowerCamelCase_    :Any=False					)							->			Any:







			'''simple docstring'''




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

			if return_labels:
					if model_class.__name__ == "XLMForQuestionAnswering":
							SCREAMING_SNAKE_CASE       :     int				= torch.zeros(
							    self.model_tester.batch_size		, dtype=torch.long		, device=lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     List[str]				= torch.zeros(
							    self.model_tester.batch_size		, dtype=torch.long		, device=lowerCamelCase_					)

			return inputs_dict





	def   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= XLMModelTester(self					)
			SCREAMING_SNAKE_CASE       :     Dict				= ConfigTester(self		, config_class=lowerCamelCase_		, emb_dim=37					)





	def   __lowerCAmelCase (						self    :List[Any]					)							->			Dict:







			'''simple docstring'''




			self.config_tester.run_common_tests()





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_model(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_lm_head(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Any					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_simple_qa(*lowerCamelCase_					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_qa(*lowerCamelCase_					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_sequence_classif(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :List[str]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_token_classif(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Any					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_for_multiple_choice(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Optional[Any]		, lowerCamelCase_    :Any		, lowerCamelCase_    :Dict		, lowerCamelCase_    :int		, lowerCamelCase_    :int		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :str=False		, lowerCamelCase_    :str=1					)							->			Union[str, Any]:







			'''simple docstring'''




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

			for idx, iter_attentions in enumerate(lowerCamelCase_					):
					# adds PAD dummy token
					SCREAMING_SNAKE_CASE       :     Any				= min_length + idx + 1
					SCREAMING_SNAKE_CASE       :     Any				= min_length + idx + 1

					SCREAMING_SNAKE_CASE       :     int				= (
					    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(lowerCamelCase_					)					)





	def   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Any		, lowerCamelCase_    :int		, lowerCamelCase_    :Optional[int]=False		, lowerCamelCase_    :Dict=1					)							->			Tuple:







			'''simple docstring'''




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

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







	@slow
	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Union[str, Any]:







			'''simple docstring'''




			for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= XLMModel.from_pretrained(lowerCamelCase_					)
					self.assertIsNotNone(lowerCamelCase_					)





@require_torch
class 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	@slow
	def   __lowerCAmelCase (						self    :Dict					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= XLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''					)
			model.to(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     int				= torch.tensor([[14, 4_47]]		, dtype=torch.long		, device=lowerCamelCase_					)  # the president
			SCREAMING_SNAKE_CASE       :     int				= [
			    14,
			    4_47,
			    14,
			    4_47,
			    14,
			    4_47,
			    14,
			    4_47,
			    14,
			    4_47,
			    14,
			    4_47,
			    14,
			    4_47,
			    14,
			    4_47,
			    14,
			    4_47,
			    14,
			    4_47,
			]  # 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
			SCREAMING_SNAKE_CASE       :     Optional[int]				= model.generate(lowerCamelCase_		, do_sample=lowerCamelCase_					)
			self.assertListEqual(output_ids[0].cpu().numpy().tolist()		, lowerCamelCase_					)







"""simple docstring"""






import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowerCamelCase__ :       Optional[Any]						     =			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def __A						(   a_ :							Optional[int]     )->							Dict:




		'''simple docstring'''






		if "token" in model_name_or_path:
				return "rag_token"
		if "sequence" in model_name_or_path:
				return "rag_sequence"
		if "bart" in model_name_or_path:
				return "bart"
		return None





def __A						(   a_ :							List[Any]		,		a_ :							Optional[int]		,		a_ :							Optional[int]     )->							Dict:




		'''simple docstring'''






		return max(metric_fn(a_		,		a_     ) for gt in ground_truths     )





def __A						(   a_ :							List[Any]		,		a_ :							Union[str, Any]		,		a_ :							str     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= []

		if args.gold_data_mode == "qa":
				SCREAMING_SNAKE_CASE       :     List[Any]				= pd.read_csv(a_		,		sep='''\t'''		,		header=a_     )
				for answer_list in data[1]:
						SCREAMING_SNAKE_CASE       :     str				= ast.literal_eval(a_     )
						answers.append(a_     )
		else:
				SCREAMING_SNAKE_CASE       :     Any				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
				SCREAMING_SNAKE_CASE       :     Dict				= [[reference] for reference in references]

		SCREAMING_SNAKE_CASE       :     Dict				= 0
		for prediction, ground_truths in zip(a_		,		a_     ):
				total += 1
				em += metric_max_over_ground_truths(a_		,		a_		,		a_     )
				fa += metric_max_over_ground_truths(a_		,		a_		,		a_     )

		SCREAMING_SNAKE_CASE       :     Any				= 100.0 * em / total
		SCREAMING_SNAKE_CASE       :     Optional[int]				= 100.0 * fa / total

		logger.info(F"F1: {fa:.2f}"     )
		logger.info(F"EM: {em:.2f}"     )





def __A						(   a_ :							Any		,		a_ :							Any		,		a_ :							List[Any]     )->							Tuple:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= args.k
		SCREAMING_SNAKE_CASE       :     Tuple				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]

		SCREAMING_SNAKE_CASE       :     Dict				= 0
		for hypo, reference in zip(a_		,		a_     ):
				SCREAMING_SNAKE_CASE       :     Optional[int]				= set(hypo.split('''\t'''     )[:k]     )
				SCREAMING_SNAKE_CASE       :     List[str]				= set(reference.split('''\t'''     )     )
				total += 1
				em += len(hypo_provenance & ref_provenance     ) / k

		SCREAMING_SNAKE_CASE       :     Dict				= 100.0 * em / total
		logger.info(F"Precision@{k}: {em: .2f}"     )





def __A						(   a_ :							Any		,		a_ :							List[str]		,		a_ :							str     )->							int:




		'''simple docstring'''






		def strip_title(a_ :							Optional[Any]     ):
				if title.startswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Tuple				= title[1:]
				if title.endswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Any				= title[:-1]
				return title

		SCREAMING_SNAKE_CASE       :     Tuple				= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    a_		,		return_tensors='''pt'''		,		padding=a_		,		truncation=a_		,		)['''input_ids'''].to(args.device     )

		SCREAMING_SNAKE_CASE       :     Any				= rag_model.rag.question_encoder(a_     )
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= question_enc_outputs[0]

		SCREAMING_SNAKE_CASE       :     Dict				= rag_model.retriever(
		    a_		,		question_enc_pool_output.cpu().detach().to(torch.floataa     ).numpy()		,		prefix=rag_model.rag.generator.config.prefix		,		n_docs=rag_model.config.n_docs		,		return_tensors='''pt'''		,		)
		SCREAMING_SNAKE_CASE       :     Any				= rag_model.retriever.index.get_doc_dicts(result.doc_ids     )
		SCREAMING_SNAKE_CASE       :     Dict				= []
		for docs in all_docs:
				SCREAMING_SNAKE_CASE       :     List[Any]				= [strip_title(a_     ) for title in docs['''title''']]
				provenance_strings.append('''\t'''.join(a_     )     )
		return provenance_strings





def __A						(   a_ :							List[Any]		,		a_ :							int		,		a_ :							str     )->							Tuple:




		'''simple docstring'''






		with torch.no_grad():
				SCREAMING_SNAKE_CASE       :     Optional[int]				= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    a_		,		return_tensors='''pt'''		,		padding=a_		,		truncation=a_     )

				SCREAMING_SNAKE_CASE       :     Dict				= inputs_dict.input_ids.to(args.device     )
				SCREAMING_SNAKE_CASE       :     Any				= inputs_dict.attention_mask.to(args.device     )
				SCREAMING_SNAKE_CASE       :     Tuple				= rag_model.generate(  # rag_model overwrites generate
				    a_		,		attention_mask=a_		,		num_beams=args.num_beams		,		min_length=args.min_length		,		max_length=args.max_length		,		early_stopping=a_		,		num_return_sequences=1		,		bad_words_ids=[[0, 0]]		,		)
				SCREAMING_SNAKE_CASE       :     Dict				= rag_model.retriever.generator_tokenizer.batch_decode(a_		,		skip_special_tokens=a_     )

				if args.print_predictions:
						for q, a in zip(a_		,		a_     ):
								logger.info('''Q: {} - A: {}'''.format(a_		,		a_     )     )

				return answers





def __A						(   )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= argparse.ArgumentParser()
		parser.add_argument(
		    '''--model_type'''		,		choices=['''rag_sequence''', '''rag_token''', '''bart''']		,		type=a_		,		help=(
		        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
		        ''' model_name_or_path'''
		    )		,		)
		parser.add_argument(
		    '''--index_name'''		,		default=a_		,		choices=['''exact''', '''compressed''', '''legacy''']		,		type=a_		,		help='''RAG model retriever type'''		,		)
		parser.add_argument(
		    '''--index_path'''		,		default=a_		,		type=a_		,		help='''Path to the retrieval index'''		,		)
		parser.add_argument('''--n_docs'''		,		default=5		,		type=a_		,		help='''Number of retrieved docs'''     )
		parser.add_argument(
		    '''--model_name_or_path'''		,		default=a_		,		type=a_		,		required=a_		,		help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''		,		)
		parser.add_argument(
		    '''--eval_mode'''		,		choices=['''e2e''', '''retrieval''']		,		default='''e2e'''		,		type=a_		,		help=(
		        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
		        ''' precision@k.'''
		    )		,		)
		parser.add_argument('''--k'''		,		default=1		,		type=a_		,		help='''k for the precision@k calculation'''     )
		parser.add_argument(
		    '''--evaluation_set'''		,		default=a_		,		type=a_		,		required=a_		,		help='''Path to a file containing evaluation samples'''		,		)
		parser.add_argument(
		    '''--gold_data_path'''		,		default=a_		,		type=a_		,		required=a_		,		help='''Path to a tab-separated file with gold samples'''		,		)
		parser.add_argument(
		    '''--gold_data_mode'''		,		default='''qa'''		,		type=a_		,		choices=['''qa''', '''ans''']		,		help=(
		        '''Format of the gold data file'''
		        '''qa - a single line in the following format: question [tab] answer_list'''
		        '''ans - a single line of the gold file contains the expected answer string'''
		    )		,		)
		parser.add_argument(
		    '''--predictions_path'''		,		type=a_		,		default='''predictions.txt'''		,		help='''Name of the predictions file, to be stored in the checkpoints directory'''		,		)
		parser.add_argument(
		    '''--eval_all_checkpoints'''		,		action='''store_true'''		,		help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''		,		)
		parser.add_argument(
		    '''--eval_batch_size'''		,		default=8		,		type=a_		,		help='''Batch size per GPU/CPU for evaluation.'''		,		)
		parser.add_argument(
		    '''--recalculate'''		,		help='''Recalculate predictions even if the prediction file exists'''		,		action='''store_true'''		,		)
		parser.add_argument(
		    '''--num_beams'''		,		default=4		,		type=a_		,		help='''Number of beams to be used when generating answers'''		,		)
		parser.add_argument('''--min_length'''		,		default=1		,		type=a_		,		help='''Min length of the generated answers'''     )
		parser.add_argument('''--max_length'''		,		default=50		,		type=a_		,		help='''Max length of the generated answers'''     )

		parser.add_argument(
		    '''--print_predictions'''		,		action='''store_true'''		,		help='''If True, prints predictions while evaluating.'''		,		)
		parser.add_argument(
		    '''--print_docs'''		,		action='''store_true'''		,		help='''If True, prints docs retried while generating.'''		,		)
		SCREAMING_SNAKE_CASE       :     List[str]				= parser.parse_args()
		SCREAMING_SNAKE_CASE       :     Dict				= torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''     )
		return args





def __A						(   a_ :							Optional[Any]     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= {}
		if args.model_type is None:
				SCREAMING_SNAKE_CASE       :     List[str]				= infer_model_type(args.model_name_or_path     )
				assert args.model_type is not None
		if args.model_type.startswith('''rag'''     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= args.n_docs
				if args.index_name is not None:
						SCREAMING_SNAKE_CASE       :     Tuple				= args.index_name
				if args.index_path is not None:
						SCREAMING_SNAKE_CASE       :     List[Any]				= args.index_path
		else:
				SCREAMING_SNAKE_CASE       :     str				= BartForConditionalGeneration

		SCREAMING_SNAKE_CASE       :     Optional[int]				= (
		    [f.path for f in os.scandir(args.model_name_or_path     ) if f.is_dir()]
		    if args.eval_all_checkpoints
		    else [args.model_name_or_path]
		)

		logger.info('''Evaluate the following checkpoints: %s'''		,		a_     )

		SCREAMING_SNAKE_CASE       :     int				= get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
		SCREAMING_SNAKE_CASE       :     str				= evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

		for checkpoint in checkpoints:
				if os.path.exists(args.predictions_path     ) and (not args.recalculate):
						logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path     )     )
						score_fn(a_		,		args.predictions_path		,		args.gold_data_path     )
						continue

				logger.info('''***** Running evaluation for {} *****'''.format(a_     )     )
				logger.info('''  Batch size = %d'''		,		args.eval_batch_size     )
				logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path     )     )

				if args.model_type.startswith('''rag'''     ):
						SCREAMING_SNAKE_CASE       :     Dict				= RagRetriever.from_pretrained(a_		,		**a_     )
						SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_class.from_pretrained(a_		,		retriever=a_		,		**a_     )
						model.retriever.init_retrieval()
				else:
						SCREAMING_SNAKE_CASE       :     str				= model_class.from_pretrained(a_		,		**a_     )
				model.to(args.device     )

				with open(args.evaluation_set		,		'''r'''     ) as eval_file, open(args.predictions_path		,		'''w'''     ) as preds_file:
						SCREAMING_SNAKE_CASE       :     Dict				= []
						for line in tqdm(a_     ):
								questions.append(line.strip()     )
								if len(a_     ) == args.eval_batch_size:
										SCREAMING_SNAKE_CASE       :     str				= evaluate_batch_fn(a_		,		a_		,		a_     )
										preds_file.write('''\n'''.join(a_     ) + '''\n'''     )
										preds_file.flush()
										SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
						if len(a_     ) > 0:
								SCREAMING_SNAKE_CASE       :     str				= evaluate_batch_fn(a_		,		a_		,		a_     )
								preds_file.write('''\n'''.join(a_     )     )
								preds_file.flush()

						score_fn(a_		,		args.predictions_path		,		args.gold_data_path     )


if __name__ == "__main__":
					lowerCamelCase__ :       List[str]						     =			get_args()
					main(args)





