





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      lowerCAmelCase							:
 def __init__(   self						:   List[Any]    ,  UpperCAmelCase						:   List[Any]    ,  UpperCAmelCase						:   Optional[Any]=13    ,  UpperCAmelCase						:   Optional[Any]=7    ,  UpperCAmelCase						:   Dict=True    ,  UpperCAmelCase						:   Any=True    ,  UpperCAmelCase						:   List[str]=True    ,  UpperCAmelCase						:   List[str]=True    ,  UpperCAmelCase						:   List[Any]=True    ,  UpperCAmelCase						:   Dict=False    ,  UpperCAmelCase						:   Dict=False    ,  UpperCAmelCase						:   List[Any]=False    ,  UpperCAmelCase						:   Tuple=2    ,  UpperCAmelCase						:   Tuple=99    ,  UpperCAmelCase						:   List[str]=0    ,  UpperCAmelCase						:   Optional[Any]=32    ,  UpperCAmelCase						:   Optional[int]=5    ,  UpperCAmelCase						:   Dict=4    ,  UpperCAmelCase						:   List[Any]=0.1    ,  UpperCAmelCase						:   List[str]=0.1    ,  UpperCAmelCase						:   Tuple=512    ,  UpperCAmelCase						:   Tuple=2    ,  UpperCAmelCase						:   List[str]=0.0_2    ,  UpperCAmelCase						:   Optional[Any]=2    ,  UpperCAmelCase						:   Union[str, Any]=4    ,  UpperCAmelCase						:   Any="last"    ,  UpperCAmelCase						:   Optional[Any]=True    ,  UpperCAmelCase						:   Dict=None    ,  UpperCAmelCase						:   Optional[int]=0    ,  )    ->			List[Any]:
     lowerCamelCase__					:   List[str]      				=	parent
     lowerCamelCase__					:   Optional[Any]      				=	batch_size
     lowerCamelCase__					:   int      				=	seq_length
     lowerCamelCase__					:   Union[str, Any]      				=	is_training
     lowerCamelCase__					:   Dict      				=	use_input_lengths
     lowerCamelCase__					:   str      				=	use_token_type_ids
     lowerCamelCase__					:   str      				=	use_labels
     lowerCamelCase__					:   int      				=	gelu_activation
     lowerCamelCase__					:   Optional[Any]      				=	sinusoidal_embeddings
     lowerCamelCase__					:   Optional[Any]      				=	causal
     lowerCamelCase__					:   Any      				=	asm
     lowerCamelCase__					:   Any      				=	n_langs
     lowerCamelCase__					:   Tuple      				=	vocab_size
     lowerCamelCase__					:   int      				=	n_special
     lowerCamelCase__					:   str      				=	hidden_size
     lowerCamelCase__					:   List[Any]      				=	num_hidden_layers
     lowerCamelCase__					:   List[Any]      				=	num_attention_heads
     lowerCamelCase__					:   str      				=	hidden_dropout_prob
     lowerCamelCase__					:   str      				=	attention_probs_dropout_prob
     lowerCamelCase__					:   Union[str, Any]      				=	max_position_embeddings
     lowerCamelCase__					:   List[Any]      				=	type_sequence_label_size
     lowerCamelCase__					:   int      				=	initializer_range
     lowerCamelCase__					:   List[str]      				=	num_labels
     lowerCamelCase__					:   List[Any]      				=	num_choices
     lowerCamelCase__					:   Optional[Any]      				=	summary_type
     lowerCamelCase__					:   Optional[Any]      				=	use_proj
     lowerCamelCase__					:   Dict      				=	scope
     lowerCamelCase__					:   List[str]      				=	bos_token_id
 def   A_			(   self						:   List[str]			)    ->			List[Any]:
     lowerCamelCase__					:   Optional[Any]      				=	ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size			)
     lowerCamelCase__					:   Any      				=	random_attention_mask([self.batch_size, self.seq_length]			)

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

     lowerCamelCase__					:   str      				=	None
     if self.use_token_type_ids:
         lowerCamelCase__					:   Optional[int]      				=	ids_tensor([self.batch_size, self.seq_length]    ,  self.n_langs			)

     lowerCamelCase__					:   Union[str, Any]      				=	None
     lowerCamelCase__					:   Union[str, Any]      				=	None
     lowerCamelCase__					:   Union[str, Any]      				=	None
     if self.use_labels:
         lowerCamelCase__					:   Any      				=	ids_tensor([self.batch_size]    ,  self.type_sequence_label_size			)
         lowerCamelCase__					:   Dict      				=	ids_tensor([self.batch_size, self.seq_length]    ,  self.num_labels			)
         lowerCamelCase__					:   int      				=	ids_tensor([self.batch_size]    ,  2			).float()
         lowerCamelCase__					:   Any      				=	ids_tensor([self.batch_size]    ,  self.num_choices			)

     lowerCamelCase__					:   Union[str, Any]      				=	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						:   str			)    ->			List[str]:
     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						:   Any    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   List[Any]    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   List[Any]    ,  )    ->			Union[str, Any]:
     lowerCamelCase__					:   str      				=	XLMModel(config=UpperCAmelCase			)
     model.to(UpperCAmelCase			)
     model.eval()
     lowerCamelCase__					:   Optional[int]      				=	model(UpperCAmelCase    ,  lengths=UpperCAmelCase    ,  langs=UpperCAmelCase			)
     lowerCamelCase__					:   List[str]      				=	model(UpperCAmelCase    ,  langs=UpperCAmelCase			)
     lowerCamelCase__					:   str      				=	model(UpperCAmelCase			)
     self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size)			)
 def   A_			(   self						:   Optional[Any]    ,  UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   List[Any]    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   List[Any]    ,  )    ->			str:
     lowerCamelCase__					:   int      				=	XLMWithLMHeadModel(UpperCAmelCase			)
     model.to(UpperCAmelCase			)
     model.eval()

     lowerCamelCase__					:   List[str]      				=	model(UpperCAmelCase    ,  token_type_ids=UpperCAmelCase    ,  labels=UpperCAmelCase			)
     self.parent.assertEqual(result.loss.shape    ,  ()			)
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.vocab_size)			)
 def   A_			(   self						:   List[str]    ,  UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   str    ,  )    ->			List[Any]:
     lowerCamelCase__					:   List[str]      				=	XLMForQuestionAnsweringSimple(UpperCAmelCase			)
     model.to(UpperCAmelCase			)
     model.eval()

     lowerCamelCase__					:   Tuple      				=	model(UpperCAmelCase			)

     lowerCamelCase__					:   Tuple      				=	model(UpperCAmelCase    ,  start_positions=UpperCAmelCase    ,  end_positions=UpperCAmelCase			)
     lowerCamelCase__					:   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   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   List[Any]    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   int    ,  )    ->			List[str]:
     lowerCamelCase__					:   Tuple      				=	XLMForQuestionAnswering(UpperCAmelCase			)
     model.to(UpperCAmelCase			)
     model.eval()

     lowerCamelCase__					:   List[str]      				=	model(UpperCAmelCase			)

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

     lowerCamelCase__					:   Dict      				=	model(
         UpperCAmelCase    ,  start_positions=UpperCAmelCase    ,  end_positions=UpperCAmelCase    ,  cls_index=UpperCAmelCase    ,  is_impossible=UpperCAmelCase    ,  )

     (lowerCamelCase__							)					:   str      				=	result_with_labels.to_tuple()

     lowerCamelCase__					:   List[str]      				=	model(UpperCAmelCase    ,  start_positions=UpperCAmelCase    ,  end_positions=UpperCAmelCase			)

     (lowerCamelCase__							)					:   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   A_			(   self						:   Union[str, Any]    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   int    ,  )    ->			int:
     lowerCamelCase__					:   Union[str, Any]      				=	XLMForSequenceClassification(UpperCAmelCase			)
     model.to(UpperCAmelCase			)
     model.eval()

     lowerCamelCase__					:   Any      				=	model(UpperCAmelCase			)
     lowerCamelCase__					:   Dict      				=	model(UpperCAmelCase    ,  labels=UpperCAmelCase			)
     self.parent.assertEqual(result.loss.shape    ,  ()			)
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.type_sequence_label_size)			)
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   List[Any]    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   str    ,  )    ->			Optional[Any]:
     lowerCamelCase__					:   List[Any]      				=	self.num_labels
     lowerCamelCase__					:   Tuple      				=	XLMForTokenClassification(UpperCAmelCase			)
     model.to(UpperCAmelCase			)
     model.eval()

     lowerCamelCase__					:   List[Any]      				=	model(UpperCAmelCase    ,  attention_mask=UpperCAmelCase    ,  labels=UpperCAmelCase			)
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.num_labels)			)
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   Dict    ,  )    ->			List[Any]:
     lowerCamelCase__					:   Optional[int]      				=	self.num_choices
     lowerCamelCase__					:   int      				=	XLMForMultipleChoice(config=UpperCAmelCase			)
     model.to(UpperCAmelCase			)
     model.eval()
     lowerCamelCase__					:   List[str]      				=	input_ids.unsqueeze(1			).expand(-1    ,  self.num_choices    ,  -1			).contiguous()
     lowerCamelCase__					:   List[str]      				=	token_type_ids.unsqueeze(1			).expand(-1    ,  self.num_choices    ,  -1			).contiguous()
     lowerCamelCase__					:   Tuple      				=	input_mask.unsqueeze(1			).expand(-1    ,  self.num_choices    ,  -1			).contiguous()
     lowerCamelCase__					:   List[str]      				=	model(
         UpperCAmelCase    ,  attention_mask=UpperCAmelCase    ,  token_type_ids=UpperCAmelCase    ,  labels=UpperCAmelCase    ,  )
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_choices)			)







 def   A_			(   self						:   Optional[Any]			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Dict      				=	self.prepare_config_and_inputs()
     (
         lowerCamelCase__							
     )					:   List[str]      				=	config_and_inputs
     lowerCamelCase__					:   int      				=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
     return config, inputs_dict


@require_torch
class      lowerCAmelCase							(      __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						unittest.TestCase    ):
 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   A_			(   self						:   Dict    ,  UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Tuple			)    ->			Optional[int]:
     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						:   Tuple    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Optional[int]=False			)    ->			Tuple:
     lowerCamelCase__					:   List[str]      				=	super()._prepare_for_class(UpperCAmelCase    ,  UpperCAmelCase    ,  return_labels=UpperCAmelCase			)

     if return_labels:
         if model_class.__name__ == "XLMForQuestionAnswering":
             lowerCamelCase__					:   int      				=	torch.zeros(
                 self.model_tester.batch_size    ,  dtype=torch.long    ,  device=UpperCAmelCase			)
             lowerCamelCase__					:   Tuple      				=	torch.zeros(
                 self.model_tester.batch_size    ,  dtype=torch.long    ,  device=UpperCAmelCase			)

     return inputs_dict
 def   A_			(   self						:   str			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	XLMModelTester(self			)
     lowerCamelCase__					:   Any      				=	ConfigTester(self    ,  config_class=UpperCAmelCase    ,  emb_dim=37			)
 def   A_			(   self						:   Optional[int]			)    ->			List[Any]:
     self.config_tester.run_common_tests()
 def   A_			(   self						:   Any			)    ->			List[Any]:
     lowerCamelCase__					:   Any      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_model(*UpperCAmelCase			)
 def   A_			(   self						:   str			)    ->			Any:
     lowerCamelCase__					:   int      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_lm_head(*UpperCAmelCase			)
 def   A_			(   self						:   Dict			)    ->			Optional[Any]:
     lowerCamelCase__					:   str      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_simple_qa(*UpperCAmelCase			)
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[Any]      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_qa(*UpperCAmelCase			)
 def   A_			(   self						:   Any			)    ->			Optional[int]:
     lowerCamelCase__					:   List[str]      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_sequence_classif(*UpperCAmelCase			)
 def   A_			(   self						:   Any			)    ->			str:
     lowerCamelCase__					:   List[Any]      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_token_classif(*UpperCAmelCase			)
 def   A_			(   self						:   Any			)    ->			Optional[int]:
     lowerCamelCase__					:   List[Any]      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_for_multiple_choice(*UpperCAmelCase			)
 def   A_			(   self						:   Optional[int]    ,  UpperCAmelCase						:   List[Any]    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   List[Any]=False    ,  UpperCAmelCase						:   str=1			)    ->			Union[str, Any]:
     self.assertIsInstance(UpperCAmelCase    ,  UpperCAmelCase			)
     self.assertListEqual(
         [isinstance(UpperCAmelCase    ,  UpperCAmelCase			) for iter_attentions in attentions]    ,  [True] * len(UpperCAmelCase			)			)
     self.assertEqual(len(UpperCAmelCase			)    ,  (max_length - min_length) * num_beam_groups			)

     for idx, iter_attentions in enumerate(UpperCAmelCase			):
         # adds PAD dummy token
         lowerCamelCase__					:   Any      				=	min_length + idx + 1
         lowerCamelCase__					:   Union[str, Any]      				=	min_length + idx + 1

         lowerCamelCase__					:   Optional[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(UpperCAmelCase			)			)
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   List[str]=False    ,  UpperCAmelCase						:   Tuple=1			)    ->			Union[str, Any]:
     self.assertIsInstance(UpperCAmelCase    ,  UpperCAmelCase			)
     self.assertListEqual(
         [isinstance(UpperCAmelCase    ,  UpperCAmelCase			) for iter_hidden_states in hidden_states]    ,  [True] * len(UpperCAmelCase			)    ,  )
     self.assertEqual(len(UpperCAmelCase			)    ,  (max_length - min_length) * num_beam_groups			)

     for idx, iter_hidden_states in enumerate(UpperCAmelCase			):
         # adds PAD dummy token
         lowerCamelCase__					:   str      				=	min_length + idx + 1
         lowerCamelCase__					:   Any      				=	(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(UpperCAmelCase			)    ,  )
     pass







 @slow
 def   A_			(   self						:   Optional[Any]			)    ->			Union[str, Any]:
     for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCamelCase__					:   Dict      				=	XLMModel.from_pretrained(UpperCAmelCase			)
         self.assertIsNotNone(UpperCAmelCase			)


@require_torch
class      lowerCAmelCase							(      unittest.TestCase    ):
 @slow
 def   A_			(   self						:   Any			)    ->			List[Any]:
     lowerCamelCase__					:   Dict      				=	XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'			)
     model.to(UpperCAmelCase			)
     lowerCamelCase__					:   int      				=	torch.tensor([[14, 447]]    ,  dtype=torch.long    ,  device=UpperCAmelCase			)  # the president
     lowerCamelCase__					:   Tuple      				=	[
         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
     lowerCamelCase__					:   List[Any]      				=	model.generate(UpperCAmelCase    ,  do_sample=UpperCAmelCase			)
     self.assertListEqual(output_ids[0].cpu().numpy().tolist()    ,  UpperCAmelCase			)












import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


_UpperCAmelCase       : int			     =       Mapping[str, np.ndarray]
_UpperCAmelCase       : List[Any]			     =       Mapping[str, Any]  # Is a nested dict.
_UpperCAmelCase       : Dict			     =       0.01

@dataclasses.dataclass(frozen=__UpperCamelCase    )
class      lowerCAmelCase							:
 UpperCAmelCase__									=	42  # [num_res, num_atom_type, 3]

 # Amino-acid type for each residue represented as an integer between 0 and
 # 20, where 20 is 'X'.
 UpperCAmelCase__									=	42  # [num_res]

 # Binary float mask to indicate presence of a particular atom. 1.0 if an atom
 # is present and 0.0 if not. This should be used for loss masking.
 UpperCAmelCase__									=	42  # [num_res, num_atom_type]

 # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
 UpperCAmelCase__									=	42  # [num_res]

 # B-factors, or temperature factors, of each residue (in sq. angstroms units),
 # representing the displacement of the residue from its ground truth mean
 # value.
 UpperCAmelCase__									=	42  # [num_res, num_atom_type]

 # Chain indices for multi-chain predictions
 UpperCAmelCase__									=	None

 # Optional remark about the protein. Included as a comment in output PDB
 # files
 UpperCAmelCase__									=	None

 # Templates used to generate this protein (prediction-only)
 UpperCAmelCase__									=	None

 # Chain corresponding to each parent
 UpperCAmelCase__									=	None


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Protein:
    lowerCamelCase__					:   Optional[int]      				=	r'(\[[A-Z]+\]\n)'
    lowerCamelCase__					:   List[str]      				=	[tag.strip() for tag in re.split(_UpperCAmelCase		,			_UpperCAmelCase				) if len(_UpperCAmelCase				) > 0]
    lowerCamelCase__					:   Iterator[Tuple[str, List[str]]]      				=	zip(tags[0::2]		,			[l.split('\n'				) for l in tags[1::2]]				)

    lowerCamelCase__					:   List[str]      				=	["N", "CA", "C"]
    lowerCamelCase__					:   Dict      				=	None
    lowerCamelCase__					:   str      				=	None
    lowerCamelCase__					:   int      				=	None
    for g in groups:
        if "[PRIMARY]" == g[0]:
            lowerCamelCase__					:   int      				=	g[1][0].strip()
            for i in range(len(_UpperCAmelCase				)				):
                if seq[i] not in residue_constants.restypes:
                    lowerCamelCase__					:   Union[str, Any]      				=	'X'  # FIXME: strings are immutable
            lowerCamelCase__					:   Union[str, Any]      				=	np.array(
                [residue_constants.restype_order.get(_UpperCAmelCase		,			residue_constants.restype_num				) for res_symbol in seq]				)
        elif "[TERTIARY]" == g[0]:
            lowerCamelCase__					:   List[List[float]]      				=	[]
            for axis in range(3				):
                tertiary.append(list(map(_UpperCAmelCase		,			g[1][axis].split()				)				)				)
            lowerCamelCase__					:   int      				=	np.array(_UpperCAmelCase				)
            lowerCamelCase__					:   Tuple      				=	np.zeros((len(tertiary[0]				) // 3, residue_constants.atom_type_num, 3)				).astype(np.floataa				)
            for i, atom in enumerate(_UpperCAmelCase				):
                lowerCamelCase__					:   int      				=	np.transpose(tertiary_np[:, i::3]				)
            atom_positions *= PICO_TO_ANGSTROM
        elif "[MASK]" == g[0]:
            lowerCamelCase__					:   int      				=	np.array(list(map({'-': 0, '+': 1}.get		,			g[1][0].strip()				)				)				)
            lowerCamelCase__					:   List[Any]      				=	np.zeros(
                (
                    len(_UpperCAmelCase				),
                    residue_constants.atom_type_num,
                )				).astype(np.floataa				)
            for i, atom in enumerate(_UpperCAmelCase				):
                lowerCamelCase__					:   Union[str, Any]      				=	1
            atom_mask *= mask[..., None]

    assert aatype is not None

    return Protein(
        atom_positions=_UpperCAmelCase		,			atom_mask=_UpperCAmelCase		,			aatype=_UpperCAmelCase		,			residue_index=np.arange(len(_UpperCAmelCase				)				)		,			b_factors=_UpperCAmelCase		,			)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase = 0				)    ->  List[str]:
    lowerCamelCase__					:   List[str]      				=	[]

    lowerCamelCase__					:   Dict      				=	prot.remark
    if remark is not None:
        pdb_headers.append(F"""REMARK {remark}"""				)

    lowerCamelCase__					:   str      				=	prot.parents
    lowerCamelCase__					:   Union[str, Any]      				=	prot.parents_chain_index
    if parents is not None and parents_chain_index is not None:
        lowerCamelCase__					:   Any      				=	[p for i, p in zip(_UpperCAmelCase		,			_UpperCAmelCase				) if i == chain_id]

    if parents is None or len(_UpperCAmelCase				) == 0:
        lowerCamelCase__					:   List[Any]      				=	['N/A']

    pdb_headers.append(F"""PARENT {" ".join(_UpperCAmelCase				)}"""				)

    return pdb_headers


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  str:
    lowerCamelCase__					:   List[str]      				=	[]
    lowerCamelCase__					:   str      				=	pdb_str.split('\n'				)

    lowerCamelCase__					:   int      				=	prot.remark
    if remark is not None:
        out_pdb_lines.append(F"""REMARK {remark}"""				)

    lowerCamelCase__					:   List[List[str]]
    if prot.parents is not None and len(prot.parents				) > 0:
        lowerCamelCase__					:   List[Any]      				=	[]
        if prot.parents_chain_index is not None:
            lowerCamelCase__					:   Dict[str, List[str]]      				=	{}
            for p, i in zip(prot.parents		,			prot.parents_chain_index				):
                parent_dict.setdefault(str(_UpperCAmelCase				)		,			[]				)
                parent_dict[str(_UpperCAmelCase				)].append(_UpperCAmelCase				)

            lowerCamelCase__					:   str      				=	max([int(_UpperCAmelCase				) for chain_idx in parent_dict]				)
            for i in range(max_idx + 1				):
                lowerCamelCase__					:   Optional[Any]      				=	parent_dict.get(str(_UpperCAmelCase				)		,			['N/A']				)
                parents_per_chain.append(_UpperCAmelCase				)
        else:
            parents_per_chain.append(list(prot.parents				)				)
    else:
        lowerCamelCase__					:   Union[str, Any]      				=	[['N/A']]

    def make_parent_line(_UpperCAmelCase				) -> str:
        return F"""PARENT {" ".join(_UpperCAmelCase				)}"""

    out_pdb_lines.append(make_parent_line(parents_per_chain[0]				)				)

    lowerCamelCase__					:   List[Any]      				=	0
    for i, l in enumerate(_UpperCAmelCase				):
        if "PARENT" not in l and "REMARK" not in l:
            out_pdb_lines.append(_UpperCAmelCase				)
        if "TER" in l and "END" not in lines[i + 1]:
            chain_counter += 1
            if not chain_counter >= len(_UpperCAmelCase				):
                lowerCamelCase__					:   Union[str, Any]      				=	parents_per_chain[chain_counter]
            else:
                lowerCamelCase__					:   Optional[Any]      				=	['N/A']

            out_pdb_lines.append(make_parent_line(_UpperCAmelCase				)				)

    return "\n".join(_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  str:
    lowerCamelCase__					:   Tuple      				=	residue_constants.restypes + ['X']

    def res_atoa(_UpperCAmelCase				) -> str:
        return residue_constants.restype_atoa.get(restypes[r]		,			'UNK'				)

    lowerCamelCase__					:   int      				=	residue_constants.atom_types

    lowerCamelCase__					:   List[str]      				=	[]

    lowerCamelCase__					:   Union[str, Any]      				=	prot.atom_mask
    lowerCamelCase__					:   Union[str, Any]      				=	prot.aatype
    lowerCamelCase__					:   int      				=	prot.atom_positions
    lowerCamelCase__					:   List[Any]      				=	prot.residue_index.astype(np.intaa				)
    lowerCamelCase__					:   Optional[int]      				=	prot.b_factors
    lowerCamelCase__					:   Any      				=	prot.chain_index

    if np.any(aatype > residue_constants.restype_num				):
        raise ValueError('Invalid aatypes.'				)

    lowerCamelCase__					:   List[Any]      				=	get_pdb_headers(_UpperCAmelCase				)
    if len(_UpperCAmelCase				) > 0:
        pdb_lines.extend(_UpperCAmelCase				)

    lowerCamelCase__					:   Union[str, Any]      				=	aatype.shape[0]
    lowerCamelCase__					:   Optional[Any]      				=	1
    lowerCamelCase__					:   str      				=	0
    lowerCamelCase__					:   Tuple      				=	string.ascii_uppercase
    lowerCamelCase__					:   str      				=	None
    # Add all atom sites.
    for i in range(_UpperCAmelCase				):
        lowerCamelCase__					:   List[Any]      				=	res_atoa(aatype[i]				)
        for atom_name, pos, mask, b_factor in zip(_UpperCAmelCase		,			atom_positions[i]		,			atom_mask[i]		,			b_factors[i]				):
            if mask < 0.5:
                continue

            lowerCamelCase__					:   Union[str, Any]      				=	'ATOM'
            lowerCamelCase__					:   Optional[int]      				=	atom_name if len(_UpperCAmelCase				) == 4 else F""" {atom_name}"""
            lowerCamelCase__					:   Any      				=	''
            lowerCamelCase__					:   Optional[Any]      				=	''
            lowerCamelCase__					:   str      				=	1.00
            lowerCamelCase__					:   str      				=	atom_name[0]  # Protein supports only C, N, O, S, this works.
            lowerCamelCase__					:   List[str]      				=	''

            lowerCamelCase__					:   str      				=	'A'
            if chain_index is not None:
                lowerCamelCase__					:   List[Any]      				=	chain_tags[chain_index[i]]

            # PDB is a columnar format, every space matters here!
            lowerCamelCase__					:   Union[str, Any]      				=	(
                F"""{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"""
                F"""{res_name_a:>3} {chain_tag:>1}"""
                F"""{residue_index[i]:>4}{insertion_code:>1}   """
                F"""{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"""
                F"""{occupancy:>6.2f}{b_factor:>6.2f}          """
                F"""{element:>2}{charge:>2}"""
            )
            pdb_lines.append(_UpperCAmelCase				)
            atom_index += 1

        lowerCamelCase__					:   Dict      				=	i == n - 1
        if chain_index is not None:
            if i != n - 1 and chain_index[i + 1] != prev_chain_index:
                lowerCamelCase__					:   List[Any]      				=	True
                lowerCamelCase__					:   List[str]      				=	chain_index[i + 1]

        if should_terminate:
            # Close the chain.
            lowerCamelCase__					:   int      				=	'TER'
            lowerCamelCase__					:   Any      				=	(
                F"""{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]				):>3} {chain_tag:>1}{residue_index[i]:>4}"""
            )
            pdb_lines.append(_UpperCAmelCase				)
            atom_index += 1

            if i != n - 1:
                # "prev" is a misnomer here. This happens at the beginning of
                # each new chain.
                pdb_lines.extend(get_pdb_headers(_UpperCAmelCase		,			_UpperCAmelCase				)				)

    pdb_lines.append('END'				)
    pdb_lines.append(''				)
    return "\n".join(_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  np.ndarray:
    return residue_constants.STANDARD_ATOM_MASK[prot.aatype]


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase = None		,			_UpperCAmelCase = None		,			_UpperCAmelCase = None		,			_UpperCAmelCase = None		,			_UpperCAmelCase = None		,			)    ->  Protein:
    return Protein(
        aatype=features['aatype']		,			atom_positions=result['final_atom_positions']		,			atom_mask=result['final_atom_mask']		,			residue_index=features['residue_index'] + 1		,			b_factors=b_factors if b_factors is not None else np.zeros_like(result['final_atom_mask']				)		,			chain_index=_UpperCAmelCase		,			remark=_UpperCAmelCase		,			parents=_UpperCAmelCase		,			parents_chain_index=_UpperCAmelCase		,			)





