




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						:

      """simple docstring"""

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







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size				)
          __UpperCamelCase  		=						random_attention_mask([self.batch_size, self.seq_length]				)

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

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

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

          __UpperCamelCase  		=						self.get_config()

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







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







      def       snake_case	(     self					: List[Any] ,       snake_case					: Optional[int] ,       snake_case					: Optional[Any] ,       snake_case					: List[Any] ,       snake_case					: int ,       snake_case					: Union[str, Any] ,       snake_case					: List[str] ,       snake_case					: int ,       snake_case					: Tuple ,       snake_case					: str ,       ):
          __UpperCamelCase  		=						XLMWithLMHeadModel(snake_case				)
          model.to(snake_case				)
          model.eval()

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







      def       snake_case	(     self					: Any ,       snake_case					: List[Any] ,       snake_case					: Any ,       snake_case					: Tuple ,       snake_case					: Any ,       snake_case					: Optional[int] ,       snake_case					: Tuple ,       snake_case					: int ,       snake_case					: str ,       snake_case					: Union[str, Any] ,       ):
          __UpperCamelCase  		=						XLMForQuestionAnsweringSimple(snake_case				)
          model.to(snake_case				)
          model.eval()

          __UpperCamelCase  		=						model(snake_case				)

          __UpperCamelCase  		=						model(snake_case ,       start_positions=snake_case ,       end_positions=snake_case				)
          __UpperCamelCase  		=						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       snake_case	(     self					: int ,       snake_case					: Tuple ,       snake_case					: Any ,       snake_case					: List[Any] ,       snake_case					: Union[str, Any] ,       snake_case					: Optional[int] ,       snake_case					: Union[str, Any] ,       snake_case					: str ,       snake_case					: Tuple ,       snake_case					: Dict ,       ):
          __UpperCamelCase  		=						XLMForQuestionAnswering(snake_case				)
          model.to(snake_case				)
          model.eval()

          __UpperCamelCase  		=						model(snake_case				)

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

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

          ((__UpperCamelCase      )		,            )  		=						result_with_labels.to_tuple()

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

          ((__UpperCamelCase      )		,            )  		=						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       snake_case	(     self					: Any ,       snake_case					: List[Any] ,       snake_case					: int ,       snake_case					: List[Any] ,       snake_case					: Union[str, Any] ,       snake_case					: List[Any] ,       snake_case					: Optional[Any] ,       snake_case					: Dict ,       snake_case					: Tuple ,       snake_case					: int ,       ):
          __UpperCamelCase  		=						XLMForSequenceClassification(snake_case				)
          model.to(snake_case				)
          model.eval()

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







      def       snake_case	(     self					: Optional[int] ,       snake_case					: Tuple ,       snake_case					: int ,       snake_case					: List[Any] ,       snake_case					: Optional[int] ,       snake_case					: Optional[int] ,       snake_case					: Optional[int] ,       snake_case					: Any ,       snake_case					: Dict ,       snake_case					: int ,       ):
          __UpperCamelCase  		=						self.num_labels
          __UpperCamelCase  		=						XLMForTokenClassification(snake_case				)
          model.to(snake_case				)
          model.eval()

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







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







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						self.prepare_config_and_inputs()
          (
              (
              __UpperCamelCase      
          )		,      (
              __UpperCamelCase      
          )		,      (
              __UpperCamelCase      
          )		,      (
              __UpperCamelCase      
          )		,      (
              __UpperCamelCase      
          )		,      (
              __UpperCamelCase      
          )		,      (
              __UpperCamelCase      
          )		,      (
              __UpperCamelCase      
          )		,      (
              __UpperCamelCase      
          )		,            
          )  		=						config_and_inputs
          __UpperCamelCase  		=						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''lengths''': input_lengths}
          return config, inputs_dict







@require_torch
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[Any]    =					(
          (
              XLMModel,
              XLMWithLMHeadModel,
              XLMForQuestionAnswering,
              XLMForSequenceClassification,
              XLMForQuestionAnsweringSimple,
              XLMForTokenClassification,
              XLMForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__			: List[str]    =					(
          (XLMWithLMHeadModel,) if is_torch_available() else ()
      )  # TODO (PVP): Check other models whether language generation is also applicable
      lowerCAmelCase__			: Tuple    =					(
          {
              "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       snake_case	(     self					: Dict ,       snake_case					: List[str] ,       snake_case					: str ,       snake_case					: Tuple ,       snake_case					: List[Any] ,       snake_case					: Any				):
          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       snake_case	(     self					: Dict ,       snake_case					: List[str] ,       snake_case					: Tuple ,       snake_case					: List[str]=False				):
          __UpperCamelCase  		=						super()._prepare_for_class(snake_case ,       snake_case ,       return_labels=snake_case				)

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

          return inputs_dict







      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						XLMModelTester(self				)
          __UpperCamelCase  		=						ConfigTester(self ,       config_class=snake_case ,       emb_dim=37				)







      def       snake_case	(     self					: Optional[int]				):
          self.config_tester.run_common_tests()







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_model(*snake_case				)







      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_lm_head(*snake_case				)







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_simple_qa(*snake_case				)







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_qa(*snake_case				)







      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_sequence_classif(*snake_case				)







      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_token_classif(*snake_case				)







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case				)







      def       snake_case	(     self					: str ,       snake_case					: List[str] ,       snake_case					: Dict ,       snake_case					: Union[str, Any] ,       snake_case					: int ,       snake_case					: List[Any] ,       snake_case					: Any=False ,       snake_case					: Optional[Any]=1				):
          self.assertIsInstance(snake_case ,       snake_case				)
          self.assertListEqual(
              [isinstance(snake_case ,       snake_case				) for iter_attentions in attentions] ,       [True] * len(snake_case				)				)
          self.assertEqual(len(snake_case				) ,       (max_length - min_length) * num_beam_groups				)

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

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







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

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







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







@require_torch
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      @slow
      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						XLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''				)
          model.to(snake_case				)
          __UpperCamelCase  		=						torch.tensor([[14, 447]] ,       dtype=torch.long ,       device=snake_case				)  # the president
          __UpperCamelCase  		=						[
              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
          __UpperCamelCase  		=						model.generate(snake_case ,       do_sample=snake_case				)
          self.assertListEqual(output_ids[0].cpu().numpy().tolist() ,       snake_case				)












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


a_   		=     logging.get_logger(__name__)

a_   		=     {
    "RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
    "RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
    "RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
    "RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
    "RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
    "RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Union[str, Any]    =					"rwkv"
      lowerCAmelCase__			: Union[str, Any]    =					{"max_position_embeddings": "context_length"}







      def __init__(     self					: Optional[int] ,       snake_case					: Optional[Any]=50277 ,       snake_case					: str=1024 ,       snake_case					: str=4096 ,       snake_case					: Optional[Any]=32 ,       snake_case					: Union[str, Any]=None ,       snake_case					: Optional[Any]=None ,       snake_case					: Optional[Any]=1E-5 ,       snake_case					: List[str]=0 ,       snake_case					: Optional[Any]=0 ,       snake_case					: Union[str, Any]=6 ,       snake_case					: Tuple=False ,       snake_case					: Any=True ,       **snake_case					: List[str] ,       ):
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						context_length
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						attention_hidden_size if attention_hidden_size is not None else hidden_size
          __UpperCamelCase  		=						intermediate_size if intermediate_size is not None else 4 * hidden_size
          __UpperCamelCase  		=						layer_norm_epsilon
          __UpperCamelCase  		=						rescale_every
          __UpperCamelCase  		=						use_cache

          __UpperCamelCase  		=						bos_token_id
          __UpperCamelCase  		=						eos_token_id

          super().__init__(
              tie_word_embeddings=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case ,       **snake_case				)






