





from scipy.stats import pearsonr

import datasets


_UpperCAmelCase        =				'\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n'


_UpperCAmelCase        =				'\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


_UpperCAmelCase        =				'\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,						_KWARGS_DESCRIPTION     )
class 							_UpperCamelCase					(	datasets.Metric     ):

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




              """simple docstring"""


              return datasets.MetricInfo(
                  description=_DESCRIPTION					,   citation=_CITATION					,   inputs_description=_KWARGS_DESCRIPTION					,   features=datasets.Features(
                      {
                          "predictions": datasets.Value("float"		),
                          "references": datasets.Value("float"		),
                      }		)					,   reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"]					,   )



       def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]=False		)       -> int:




              """simple docstring"""


              if return_pvalue:
                     UpperCamelCase_          =  pearsonr(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
                     return {"pearsonr": results[0], "p-value": results[1]}
              else:
                     return {"pearsonr": float(pearsonr(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)[0]		)}








import unittest

from transformers import AutoTokenizer, FalconConfig, 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 (
         FalconForCausalLM,
         FalconForQuestionAnswering,
         FalconForSequenceClassification,
         FalconForTokenClassification,
         FalconModel,
     )



class 							_UpperCamelCase					:

       def __init__(		self: Dict					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]=3					,   _SCREAMING_SNAKE_CASE: Any=7					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=True					,   _SCREAMING_SNAKE_CASE: Optional[int]=True					,   _SCREAMING_SNAKE_CASE: List[Any]=False					,   _SCREAMING_SNAKE_CASE: str=True					,   _SCREAMING_SNAKE_CASE: Any=99					,   _SCREAMING_SNAKE_CASE: List[str]=32					,   _SCREAMING_SNAKE_CASE: List[str]=5					,   _SCREAMING_SNAKE_CASE: Optional[int]=4					,   _SCREAMING_SNAKE_CASE: str=37					,   _SCREAMING_SNAKE_CASE: Optional[int]="gelu"					,   _SCREAMING_SNAKE_CASE: str=0.1					,   _SCREAMING_SNAKE_CASE: Any=0.1					,   _SCREAMING_SNAKE_CASE: Optional[Any]=512					,   _SCREAMING_SNAKE_CASE: Optional[Any]=16					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=2					,   _SCREAMING_SNAKE_CASE: Tuple=0.02					,   _SCREAMING_SNAKE_CASE: Optional[Any]=3					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=4					,   _SCREAMING_SNAKE_CASE: Tuple=None					,   )       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  parent
              UpperCamelCase_          =  batch_size
              UpperCamelCase_          =  seq_length
              UpperCamelCase_          =  is_training
              UpperCamelCase_          =  use_input_mask
              UpperCamelCase_          =  use_token_type_ids
              UpperCamelCase_          =  use_labels
              UpperCamelCase_          =  vocab_size
              UpperCamelCase_          =  hidden_size
              UpperCamelCase_          =  num_hidden_layers
              UpperCamelCase_          =  num_attention_heads
              UpperCamelCase_          =  intermediate_size
              UpperCamelCase_          =  hidden_act
              UpperCamelCase_          =  hidden_dropout_prob
              UpperCamelCase_          =  attention_probs_dropout_prob
              UpperCamelCase_          =  max_position_embeddings
              UpperCamelCase_          =  type_vocab_size
              UpperCamelCase_          =  type_sequence_label_size
              UpperCamelCase_          =  initializer_range
              UpperCamelCase_          =  num_labels
              UpperCamelCase_          =  num_choices
              UpperCamelCase_          =  scope

       def lowercase (		self: Any		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size		)

              UpperCamelCase_          =  None
              if self.use_input_mask:
                     UpperCamelCase_          =  random_attention_mask([self.batch_size, self.seq_length]		)

              UpperCamelCase_          =  None

              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]					,   self.num_choices		)

              UpperCamelCase_          =  self.get_config()

              return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

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




              """simple docstring"""


              return FalconConfig(
                  vocab_size=self.vocab_size					,   hidden_size=self.hidden_size					,   num_hidden_layers=self.num_hidden_layers					,   num_attention_heads=self.num_attention_heads					,   intermediate_size=self.intermediate_size					,   hidden_act=self.hidden_act					,   hidden_dropout_prob=self.hidden_dropout_prob					,   attention_probs_dropout_prob=self.attention_probs_dropout_prob					,   max_position_embeddings=self.max_position_embeddings					,   type_vocab_size=self.type_vocab_size					,   is_decoder=_SCREAMING_SNAKE_CASE					,   initializer_range=self.initializer_range					,   pad_token_id=1					,   new_decoder_architecture=_SCREAMING_SNAKE_CASE					,   )

       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: Dict					,   _SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: Dict					,   _SCREAMING_SNAKE_CASE: Union[str, Any]		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  FalconModel(config=_SCREAMING_SNAKE_CASE		)
              model.to(_SCREAMING_SNAKE_CASE		)
              model.eval()
              UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE		)
              self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)		)

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




              """simple docstring"""


              UpperCamelCase_          =  True
              UpperCamelCase_          =  FalconModel(_SCREAMING_SNAKE_CASE		)
              model.to(_SCREAMING_SNAKE_CASE		)
              model.eval()
              UpperCamelCase_          =  model(
                  _SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   encoder_hidden_states=_SCREAMING_SNAKE_CASE					,   encoder_attention_mask=_SCREAMING_SNAKE_CASE					,   )
              UpperCamelCase_          =  model(
                  _SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   encoder_hidden_states=_SCREAMING_SNAKE_CASE					,   )
              UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE		)
              self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)		)

       def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Optional[int]					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: int					,   )       -> int:




              """simple docstring"""


              UpperCamelCase_          =  FalconForCausalLM(config=_SCREAMING_SNAKE_CASE		)
              model.to(_SCREAMING_SNAKE_CASE		)
              model.eval()
              UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   labels=_SCREAMING_SNAKE_CASE		)
              self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.vocab_size)		)

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




              """simple docstring"""


              UpperCamelCase_          =  True
              UpperCamelCase_          =  True
              UpperCamelCase_          =  FalconForCausalLM(config=_SCREAMING_SNAKE_CASE		)
              model.to(_SCREAMING_SNAKE_CASE		)
              model.eval()

              # first forward pass
              UpperCamelCase_          =  model(
                  _SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   encoder_hidden_states=_SCREAMING_SNAKE_CASE					,   encoder_attention_mask=_SCREAMING_SNAKE_CASE					,   use_cache=_SCREAMING_SNAKE_CASE					,   )
              UpperCamelCase_          =  outputs.past_key_values

              # create hypothetical multiple next token and extent to next_input_ids
              UpperCamelCase_          =  ids_tensor((self.batch_size, 3)					,   config.vocab_size		)
              UpperCamelCase_          =  ids_tensor((self.batch_size, 3)					,   vocab_size=2		)

              # append to next input_ids and
              UpperCamelCase_          =  torch.cat([input_ids, next_tokens]					,   dim=-1		)
              UpperCamelCase_          =  torch.cat([input_mask, next_mask]					,   dim=-1		)

              UpperCamelCase_          =  model(
                  _SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   encoder_hidden_states=_SCREAMING_SNAKE_CASE					,   encoder_attention_mask=_SCREAMING_SNAKE_CASE					,   output_hidden_states=_SCREAMING_SNAKE_CASE					,   )["hidden_states"][0]
              UpperCamelCase_          =  model(
                  _SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   encoder_hidden_states=_SCREAMING_SNAKE_CASE					,   encoder_attention_mask=_SCREAMING_SNAKE_CASE					,   past_key_values=_SCREAMING_SNAKE_CASE					,   output_hidden_states=_SCREAMING_SNAKE_CASE					,   )["hidden_states"][0]

              # select random slice
              UpperCamelCase_          =  ids_tensor((1,)					,   output_from_past.shape[-1]		).item()
              UpperCamelCase_          =  output_from_no_past[:, -3:, random_slice_idx].detach()
              UpperCamelCase_          =  output_from_past[:, :, random_slice_idx].detach()

              self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

              # test that outputs are equal for slice
              self.parent.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   atol=1e-3		)		)



       def lowercase (		self: Tuple		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  self.prepare_config_and_inputs()
              (
                  (
                  UpperCamelCase_
              )       , (
                  UpperCamelCase_
              )       , (
                  UpperCamelCase_
              )       , (
                  UpperCamelCase_
              )       , (
                  UpperCamelCase_
              )       , (
                  UpperCamelCase_
              )       , (
                  UpperCamelCase_
              )       , 
              )          =  config_and_inputs
              UpperCamelCase_          =  {"input_ids": input_ids, "attention_mask": input_mask}
              return config, inputs_dict



@require_torch
class 							_UpperCamelCase					(	lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Optional[Any]         =   (
           (
               FalconModel,
               FalconForCausalLM,
               FalconForSequenceClassification,
               FalconForTokenClassification,
               FalconForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       _UpperCamelCase				:					Union[str, Any]         =   (FalconForCausalLM,) if is_torch_available() else ()
       _UpperCamelCase				:					Optional[int]         =   (
           {
               '''feature-extraction''': FalconModel,
               '''text-classification''': FalconForSequenceClassification,
               '''text-generation''': FalconForCausalLM,
               '''question-answering''': FalconForQuestionAnswering,
               '''token-classification''': FalconForTokenClassification,
               '''zero-shot''': FalconForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       _UpperCamelCase				:					Any         =   False
       _UpperCamelCase				:					int         =   False

       def lowercase (		self: str		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  FalconModelTester(self		)
              UpperCamelCase_          =  ConfigTester(self					,   config_class=_SCREAMING_SNAKE_CASE					,   hidden_size=37		)

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




              """simple docstring"""


              self.config_tester.run_common_tests()

       def lowercase (		self: str		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              UpperCamelCase_       , *UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs()
              for alibi in [True, False]:
                     UpperCamelCase_          =  alibi
                     self.model_tester.create_and_check_model(_SCREAMING_SNAKE_CASE					,   *_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Any		)       -> str:




              """simple docstring"""


              UpperCamelCase_       , UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()
              UpperCamelCase_          =  3
              UpperCamelCase_          =  input_dict["input_ids"]
              UpperCamelCase_          =  input_ids.ne(1		).to(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  ids_tensor([self.model_tester.batch_size]					,   self.model_tester.type_sequence_label_size		)
              UpperCamelCase_          =  FalconForSequenceClassification(_SCREAMING_SNAKE_CASE		)
              model.to(_SCREAMING_SNAKE_CASE		)
              model.eval()
              UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   labels=_SCREAMING_SNAKE_CASE		)
              self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)		)

       def lowercase (		self: Dict		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_       , UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()
              UpperCamelCase_          =  3
              UpperCamelCase_          =  "single_label_classification"
              UpperCamelCase_          =  input_dict["input_ids"]
              UpperCamelCase_          =  input_ids.ne(1		).to(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  ids_tensor([self.model_tester.batch_size]					,   self.model_tester.type_sequence_label_size		)
              UpperCamelCase_          =  FalconForSequenceClassification(_SCREAMING_SNAKE_CASE		)
              model.to(_SCREAMING_SNAKE_CASE		)
              model.eval()
              UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   labels=_SCREAMING_SNAKE_CASE		)
              self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)		)

       def lowercase (		self: List[str]		)       -> int:




              """simple docstring"""


              UpperCamelCase_       , UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()
              UpperCamelCase_          =  input_dict["input_ids"]
              UpperCamelCase_          =  FalconForCausalLM(_SCREAMING_SNAKE_CASE		)
              model.to(_SCREAMING_SNAKE_CASE		)
              model.eval()
              UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   use_cache=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  input_ids.shape[0]
              UpperCamelCase_          =  model._convert_to_rw_cache(result.past_key_values		)
              UpperCamelCase_          =  model._convert_cache_to_standard_format(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              for layer in range(len(_SCREAMING_SNAKE_CASE		)		):
                     for tensor_idx in range(2		):
                            self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3		)
                            self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4		)
                            self.assertTrue(
                                torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]		)		)

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




              """simple docstring"""


              UpperCamelCase_       , UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()
              UpperCamelCase_          =  3
              UpperCamelCase_          =  "multi_label_classification"
              UpperCamelCase_          =  input_dict["input_ids"]
              UpperCamelCase_          =  input_ids.ne(1		).to(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  ids_tensor(
                  [self.model_tester.batch_size, config.num_labels]					,   self.model_tester.type_sequence_label_size		).to(torch.float		)
              UpperCamelCase_          =  FalconForSequenceClassification(_SCREAMING_SNAKE_CASE		)
              model.to(_SCREAMING_SNAKE_CASE		)
              model.eval()
              UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   labels=_SCREAMING_SNAKE_CASE		)
              self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)		)



       def lowercase (		self: List[Any]		)       -> List[Any]:




              """simple docstring"""


              for model_class in self.all_generative_model_classes:
                     UpperCamelCase_       , UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()

                     # If it doesn't support cache, pass the test
                     if not hasattr(_SCREAMING_SNAKE_CASE					,   "use_cache"		):
                            return

                     UpperCamelCase_          =  model_class(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)
                     if "use_cache" not in inputs:
                            UpperCamelCase_          =  True
                     UpperCamelCase_          =  model(**_SCREAMING_SNAKE_CASE		)

                     # If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
                     if "past_key_values" not in outputs:
                            return

                     UpperCamelCase_          =  (
                         getattr(_SCREAMING_SNAKE_CASE					,   "decoder_layers"					,   _SCREAMING_SNAKE_CASE		)
                         or getattr(_SCREAMING_SNAKE_CASE					,   "num_decoder_layers"					,   _SCREAMING_SNAKE_CASE		)
                         or config.num_hidden_layers
                     )
                     UpperCamelCase_          =  getattr(_SCREAMING_SNAKE_CASE					,   "num_kv_heads"					,   config.num_attention_heads		)
                     UpperCamelCase_          =  getattr(_SCREAMING_SNAKE_CASE					,   "d_model"					,   config.hidden_size		)
                     UpperCamelCase_          =  embed_dim // num_attention_heads

                     UpperCamelCase_          =  outputs["past_key_values"]
                     self.assertEqual(len(_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)

                     UpperCamelCase_       , UpperCamelCase_          =  inputs["input_ids"].shape
                     for i in range(_SCREAMING_SNAKE_CASE		):
                            if config.new_decoder_architecture:
                                   UpperCamelCase_          =  config.num_attention_heads
                            elif config.multi_query:
                                   UpperCamelCase_          =  1
                            self.assertEqual(len(past_kv[0]		)					,   2		)  # K V for the decoder = 2
                            self.assertEqual(
                                past_kv[i][0].shape					,   (batch_size, num_attention_heads, seq_length, per_head_embed_dim)		)
                            self.assertEqual(
                                past_kv[i][1].shape					,   (batch_size, num_attention_heads, seq_length, per_head_embed_dim)		)



@require_torch
class 							_UpperCamelCase					(	unittest.TestCase     ):

       @slow
       def lowercase (		self: Tuple		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  AutoTokenizer.from_pretrained("Rocketknight1/falcon-rw-1b"		)
              UpperCamelCase_          =  FalconForCausalLM.from_pretrained("Rocketknight1/falcon-rw-1b"		)
              model.eval()
              model.to(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tokenizer("My favorite food is"					,   return_tensors="pt"		).to(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  (
                  "My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."
              )

              UpperCamelCase_          =  model.generate(**_SCREAMING_SNAKE_CASE					,   do_sample=_SCREAMING_SNAKE_CASE					,   max_new_tokens=19		)
              UpperCamelCase_          =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE		)[0]

              self.assertEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
                     UpperCamelCase_          =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  FalconForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE		)
                     model.eval()
                     model.to(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  tokenizer("My favorite food is"					,   return_tensors="pt"		).to(_SCREAMING_SNAKE_CASE		)

                     # We just test that these run without errors - the models are randomly initialized
                     # and so the actual text outputs will be garbage
                     model.generate(**_SCREAMING_SNAKE_CASE					,   do_sample=_SCREAMING_SNAKE_CASE					,   max_new_tokens=4		)
                     model.generate(**_SCREAMING_SNAKE_CASE					,   do_sample=_SCREAMING_SNAKE_CASE					,   max_new_tokens=4		)
                     model.generate(**_SCREAMING_SNAKE_CASE					,   num_beams=2					,   max_new_tokens=4		)



       @slow
       def lowercase (		self: Union[str, Any]		)       -> Any:




              """simple docstring"""


              with torch.no_grad():
                     for repo in [
                         "Rocketknight1/falcon-rw-1b",
                         "Rocketknight1/tiny-random-falcon-7b",
                         "Rocketknight1/tiny-random-falcon-40b",
                     ]:
                            UpperCamelCase_          =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_          =  FalconForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE		)
                            model.eval()
                            model.to(device=_SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_          =  tokenizer("My favorite food is"					,   return_tensors="pt"		).to(_SCREAMING_SNAKE_CASE		)

                            # Test results are the same with and without cache
                            UpperCamelCase_          =  model.generate(**_SCREAMING_SNAKE_CASE					,   do_sample=_SCREAMING_SNAKE_CASE					,   max_new_tokens=20					,   use_cache=_SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_          =  model.generate(**_SCREAMING_SNAKE_CASE					,   do_sample=_SCREAMING_SNAKE_CASE					,   max_new_tokens=20					,   use_cache=_SCREAMING_SNAKE_CASE		)
                            self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0		)

