






'''simple docstring'''




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
  import torch

class      lowerCamelCase_     :
      '''simple docstring'''

      def __init__(	self						:    List[str]						, A						:    Any						, A						:    Dict=14						, A						:    int=7						, A						:    Optional[Any]=True						, A						:    Optional[Any]=True						, A						:    List[Any]=False						, A						:    int=True						, A						:    Optional[Any]=99						, A						:    Union[str, Any]=32						, A						:    Any=4						, A						:    List[Any]=4						, A						:    Optional[Any]=[[["global", "local"], 2]]						, A						:    Tuple=37						, A						:    int="gelu"						, A						:    List[Any]=0.1						, A						:    Tuple=0.1						, A						:    List[str]=512						, A						:    Any=7						, A						:    Optional[int]=0.02						, ):
             _UpperCAmelCase					:  List[Any]											=   parent
             _UpperCAmelCase					:  int											=   batch_size
             _UpperCAmelCase					:  int											=   seq_length
             _UpperCAmelCase					:  Optional[int]											=   is_training
             _UpperCAmelCase					:  str											=   use_input_mask
             _UpperCAmelCase					:  Optional[int]											=   use_token_type_ids
             _UpperCAmelCase					:  int											=   use_labels
             _UpperCAmelCase					:  Union[str, Any]											=   vocab_size
             _UpperCAmelCase					:  List[Any]											=   hidden_size
             _UpperCAmelCase					:  Optional[Any]											=   num_hidden_layers
             _UpperCAmelCase					:  Union[str, Any]											=   num_attention_heads
             _UpperCAmelCase					:  Dict											=   attention_types
             _UpperCAmelCase					:  int											=   intermediate_size
             _UpperCAmelCase					:  Union[str, Any]											=   hidden_act
             _UpperCAmelCase					:  Dict											=   hidden_dropout_prob
             _UpperCAmelCase					:  Any											=   attention_probs_dropout_prob
             _UpperCAmelCase					:  List[str]											=   max_position_embeddings
             _UpperCAmelCase					:  Any											=   window_size
             _UpperCAmelCase					:  List[str]											=   initializer_range
             _UpperCAmelCase					:  Union[str, Any]											=   None
             _UpperCAmelCase					:  Optional[int]											=   vocab_size - 1
             _UpperCAmelCase					:  str											=   vocab_size - 1
             _UpperCAmelCase					:  Dict											=   vocab_size - 1




      def 					_A     (	self						:    str	):
             _UpperCAmelCase					:  Tuple											=   ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size	)

             _UpperCAmelCase					:  Tuple											=   None
             if self.use_input_mask:
                    _UpperCAmelCase					:  List[Any]											=   random_attention_mask([self.batch_size, self.seq_length]	)

             _UpperCAmelCase					:  Any											=   GPTNeoConfig(
                 vocab_size=self.vocab_size						, hidden_size=self.hidden_size						, num_layers=self.num_hidden_layers						, num_heads=self.num_attention_heads						, max_position_embeddings=self.max_position_embeddings						, use_cache=A						, bos_token_id=self.bos_token_id						, eos_token_id=self.eos_token_id						, pad_token_id=self.pad_token_id						, window_size=self.window_size						, attention_types=self.attention_types						, )

             return (config, input_ids, input_mask)




      def 					_A     (	self						:    int	):
             _UpperCAmelCase					:  Optional[int]											=   self.prepare_config_and_inputs()
             _UpperCAmelCase					:  Any											=   config_and_inputs
             _UpperCAmelCase					:  Tuple											=   {"input_ids": input_ids, "attention_mask": attention_mask}
             return config, inputs_dict




      def 					_A     (	self						:    Dict						, A						:    Optional[int]						, A						:    Optional[int]						, A						:    Tuple						, A						:    Optional[Any]	):
             _UpperCAmelCase					:  Union[str, Any]											=   20
             _UpperCAmelCase					:  str											=   model_class_name(A	)

             _UpperCAmelCase					:  List[str]											=   model.init_cache(input_ids.shape[0]						, A	)
             _UpperCAmelCase					:  str											=   jnp.ones((input_ids.shape[0], max_decoder_length)						, dtype="i4"	)

             _UpperCAmelCase					:  Union[str, Any]											=   jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1	)[None, :]						, (input_ids.shape[0], input_ids.shape[-1] - 1)	)
             _UpperCAmelCase					:  str											=   model(
                 input_ids[:, :-1]						, attention_mask=A						, past_key_values=A						, position_ids=A						, )

             _UpperCAmelCase					:  int											=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						, dtype="i4"	)
             _UpperCAmelCase					:  Union[str, Any]											=   model(
                 input_ids[:, -1:]						, attention_mask=A						, past_key_values=outputs_cache.past_key_values						, position_ids=A						, )

             _UpperCAmelCase					:  Any											=   model(A	)

             _UpperCAmelCase					:  List[Any]											=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
             self.parent.assertTrue(diff < 1E-3						, msg=F"""Max diff is {diff}"""	)





      def 					_A     (	self						:    List[str]						, A						:    Dict						, A						:    str						, A						:    List[Any]						, A						:    Tuple	):
             _UpperCAmelCase					:  int											=   20
             _UpperCAmelCase					:  str											=   model_class_name(A	)

             _UpperCAmelCase					:  Union[str, Any]											=   jnp.concatenate(
                 [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])	)]						, axis=-1						, )

             _UpperCAmelCase					:  List[Any]											=   model.init_cache(input_ids.shape[0]						, A	)
             _UpperCAmelCase					:  List[Any]											=   jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1	)[None, :]						, (input_ids.shape[0], input_ids.shape[-1] - 1)	)

             _UpperCAmelCase					:  List[str]											=   model(
                 input_ids[:, :-1]						, attention_mask=A						, past_key_values=A						, position_ids=A						, )
             _UpperCAmelCase					:  Optional[Any]											=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						, dtype="i4"	)
             _UpperCAmelCase					:  Optional[Any]											=   model(
                 input_ids[:, -1:]						, past_key_values=outputs_cache.past_key_values						, attention_mask=A						, position_ids=A						, )

             _UpperCAmelCase					:  Union[str, Any]											=   model(A						, attention_mask=A	)

             _UpperCAmelCase					:  List[Any]											=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
             self.parent.assertTrue(diff < 1E-3						, msg=F"""Max diff is {diff}"""	)

@require_flax
class      lowerCamelCase_     (snake_case__							,			snake_case__							,			unittest.TestCase   ):
      '''simple docstring'''

      __UpperCamelCase:  List[Any]      =						(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      __UpperCamelCase:  Dict      =						(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




      def 					_A     (	self						:    str	):
             _UpperCAmelCase					:  Union[str, Any]											=   FlaxGPTNeoModelTester(self	)




      def 					_A     (	self						:    Optional[int]	):
             for model_class_name in self.all_model_classes:
                    _UpperCAmelCase					:  Optional[Any]											=   self.model_tester.prepare_config_and_inputs()
                    self.model_tester.check_use_cache_forward(A						, A						, A						, A	)




      def 					_A     (	self						:    Union[str, Any]	):
             for model_class_name in self.all_model_classes:
                    _UpperCAmelCase					:  Dict											=   self.model_tester.prepare_config_and_inputs()
                    self.model_tester.check_use_cache_forward_with_attn_mask(
                        A						, A						, A						, A	)




      @slow
      def 					_A     (	self						:    Union[str, Any]	):
             _UpperCAmelCase					:  Optional[Any]											=   GPTaTokenizer.from_pretrained("gpt2"						, pad_token="<|endoftext|>"						, padding_side="left"	)
             _UpperCAmelCase					:  Any											=   tokenizer(["Hello this is a long string", "Hey"]						, return_tensors="np"						, padding=A						, truncation=A	)

             _UpperCAmelCase					:  str											=   FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M"	)
             _UpperCAmelCase					:  List[Any]											=   False
             _UpperCAmelCase					:  Tuple											=   model.config.eos_token_id

             _UpperCAmelCase					:  str											=   jax.jit(model.generate	)

             _UpperCAmelCase					:  int											=   jit_generate(
                 inputs["input_ids"]						, attention_mask=inputs["attention_mask"]						, pad_token_id=tokenizer.pad_token_id	).sequences

             _UpperCAmelCase					:  Dict											=   tokenizer.batch_decode(A						, skip_special_tokens=A	)

             _UpperCAmelCase					:  Tuple											=   [
                 "Hello this is a long string of text.\n\nI'm trying to get the text of the",
                 "Hey, I'm a little late to the party. I'm going to",
             ]

             self.assertListEqual(A						, A	)




      @is_pt_flax_cross_test
      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  Optional[int]											=   self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__	):
                           # prepare inputs
                           _UpperCAmelCase					:  Optional[int]											=   self._prepare_for_class(A						, A	)
                           _UpperCAmelCase					:  int											=   {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

                           # load corresponding PyTorch class
                           _UpperCAmelCase					:  Any											=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
                           _UpperCAmelCase					:  Tuple											=   getattr(A						, A	)

                           _UpperCAmelCase					:  Optional[int]											=   pt_inputs["input_ids"].shape
                           _UpperCAmelCase					:  Optional[int]											=   np.random.randint(0						, seq_length - 1						, size=(batch_size,)	)
                           for batch_idx, start_index in enumerate(A	):
                                  _UpperCAmelCase					:  Optional[int]											=   0
                                  _UpperCAmelCase					:  Dict											=   1
                                  _UpperCAmelCase					:  Optional[Any]											=   0
                                  _UpperCAmelCase					:  Optional[Any]											=   1
                           _UpperCAmelCase					:  Any											=   pt_model_class(A	).eval()
                           _UpperCAmelCase					:  int											=   model_class(A						, dtype=jnp.floataa	)

                           _UpperCAmelCase					:  Union[str, Any]											=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()						, A	)
                           _UpperCAmelCase					:  Optional[Any]											=   fx_state

                           with torch.no_grad():
                                  _UpperCAmelCase					:  List[Any]											=   pt_model(**A	).to_tuple()

                           _UpperCAmelCase					:  List[Any]											=   fx_model(**A	).to_tuple()
                           self.assertEqual(len(A	)						, len(A	)						, "Output lengths differ between Flax and PyTorch"	)
                           for fx_output, pt_output in zip(A						, A	):
                                  self.assert_almost_equals(fx_output[:, -1]						, pt_output[:, -1].numpy()						, 4E-2	)

                           with tempfile.TemporaryDirectory() as tmpdirname:
                                  pt_model.save_pretrained(A	)
                                  _UpperCAmelCase					:  Optional[Any]											=   model_class.from_pretrained(A						, from_pt=A	)

                           _UpperCAmelCase					:  Optional[int]											=   fx_model_loaded(**A	).to_tuple()
                           self.assertEqual(
                               len(A	)						, len(A	)						, "Output lengths differ between Flax and PyTorch"	)
                           for fx_output_loaded, pt_output in zip(A						, A	):
                                  self.assert_almost_equals(fx_output_loaded[:, -1]						, pt_output[:, -1].numpy()						, 4E-2	)




      @is_pt_flax_cross_test
      def 					_A     (	self						:    int	):
             _UpperCAmelCase					:  Optional[int]											=   self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__	):
                           # prepare inputs
                           _UpperCAmelCase					:  Optional[Any]											=   self._prepare_for_class(A						, A	)
                           _UpperCAmelCase					:  Optional[Any]											=   {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

                           # load corresponding PyTorch class
                           _UpperCAmelCase					:  Tuple											=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
                           _UpperCAmelCase					:  Tuple											=   getattr(A						, A	)

                           _UpperCAmelCase					:  str											=   pt_model_class(A	).eval()
                           _UpperCAmelCase					:  str											=   model_class(A						, dtype=jnp.floataa	)

                           _UpperCAmelCase					:  int											=   load_flax_weights_in_pytorch_model(A						, fx_model.params	)
                           _UpperCAmelCase					:  Optional[int]											=   pt_inputs["input_ids"].shape
                           _UpperCAmelCase					:  int											=   np.random.randint(0						, seq_length - 1						, size=(batch_size,)	)
                           for batch_idx, start_index in enumerate(A	):
                                  _UpperCAmelCase					:  Any											=   0
                                  _UpperCAmelCase					:  Optional[int]											=   1
                                  _UpperCAmelCase					:  Tuple											=   0
                                  _UpperCAmelCase					:  str											=   1

                           # make sure weights are tied in PyTorch
                           pt_model.tie_weights()

                           with torch.no_grad():
                                  _UpperCAmelCase					:  List[str]											=   pt_model(**A	).to_tuple()

                           _UpperCAmelCase					:  Tuple											=   fx_model(**A	).to_tuple()
                           self.assertEqual(len(A	)						, len(A	)						, "Output lengths differ between Flax and PyTorch"	)
                           for fx_output, pt_output in zip(A						, A	):
                                  self.assert_almost_equals(fx_output[:, -1]						, pt_output[:, -1].numpy()						, 4E-2	)

                           with tempfile.TemporaryDirectory() as tmpdirname:
                                  fx_model.save_pretrained(A	)
                                  _UpperCAmelCase					:  List[str]											=   pt_model_class.from_pretrained(A						, from_flax=A	)

                           with torch.no_grad():
                                  _UpperCAmelCase					:  List[str]											=   pt_model_loaded(**A	).to_tuple()

                           self.assertEqual(
                               len(A	)						, len(A	)						, "Output lengths differ between Flax and PyTorch"	)
                           for fx_output, pt_output in zip(A						, A	):
                                  self.assert_almost_equals(fx_output[:, -1]						, pt_output[:, -1].numpy()						, 4E-2	)





      @slow
      def 					_A     (	self						:    Any	):
             for model_class_name in self.all_model_classes:
                    _UpperCAmelCase					:  Dict											=   model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"	)
                    _UpperCAmelCase					:  str											=   model(np.ones((1, 1)	)	)
                    self.assertIsNotNone(A	)


'''simple docstring'''




from sklearn.metrics import accuracy_score

import datasets


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


__SCREAMING_SNAKE_CASE	:					Any					=  """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,			_KWARGS_DESCRIPTION   )
class      lowerCamelCase_     (datasets.Metric   ):
      '''simple docstring'''

      def 					_A     (	self						:    int	):
             return datasets.MetricInfo(
                 description=_DESCRIPTION						, citation=_CITATION						, inputs_description=_KWARGS_DESCRIPTION						, features=datasets.Features(
                     {
                         "predictions": datasets.Sequence(datasets.Value("int32"	)	),
                         "references": datasets.Sequence(datasets.Value("int32"	)	),
                     }
                     if self.config_name == "multilabel"
                     else {
                         "predictions": datasets.Value("int32"	),
                         "references": datasets.Value("int32"	),
                     }	)						, reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"]						, )





      def 					_A     (	self						:    str						, A						:    List[str]						, A						:    int						, A						:    List[Any]=True						, A						:    List[str]=None	):
             return {
                 "accuracy": float(
                     accuracy_score(A						, A						, normalize=A						, sample_weight=A	)	)
             }

