
import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   import jax.numpy as jnp

   from transformers.models.roformer.modeling_flax_roformer import (
       FlaxRoFormerForMaskedLM,
       FlaxRoFormerForMultipleChoice,
       FlaxRoFormerForQuestionAnswering,
       FlaxRoFormerForSequenceClassification,
       FlaxRoFormerForTokenClassification,
       FlaxRoFormerModel,
   )





class 							UpperCamelCase__						(     unittest.TestCase     ):






       """simple docstring"""


       def __init__(				self      ,			_A      ,			_A=13      ,			_A=7      ,			_A=True      ,			_A=True      ,			_A=True      ,			_A=True      ,			_A=99      ,			_A=32      ,			_A=5      ,			_A=4      ,			_A=37      ,			_A="gelu"      ,			_A=0.1      ,			_A=0.1      ,			_A=512      ,			_A=16      ,			_A=2      ,			_A=0.02      ,			_A=4      ,			)							->							Tuple:
          SCREAMING_SNAKE_CASE_     =	parent
          SCREAMING_SNAKE_CASE_     =	batch_size
          SCREAMING_SNAKE_CASE_     =	seq_length
          SCREAMING_SNAKE_CASE_     =	is_training
          SCREAMING_SNAKE_CASE_     =	use_attention_mask
          SCREAMING_SNAKE_CASE_     =	use_token_type_ids
          SCREAMING_SNAKE_CASE_     =	use_labels
          SCREAMING_SNAKE_CASE_     =	vocab_size
          SCREAMING_SNAKE_CASE_     =	hidden_size
          SCREAMING_SNAKE_CASE_     =	num_hidden_layers
          SCREAMING_SNAKE_CASE_     =	num_attention_heads
          SCREAMING_SNAKE_CASE_     =	intermediate_size
          SCREAMING_SNAKE_CASE_     =	hidden_act
          SCREAMING_SNAKE_CASE_     =	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_     =	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_     =	max_position_embeddings
          SCREAMING_SNAKE_CASE_     =	type_vocab_size
          SCREAMING_SNAKE_CASE_     =	type_sequence_label_size
          SCREAMING_SNAKE_CASE_     =	initializer_range
          SCREAMING_SNAKE_CASE_     =	num_choices



       def   _UpperCamelCase     (				self     )							->							int:
          SCREAMING_SNAKE_CASE_     =	ids_tensor([self.batch_size, self.seq_length]      ,			self.vocab_size     )

          SCREAMING_SNAKE_CASE_     =	None
          if self.use_attention_mask:
             SCREAMING_SNAKE_CASE_     =	random_attention_mask([self.batch_size, self.seq_length]     )

          SCREAMING_SNAKE_CASE_     =	None
          if self.use_token_type_ids:
             SCREAMING_SNAKE_CASE_     =	ids_tensor([self.batch_size, self.seq_length]      ,			self.type_vocab_size     )

          SCREAMING_SNAKE_CASE_     =	RoFormerConfig(
              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=__A      ,			initializer_range=self.initializer_range      ,			)

          return config, input_ids, token_type_ids, attention_mask




       def   _UpperCamelCase     (				self     )							->							Optional[int]:
          SCREAMING_SNAKE_CASE_     =	self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_     ,      SCREAMING_SNAKE_CASE_     ,      SCREAMING_SNAKE_CASE_     ,      SCREAMING_SNAKE_CASE_     =	config_and_inputs
          SCREAMING_SNAKE_CASE_     =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
          return config, inputs_dict







@require_flax
class 							UpperCamelCase__						(     __lowerCamelCase    ,  unittest.TestCase     ):






       """simple docstring"""


       UpperCAmelCase_							    =True

       UpperCAmelCase_							    =(
           (
               FlaxRoFormerModel,
               FlaxRoFormerForMaskedLM,
               FlaxRoFormerForSequenceClassification,
               FlaxRoFormerForTokenClassification,
               FlaxRoFormerForMultipleChoice,
               FlaxRoFormerForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )



       def   _UpperCamelCase     (				self     )							->							int:
          SCREAMING_SNAKE_CASE_     =	FlaxRoFormerModelTester(self     )




       @slow
       def   _UpperCamelCase     (				self     )							->							str:
          for model_class_name in self.all_model_classes:
             SCREAMING_SNAKE_CASE_     =	model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''      ,			from_pt=__A     )
             SCREAMING_SNAKE_CASE_     =	model(np.ones((1, 1)     )     )
             self.assertIsNotNone(__A     )







@require_flax
class 							UpperCamelCase__						(     unittest.TestCase     ):






       """simple docstring"""


       @slow
       def   _UpperCamelCase     (				self     )							->							str:
          SCREAMING_SNAKE_CASE_     =	FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''     )
          SCREAMING_SNAKE_CASE_     =	jnp.array([[0, 1, 2, 3, 4, 5]]     )
          SCREAMING_SNAKE_CASE_     =	model(__A     )[0]

          SCREAMING_SNAKE_CASE_     =	50000

          SCREAMING_SNAKE_CASE_     =	(1, 6, vocab_size)
          self.assertEqual(output.shape      ,			__A     )

          SCREAMING_SNAKE_CASE_     =	jnp.array(
              [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]     )

          self.assertTrue(jnp.allclose(output[:, :3, :3]      ,			__A      ,			atol=1E-4     )     )









'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


a__							:							Any =logging.get_logger(__name__)

a__							:							Optional[Any] ={
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Dict					="gpt_neo"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=["past_key_values"]
 SCREAMING_SNAKE_CASE_	:					List[Any]					={"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}






 def __init__(       self       :							Union[str, Any]							,	__A       :							Union[str, Any]=5_0_2_5_7							,	__A       :							Any=2_0_4_8							,	__A       :							Optional[Any]=2_0_4_8							,	__A       :							Any=2_4							,	__A       :							Union[str, Any]=[[["global", "local"], 1_2]]							,	__A       :							str=1_6							,	__A       :							Optional[int]=None							,	__A       :							Union[str, Any]=2_5_6							,	__A       :							Any="gelu_new"							,	__A       :							Dict=0.0							,	__A       :							Optional[int]=0.0							,	__A       :							int=0.0							,	__A       :							List[str]=0.1							,	__A       :							Any=1e-5							,	__A       :							int=0.02							,	__A       :							List[str]=True							,	__A       :							Tuple=5_0_2_5_6							,	__A       :							Optional[Any]=5_0_2_5_6							,	**__A       :							Optional[Any]							,	):
        __UpperCamelCase       =  vocab_size
        __UpperCamelCase       =  max_position_embeddings
        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  num_layers
        __UpperCamelCase       =  num_heads
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  window_size
        __UpperCamelCase       =  activation_function
        __UpperCamelCase       =  resid_dropout
        __UpperCamelCase       =  embed_dropout
        __UpperCamelCase       =  attention_dropout
        __UpperCamelCase       =  classifier_dropout
        __UpperCamelCase       =  layer_norm_epsilon
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  use_cache

        __UpperCamelCase       =  bos_token_id
        __UpperCamelCase       =  eos_token_id

        __UpperCamelCase       =  attention_types
        __UpperCamelCase       =  self.expand_attention_types_params(__A	)

        if len(self.attention_layers	) != self.num_layers:
               raise ValueError(
                   'Configuration for convolutional module is incorrect. '
                   'It is required that `len(config.attention_layers)` == `config.num_layers` '
                   f'''but is `len(config.attention_layers) = {len(self.attention_layers	)}`, '''
                   f'''`config.num_layers = {self.num_layers}`. '''
                   '`config.attention_layers` is prepared using `config.attention_types`. '
                   'Please verify the value of `config.attention_types` argument.'	)

        super().__init__(bos_token_id=__A							,	eos_token_id=__A							,	**__A	)






 @staticmethod
 def        _lowerCamelCase					(       __A       :							Tuple	):
        __UpperCamelCase       =  []
        for item in attention_types:
               for _ in range(item[1]	):
                      attentions.extend(item[0]	)
        return attentions






def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Any				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  List[str]     ) ->      Any:



       """simple docstring"""






       import torch

       __UpperCamelCase       =  input.size()
       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  shape[dimension]

       __UpperCamelCase       =  torch.arange(0				,  __lowercase				,  __lowercase     )
       __UpperCamelCase       =  torch.div(sizedim - size				,  __lowercase				,  rounding_mode='floor'     ) + 1
       __UpperCamelCase       =  torch.arange(__lowercase     ) + low_indices[:min_length][:, None]

       __UpperCamelCase       =  [slice(__lowercase     )] * rank
       __UpperCamelCase       =  indices
       __UpperCamelCase       =  input[s]

       __UpperCamelCase       =  list(range(0				,  rank + 1     )     )
       perm.append(perm.pop(dimension + 1     )     )

       return sliced.permute(__lowercase     )
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Optional[int]     ) ->      Optional[int]:



       """simple docstring"""






       import torch

       __UpperCamelCase       =  torch.arange(1				,  __lowercase     )
       __UpperCamelCase       =  torch.remainder(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  remainders == 0
       __UpperCamelCase       =  candidates[divisor_indices]
       __UpperCamelCase       =  torch.max(__lowercase     )
       return largest_divisor, torch.div(__lowercase				,  __lowercase				,  rounding_mode='floor'     )


class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 @property
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}	)
        if self.use_past:
               self.fill_with_past_key_values_(__A							,	direction='inputs'	)
               __UpperCamelCase       =  {0: 'batch', 1: 'past_sequence + sequence'}
        else:
               __UpperCamelCase       =  {0: 'batch', 1: 'sequence'}

        return common_inputs






 @property
 def        _lowerCamelCase					(       self       :							int	):
        return self._config.num_heads






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							PreTrainedTokenizer							,	__A       :							int = -1							,	__A       :							int = -1							,	__A       :							bool = False							,	__A       :							Optional[TensorType] = None							,	):
        __UpperCamelCase       =  super(__A							,	self	).generate_dummy_inputs(
            __A							,	batch_size=__A							,	seq_length=__A							,	is_pair=__A							,	framework=__A	)

        # We need to order the input in the way they appears in the forward()
        __UpperCamelCase       =  OrderedDict({'input_ids': common_inputs['input_ids']}	)

        # Need to add the past_keys
        if self.use_past:
               if not is_torch_available():
                      raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
               else:
                      import torch

                      __UpperCamelCase       ,						__UpperCamelCase       =  common_inputs['input_ids'].shape
                      # Not using the same length for past_key_values
                      __UpperCamelCase       =  seqlen + 2
                      __UpperCamelCase       =  (
                          batch,
                          self.num_attention_heads,
                          past_key_values_length,
                          self._config.hidden_size // self.num_attention_heads,
                      )
                      __UpperCamelCase       =  [
                          (torch.zeros(__A	), torch.zeros(__A	)) for _ in range(self.num_layers	)
                      ]

        __UpperCamelCase       =  common_inputs['attention_mask']
        if self.use_past:
               __UpperCamelCase       =  ordered_inputs['attention_mask'].dtype
               __UpperCamelCase       =  torch.cat(
                   [ordered_inputs['attention_mask'], torch.ones(__A							,	__A							,	dtype=__A	)]							,	dim=1	)

        return ordered_inputs






 @property
 def        _lowerCamelCase					(       self       :							Dict	):
        return 1_3
