






import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
     FlaxRobertaPreLayerNormForCausalLM,
     FlaxRobertaPreLayerNormForMaskedLM,
     FlaxRobertaPreLayerNormForMultipleChoice,
     FlaxRobertaPreLayerNormForQuestionAnswering,
     FlaxRobertaPreLayerNormForSequenceClassification,
     FlaxRobertaPreLayerNormForTokenClassification,
     FlaxRobertaPreLayerNormModel,
 )





class 			__a  (    unittest.TestCase       ):







 def __init__(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=13							,   SCREAMING_SNAKE_CASE					:   List[Any]=7							,   SCREAMING_SNAKE_CASE					:   int=True							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=True							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=True							,   SCREAMING_SNAKE_CASE					:   List[str]=True							,   SCREAMING_SNAKE_CASE					:   str=99							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=32							,   SCREAMING_SNAKE_CASE					:   Optional[int]=5							,   SCREAMING_SNAKE_CASE					:   Tuple=4							,   SCREAMING_SNAKE_CASE					:   int=37							,   SCREAMING_SNAKE_CASE					:   Tuple="gelu"							,   SCREAMING_SNAKE_CASE					:   Dict=0.1							,   SCREAMING_SNAKE_CASE					:   Optional[int]=0.1							,   SCREAMING_SNAKE_CASE					:   List[Any]=5_12							,   SCREAMING_SNAKE_CASE					:   Tuple=16							,   SCREAMING_SNAKE_CASE					:   str=2							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.0_2							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=4							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  parent
      UpperCamelCase__			:   List[Any]       				=  batch_size
      UpperCamelCase__			:   List[str]       				=  seq_length
      UpperCamelCase__			:   Optional[Any]       				=  is_training
      UpperCamelCase__			:   int       				=  use_attention_mask
      UpperCamelCase__			:   str       				=  use_token_type_ids
      UpperCamelCase__			:   str       				=  use_labels
      UpperCamelCase__			:   int       				=  vocab_size
      UpperCamelCase__			:   List[str]       				=  hidden_size
      UpperCamelCase__			:   Optional[int]       				=  num_hidden_layers
      UpperCamelCase__			:   List[Any]       				=  num_attention_heads
      UpperCamelCase__			:   Optional[int]       				=  intermediate_size
      UpperCamelCase__			:   int       				=  hidden_act
      UpperCamelCase__			:   Optional[Any]       				=  hidden_dropout_prob
      UpperCamelCase__			:   List[str]       				=  attention_probs_dropout_prob
      UpperCamelCase__			:   Any       				=  max_position_embeddings
      UpperCamelCase__			:   List[str]       				=  type_vocab_size
      UpperCamelCase__			:   Tuple       				=  type_sequence_label_size
      UpperCamelCase__			:   Optional[int]       				=  initializer_range
      UpperCamelCase__			:   List[str]       				=  num_choices







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  ids_tensor([self.batch_size, self.seq_length]							,   self.vocab_size						)

      UpperCamelCase__			:   Dict       				=  None
      if self.use_attention_mask:
           UpperCamelCase__			:   Union[str, Any]       				=  random_attention_mask([self.batch_size, self.seq_length]						)

      UpperCamelCase__			:   List[str]       				=  None
      if self.use_token_type_ids:
           UpperCamelCase__			:   Dict       				=  ids_tensor([self.batch_size, self.seq_length]							,   self.type_vocab_size						)

      UpperCamelCase__			:   Any       				=  RobertaPreLayerNormConfig(
          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							,   )

      return config, input_ids, token_type_ids, attention_mask







 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  self.prepare_config_and_inputs()
      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  config_and_inputs
      UpperCamelCase__			:   Any       				=  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
      return config, inputs_dict







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  self.prepare_config_and_inputs()
      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   Optional[Any]       				=  config_and_inputs

      UpperCamelCase__			:   List[str]       				=  True
      UpperCamelCase__			:   Optional[Any]       				=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
      UpperCamelCase__			:   Optional[Any]       				=  ids_tensor([self.batch_size, self.seq_length]							,   vocab_size=2						)

      return (
          config,
          input_ids,
          token_type_ids,
          encoder_hidden_states,
          encoder_attention_mask,
      )







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 			__a  (    A__       ,   unittest.TestCase       ):
 _lowerCAmelCase       :       List[str]        = True

 _lowerCAmelCase       :       str        = (
     (
         FlaxRobertaPreLayerNormModel,
         FlaxRobertaPreLayerNormForCausalLM,
         FlaxRobertaPreLayerNormForMaskedLM,
         FlaxRobertaPreLayerNormForSequenceClassification,
         FlaxRobertaPreLayerNormForTokenClassification,
         FlaxRobertaPreLayerNormForMultipleChoice,
         FlaxRobertaPreLayerNormForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  FlaxRobertaPreLayerNormModelTester(self						)







 @slow
 def 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






      for model_class_name in self.all_model_classes:
           UpperCamelCase__			:   List[Any]       				=  model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"							,   from_pt=SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   List[Any]       				=  model(np.ones((1, 1)						)						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)







@require_flax
class 			__a  (    unittest.TestCase       ):







 @slow
 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"							,   from_pt=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[int]       				=  np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]							,   dtype=jnp.intaa						)
      UpperCamelCase__			:   Dict       				=  model(SCREAMING_SNAKE_CASE						)[0]
      UpperCamelCase__			:   Optional[Any]       				=  [1, 11, 5_02_65]
      self.assertEqual(list(output.shape						)							,   SCREAMING_SNAKE_CASE						)
      # compare the actual values for a slice.
      UpperCamelCase__			:   List[Any]       				=  np.array(
          [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]							,   dtype=np.floataa						)
      self.assertTrue(np.allclose(output[:, :3, :3]							,   SCREAMING_SNAKE_CASE							,   atol=1e-4						)						)







 @slow
 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"							,   from_pt=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   str       				=  np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]							,   dtype=jnp.intaa						)
      UpperCamelCase__			:   Optional[int]       				=  model(SCREAMING_SNAKE_CASE						)[0]
      # compare the actual values for a slice.
      UpperCamelCase__			:   Dict       				=  np.array(
          [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]							,   dtype=np.floataa						)
      self.assertTrue(np.allclose(output[:, :3, :3]							,   SCREAMING_SNAKE_CASE							,   atol=1e-4						)						)







# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


lowerCamelCase	:							Optional[Any]				=subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
lowerCamelCase	:							str				=subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

lowerCamelCase	:							List[Any]				='''|'''.join(sys.argv[1:])
lowerCamelCase	:							str				=re.compile(RF"""^({joined_dirs}).*?\.py$""")

lowerCamelCase	:							Optional[int]				=[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')