


import unittest

import numpy as np

from transformers import RobertaConfig, 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():
 from transformers.models.roberta.modeling_flax_roberta import (
     FlaxRobertaForCausalLM,
     FlaxRobertaForMaskedLM,
     FlaxRobertaForMultipleChoice,
     FlaxRobertaForQuestionAnswering,
     FlaxRobertaForSequenceClassification,
     FlaxRobertaForTokenClassification,
     FlaxRobertaModel,
 )






class _lowerCamelCase(		unittest.TestCase     ):
       def __init__(      self,   lowerCamelCase,   lowerCamelCase=13,   lowerCamelCase=7,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=99,   lowerCamelCase=32,   lowerCamelCase=5,   lowerCamelCase=4,   lowerCamelCase=37,   lowerCamelCase="gelu",   lowerCamelCase=0.1,   lowerCamelCase=0.1,   lowerCamelCase=5_12,   lowerCamelCase=16,   lowerCamelCase=2,   lowerCamelCase=0.0_2,   lowerCamelCase=4,   ) ->     Dict:




           """simple docstring"""

           _lowercase :							List[str]   	=	parent
           _lowercase :							Tuple   	=	batch_size
           _lowercase :							Union[str, Any]   	=	seq_length
           _lowercase :							int   	=	is_training
           _lowercase :							str   	=	use_attention_mask
           _lowercase :							Any   	=	use_token_type_ids
           _lowercase :							List[Any]   	=	use_labels
           _lowercase :							List[Any]   	=	vocab_size
           _lowercase :							Optional[int]   	=	hidden_size
           _lowercase :							int   	=	num_hidden_layers
           _lowercase :							Tuple   	=	num_attention_heads
           _lowercase :							int   	=	intermediate_size
           _lowercase :							List[str]   	=	hidden_act
           _lowercase :							Union[str, Any]   	=	hidden_dropout_prob
           _lowercase :							List[Any]   	=	attention_probs_dropout_prob
           _lowercase :							Any   	=	max_position_embeddings
           _lowercase :							Optional[Any]   	=	type_vocab_size
           _lowercase :							Optional[Any]   	=	type_sequence_label_size
           _lowercase :							str   	=	initializer_range
           _lowercase :							str   	=	num_choices
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							Any   	=	ids_tensor([self.batch_size, self.seq_length],   self.vocab_size)

           _lowercase :							Tuple   	=	None
           if self.use_attention_mask:
               _lowercase :							Optional[int]   	=	random_attention_mask([self.batch_size, self.seq_length])

           _lowercase :							Optional[Any]   	=	None
           if self.use_token_type_ids:
               _lowercase :							List[str]   	=	ids_tensor([self.batch_size, self.seq_length],   self.type_vocab_size)

           _lowercase :							Tuple   	=	RobertaConfig(
               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=lowerCamelCase,   initializer_range=self.initializer_range,   )

           return config, input_ids, token_type_ids, attention_mask
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							List[Any]   	=	self.prepare_config_and_inputs()
           _lowercase	,       _lowercase	,       _lowercase	,       _lowercase :							Optional[int]   	=	config_and_inputs
           _lowercase :							List[Any]   	=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
           return config, inputs_dict




       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.prepare_config_and_inputs()
           _lowercase	,       _lowercase	,       _lowercase	,       _lowercase :							Optional[Any]   	=	config_and_inputs

           _lowercase :							Union[str, Any]   	=	True
           _lowercase :							Dict   	=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
           _lowercase :							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
class _lowerCamelCase(		_a,				unittest.TestCase     ):
       lowercase_				:			Union[str, Any]									=    True

       lowercase_				:			int									=    (
           (
               FlaxRobertaModel,
               FlaxRobertaForCausalLM,
               FlaxRobertaForMaskedLM,
               FlaxRobertaForSequenceClassification,
               FlaxRobertaForTokenClassification,
               FlaxRobertaForMultipleChoice,
               FlaxRobertaForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase :							List[Any]   	=	FlaxRobertaModelTester(self)




       @slow
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           for model_class_name in self.all_model_classes:
               _lowercase :							List[str]   	=	model_class_name.from_pretrained('roberta-base',   from_pt=lowerCamelCase)
               _lowercase :							Optional[int]   	=	model(np.ones((1, 1)))
               self.assertIsNotNone(lowerCamelCase)






# 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


SCREAMING_SNAKE_CASE :   List[Any]							      =				subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
SCREAMING_SNAKE_CASE :   Optional[int]							      =				(
    subprocess.check_output(F"git diff --diff-filter=d --name-only {fork_point_sha}".split()).decode("utf-8").split()
)

SCREAMING_SNAKE_CASE :   Any							      =				"|".join(sys.argv[1:])
SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				re.compile(rF"^({joined_dirs}).*?\.py$")

SCREAMING_SNAKE_CASE :   List[Any]							      =				[x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")


