





'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
      )


class       a_  :

    '''simple docstring'''


    def __init__(	self				,	A				,	A=13				,	A=7				,	A=True				,	A=True				,	A=True				,	A=True				,	A=99				,	A=32				,	A=2				,	A=4				,	A=37				,	A="gelu"				,	A=0.1				,	A=0.1				,	A=512				,	A=16				,	A=2				,	A=0.02				,	A=3				,	A=4				,	A=None				,	)	->     Tuple:
        _SCREAMING_SNAKE_CASE             =    parent
        _SCREAMING_SNAKE_CASE             =    13
        _SCREAMING_SNAKE_CASE             =    7
        _SCREAMING_SNAKE_CASE             =    True
        _SCREAMING_SNAKE_CASE             =    True
        _SCREAMING_SNAKE_CASE             =    True
        _SCREAMING_SNAKE_CASE             =    True
        _SCREAMING_SNAKE_CASE             =    99
        _SCREAMING_SNAKE_CASE             =    32
        _SCREAMING_SNAKE_CASE             =    2
        _SCREAMING_SNAKE_CASE             =    4
        _SCREAMING_SNAKE_CASE             =    37
        _SCREAMING_SNAKE_CASE             =    """gelu"""
        _SCREAMING_SNAKE_CASE             =    0.1
        _SCREAMING_SNAKE_CASE             =    0.1
        _SCREAMING_SNAKE_CASE             =    512
        _SCREAMING_SNAKE_CASE             =    16
        _SCREAMING_SNAKE_CASE             =    False
        _SCREAMING_SNAKE_CASE             =    -1
        _SCREAMING_SNAKE_CASE             =    True
        _SCREAMING_SNAKE_CASE             =    2
        _SCREAMING_SNAKE_CASE             =    0.02
        _SCREAMING_SNAKE_CASE             =    3
        _SCREAMING_SNAKE_CASE             =    4
        _SCREAMING_SNAKE_CASE             =    None


    def     snake_case_(	self )	->     List[Any]:
        _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size, self.seq_length]				,	self.vocab_size )

        _SCREAMING_SNAKE_CASE             =    None
        if self.use_input_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             =    None
        _SCREAMING_SNAKE_CASE             =    None
        _SCREAMING_SNAKE_CASE             =    None
        if self.use_labels:
            _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size]				,	self.type_sequence_label_size )
            _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size, self.seq_length]				,	self.num_labels )

        _SCREAMING_SNAKE_CASE             =    DebertaConfig(
            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				,	relative_attention=self.relative_attention				,	max_relative_positions=self.max_relative_positions				,	position_biased_input=self.position_biased_input				,	initializer_range=self.initializer_range				,	return_dict=A				,	)

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


    def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A )	->     Any:
        _SCREAMING_SNAKE_CASE             =    TFDebertaModel(config=A )
        _SCREAMING_SNAKE_CASE             =    {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

        _SCREAMING_SNAKE_CASE             =    [input_ids, input_mask]
        _SCREAMING_SNAKE_CASE             =    model(A )

        _SCREAMING_SNAKE_CASE             =    model(A )

        self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.seq_length, self.hidden_size) )


    def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A )	->     int:
        _SCREAMING_SNAKE_CASE             =    TFDebertaForMaskedLM(config=A )
        _SCREAMING_SNAKE_CASE             =    {
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }
        _SCREAMING_SNAKE_CASE             =    model(A )
        self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.vocab_size) )


    def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A )	->     List[str]:
        _SCREAMING_SNAKE_CASE             =    self.num_labels
        _SCREAMING_SNAKE_CASE             =    TFDebertaForSequenceClassification(config=A )
        _SCREAMING_SNAKE_CASE             =    {
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }

        _SCREAMING_SNAKE_CASE             =    model(A )
        self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_labels) )


    def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A )	->     Optional[int]:
        _SCREAMING_SNAKE_CASE             =    self.num_labels
        _SCREAMING_SNAKE_CASE             =    TFDebertaForTokenClassification(config=A )
        _SCREAMING_SNAKE_CASE             =    {
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }
        _SCREAMING_SNAKE_CASE             =    model(A )
        self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.num_labels) )


    def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A )	->     Optional[Any]:
        _SCREAMING_SNAKE_CASE             =    TFDebertaForQuestionAnswering(config=A )
        _SCREAMING_SNAKE_CASE             =    {
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }

        _SCREAMING_SNAKE_CASE             =    model(A )
        self.parent.assertEqual(result.start_logits.shape				,	(self.batch_size, self.seq_length) )
        self.parent.assertEqual(result.end_logits.shape				,	(self.batch_size, self.seq_length) )





    def     snake_case_(	self )	->     List[str]:
        _SCREAMING_SNAKE_CASE             =    self.prepare_config_and_inputs()
        (
            (
            _SCREAMING_SNAKE_CASE
        )       , (
            _SCREAMING_SNAKE_CASE
        )       , (
            _SCREAMING_SNAKE_CASE
        )       , (
            _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""": input_mask}
        return config, inputs_dict




@require_tf
class       a_  (       snake_case_   ,					snake_case_   ,					unittest.TestCase ):

    '''simple docstring'''
    UpperCamelCase									=				(
        (
            TFDebertaModel,
            TFDebertaForMaskedLM,
            TFDebertaForQuestionAnswering,
            TFDebertaForSequenceClassification,
            TFDebertaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    UpperCamelCase									=				(
        {
            '''feature-extraction''': TFDebertaModel,
            '''fill-mask''': TFDebertaForMaskedLM,
            '''question-answering''': TFDebertaForQuestionAnswering,
            '''text-classification''': TFDebertaForSequenceClassification,
            '''token-classification''': TFDebertaForTokenClassification,
            '''zero-shot''': TFDebertaForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    UpperCamelCase									=				False
    UpperCamelCase									=				False


    def     snake_case_(	self )	->     Tuple:
        _SCREAMING_SNAKE_CASE             =    TFDebertaModelTester(self )
        _SCREAMING_SNAKE_CASE             =    ConfigTester(self				,	config_class=A				,	hidden_size=37 )


    def     snake_case_(	self )	->     Optional[int]:
        self.config_tester.run_common_tests()


    def     snake_case_(	self )	->     Optional[int]:
        _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A )


    def     snake_case_(	self )	->     Optional[int]:
        _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*A )


    def     snake_case_(	self )	->     str:
        _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*A )


    def     snake_case_(	self )	->     List[str]:
        _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*A )


    def     snake_case_(	self )	->     List[str]:
        _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*A )





    @slow
    def     snake_case_(	self )	->     Tuple:
        _SCREAMING_SNAKE_CASE             =    TFDebertaModel.from_pretrained("""kamalkraj/deberta-base""" )
        self.assertIsNotNone(A )




@require_tf
class       a_  (       unittest.TestCase ):

    '''simple docstring'''


    @unittest.skip(reason="""Model not available yet""" )
    def     snake_case_(	self )	->     List[str]:
        pass





    @slow
    def     snake_case_(	self )	->     str:
        _SCREAMING_SNAKE_CASE             =    TFDebertaModel.from_pretrained("""kamalkraj/deberta-base""" )
        _SCREAMING_SNAKE_CASE             =    tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
        _SCREAMING_SNAKE_CASE             =    tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
        _SCREAMING_SNAKE_CASE             =    model(A				,	attention_mask=A )[0]

        _SCREAMING_SNAKE_CASE             =    tf.constant(
            [
                [
                    [-0.5985_5896, -0.8055_2566, -0.846_2135],
                    [1.448_4025, -0.9348_3794, -0.8059_3085],
                    [0.312_2741, 0.0031_6059, -1.413_1377],
                ]
            ] )
        tf.debugging.assert_near(output[:, 1:4, 1:4]				,	A				,	atol=1e-4 )










'''simple docstring'''

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List





def lowerCamelCase		(  __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					str					)		->Tuple:
    if name != "training_function" and name != "main":
        raise ValueError(F'Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''					)
    _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    [], False
    for line in lines:
        if not found_start and F'def {name}' in line:
            _SCREAMING_SNAKE_CASE             =    True
            good_lines.append(__lowerCamelCase					)
            continue
        if found_start:
            if name == "training_function" and "def main" in line:
                return good_lines
            if name == "main" and "if __name__" in line:
                return good_lines
            good_lines.append(__lowerCamelCase					)





def lowerCamelCase		(  __lowerCamelCase				:					List[str]					)		->List[str]:
    return [line for line in lines if not line.lstrip().startswith("""#"""					) and line != "\n"]






def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					str      , __lowerCamelCase				:					bool      , __lowerCamelCase				:					str = None					)		->List[str]:
    with open(__lowerCamelCase      , """r"""					) as f:
        _SCREAMING_SNAKE_CASE             =    f.readlines()
    with open(os.path.abspath(os.path.join("""examples"""      , """nlp_example.py"""					)					)      , """r"""					) as f:
        _SCREAMING_SNAKE_CASE             =    f.readlines()
    with open(__lowerCamelCase      , """r"""					) as f:
        _SCREAMING_SNAKE_CASE             =    f.readlines()
    if secondary_filename is not None:
        with open(__lowerCamelCase      , """r"""					) as f:
            _SCREAMING_SNAKE_CASE             =    f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
    if parser_only:
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
        if secondary_filename is not None:
            _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
    else:
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
        if secondary_filename is not None:
            _SCREAMING_SNAKE_CASE             =    clean_lines(
                get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)

    _SCREAMING_SNAKE_CASE             =    """train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

    # Specific code in our script that differs from the full version, aka what is new
    _SCREAMING_SNAKE_CASE             =    []
    _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
    _SCREAMING_SNAKE_CASE             =    iter(__lowerCamelCase					)
    for i in range(len(__lowerCamelCase					) - 1					):
        if i not in passed_idxs:
            _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)
            if (line not in full_file_func) and (line.lstrip() != _dl_line):
                if "TESTING_MOCKED_DATALOADERS" not in line:
                    new_feature_code.append(__lowerCamelCase					)
                    passed_idxs.append(__lowerCamelCase					)
                else:
                    # Skip over the `config['num_epochs'] = 2` statement
                    _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)

    # Extract out just the new parts from the full_file_training_func
    _SCREAMING_SNAKE_CASE             =    []
    _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
    for i, line in enumerate(__lowerCamelCase					):
        if i not in passed_idxs:
            if (line not in full_file_func) and (line.lstrip() != _dl_line):
                if "TESTING_MOCKED_DATALOADERS" not in line:
                    new_full_example_parts.append(__lowerCamelCase					)
                    passed_idxs.append(__lowerCamelCase					)

    # Finally, get the overall diff
    _SCREAMING_SNAKE_CASE             =    [line for line in new_feature_code if line not in new_full_example_parts]
    if secondary_filename is not None:
        _SCREAMING_SNAKE_CASE             =    [line for line in full_file_contents if line not in secondary_file_func]
        _SCREAMING_SNAKE_CASE             =    [line for line in diff_from_example if line not in diff_from_two]

    return diff_from_example



