


import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_=None				)					->		List[Any]:
    _lowercase :							str   	=	None
    if token is not None:
        _lowercase :							Dict   	=	{'Accept': 'application/vnd.github+json', 'Authorization': F'''Bearer {token}'''}

    _lowercase :							Dict   	=	F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
    _lowercase :							Tuple   	=	requests.get(lowerCamelCase_							,				headers=lowerCamelCase_				).json()
    _lowercase :							Optional[Any]   	=	{}

    try:
        job_links.update({job['name']: job['html_url'] for job in result['jobs']}				)
        _lowercase :							Tuple   	=	math.ceil((result['total_count'] - 100) / 100				)

        for i in range(lowerCamelCase_				):
            _lowercase :							List[str]   	=	requests.get(url + F'''&page={i + 2}'''							,				headers=lowerCamelCase_				).json()
            job_links.update({job['name']: job['html_url'] for job in result['jobs']}				)

        return job_links
    except Exception:
        print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}'''				)

    return {}



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_=None				)					->		Dict:
    _lowercase :							Optional[Any]   	=	None
    if token is not None:
        _lowercase :							Any   	=	{'Accept': 'application/vnd.github+json', 'Authorization': F'''Bearer {token}'''}

    _lowercase :							Optional[int]   	=	F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'''
    _lowercase :							int   	=	requests.get(lowerCamelCase_							,				headers=lowerCamelCase_				).json()
    _lowercase :							Optional[int]   	=	{}

    try:
        artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}				)
        _lowercase :							Tuple   	=	math.ceil((result['total_count'] - 100) / 100				)

        for i in range(lowerCamelCase_				):
            _lowercase :							Union[str, Any]   	=	requests.get(url + F'''&page={i + 2}'''							,				headers=lowerCamelCase_				).json()
            artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}				)

        return artifacts
    except Exception:
        print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}'''				)

    return {}



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		int:
    _lowercase :							List[str]   	=	None
    if token is not None:
        _lowercase :							int   	=	{'Accept': 'application/vnd.github+json', 'Authorization': F'''Bearer {token}'''}

    _lowercase :							List[Any]   	=	requests.get(lowerCamelCase_							,				headers=lowerCamelCase_							,				allow_redirects=lowerCamelCase_				)
    _lowercase :							Optional[Any]   	=	result.headers['Location']
    _lowercase :							List[Any]   	=	requests.get(lowerCamelCase_							,				allow_redirects=lowerCamelCase_				)
    _lowercase :							Optional[int]   	=	os.path.join(lowerCamelCase_							,				F'''{artifact_name}.zip'''				)
    with open(lowerCamelCase_							,				'wb'				) as fp:
        fp.write(response.content				)



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_=None				)					->		Tuple:
    _lowercase :							Dict   	=	[]
    _lowercase :							str   	=	[]
    _lowercase :							str   	=	None

    with zipfile.ZipFile(lowerCamelCase_				) as z:
        for filename in z.namelist():
            if not os.path.isdir(lowerCamelCase_				):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(lowerCamelCase_				) as f:
                        for line in f:
                            _lowercase :							str   	=	line.decode('UTF-8'				).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    _lowercase :							str   	=	line[: line.index(': '				)]
                                    _lowercase :							Optional[int]   	=	line[line.index(': '				) + len(': '				) :]
                                    errors.append([error_line, error]				)
                                except Exception:
                                    # skip un-related lines
                                    pass
                            elif filename == "summary_short.txt" and line.startswith('FAILED '				):
                                # `test` is the test method that failed
                                _lowercase :							int   	=	line[len('FAILED '				) :]
                                failed_tests.append(lowerCamelCase_				)
                            elif filename == "job_name.txt":
                                _lowercase :							Tuple   	=	line

    if len(lowerCamelCase_				) != len(lowerCamelCase_				):
        raise ValueError(
            F'''`errors` and `failed_tests` should have the same number of elements. Got {len(lowerCamelCase_				)} for `errors` '''
            F'''and {len(lowerCamelCase_				)} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'''
            ' problem.'				)

    _lowercase :							Optional[Any]   	=	None
    if job_name and job_links:
        _lowercase :							Union[str, Any]   	=	job_links.get(lowerCamelCase_							,				lowerCamelCase_				)

    # A list with elements of the form (line of error, error, failed test)
    _lowercase :							Optional[Any]   	=	[x + [y] + [job_link] for x, y in zip(lowerCamelCase_							,				lowerCamelCase_				)]

    return result



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_=None				)					->		Dict:
    _lowercase :							Optional[int]   	=	[]

    _lowercase :							Optional[Any]   	=	[os.path.join(lowerCamelCase_							,				lowerCamelCase_				) for p in os.listdir(lowerCamelCase_				) if p.endswith('.zip'				)]
    for p in paths:
        errors.extend(get_errors_from_single_artifact(lowerCamelCase_							,				job_links=lowerCamelCase_				)				)

    return errors



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_=None				)					->		Optional[Any]:
    _lowercase :							Any   	=	Counter()
    counter.update([x[1] for x in logs]				)
    _lowercase :							Optional[Any]   	=	counter.most_common()
    _lowercase :							Optional[Any]   	=	{}
    for error, count in counts:
        if error_filter is None or error not in error_filter:
            _lowercase :							List[str]   	=	{'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

    _lowercase :							Union[str, Any]   	=	dict(sorted(r.items()							,				key=lambda lowerCamelCase_				: item[1]["count"]							,				reverse=lowerCamelCase_				)				)
    return r



def        UpperCamelCase_(   lowerCamelCase_				)					->		Optional[Any]:
    _lowercase :							Union[str, Any]   	=	test.split('::'				)[0]
    if test.startswith('tests/models/'				):
        _lowercase :							Optional[Any]   	=	test.split('/'				)[2]
    else:
        _lowercase :							Optional[int]   	=	None

    return test



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_=None				)					->		Tuple:
    _lowercase :							str   	=	[(x[0], x[1], get_model(x[2]				)) for x in logs]
    _lowercase :							Union[str, Any]   	=	[x for x in logs if x[2] is not None]
    _lowercase :							Dict   	=	{x[2] for x in logs}

    _lowercase :							Optional[Any]   	=	{}
    for test in tests:
        _lowercase :							Union[str, Any]   	=	Counter()
        # count by errors in `test`
        counter.update([x[1] for x in logs if x[2] == test]				)
        _lowercase :							Dict   	=	counter.most_common()
        _lowercase :							int   	=	{error: count for error, count in counts if (error_filter is None or error not in error_filter)}
        _lowercase :							int   	=	sum(error_counts.values()				)
        if n_errors > 0:
            _lowercase :							List[str]   	=	{'count': n_errors, 'errors': error_counts}

    _lowercase :							str   	=	dict(sorted(r.items()							,				key=lambda lowerCamelCase_				: item[1]["count"]							,				reverse=lowerCamelCase_				)				)
    return r



def        UpperCamelCase_(   lowerCamelCase_				)					->		List[Any]:
    _lowercase :							Dict   	=	'| no. | error | status |'
    _lowercase :							Any   	=	'|-:|:-|:-|'
    _lowercase :							Optional[Any]   	=	[header, sep]
    for error in reduced_by_error:
        _lowercase :							Optional[Any]   	=	reduced_by_error[error]['count']
        _lowercase :							List[str]   	=	F'''| {count} | {error[:100]} |  |'''
        lines.append(lowerCamelCase_				)

    return "\n".join(lowerCamelCase_				)



def        UpperCamelCase_(   lowerCamelCase_				)					->		List[Any]:
    _lowercase :							List[str]   	=	'| model | no. of errors | major error | count |'
    _lowercase :							List[Any]   	=	'|-:|-:|-:|-:|'
    _lowercase :							List[Any]   	=	[header, sep]
    for model in reduced_by_model:
        _lowercase :							Dict   	=	reduced_by_model[model]['count']
        _lowercase	,       _lowercase :							Optional[int]   	=	list(reduced_by_model[model]['errors'].items()				)[0]
        _lowercase :							Any   	=	F'''| {model} | {count} | {error[:60]} | {_count} |'''
        lines.append(lowerCamelCase_				)

    return "\n".join(lowerCamelCase_				)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE :   Any							      =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
 parser.add_argument(
     "--output_dir",
     type=str,
     required=True,
     help="Where to store the downloaded artifacts and other result files.",
 )
 parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
 SCREAMING_SNAKE_CASE :   Tuple							      =				parser.parse_args()

 os.makedirs(args.output_dir, exist_ok=True)

 SCREAMING_SNAKE_CASE :   Any							      =				get_job_links(args.workflow_run_id, token=args.token)
 SCREAMING_SNAKE_CASE :   Dict							      =				{}
 # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
 # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
 if _job_links:
  for k, v in _job_links.items():
   # This is how GitHub actions combine job names.
   if " / " in k:
    SCREAMING_SNAKE_CASE :   Optional[int]							      =				k.find(" / ")
    SCREAMING_SNAKE_CASE :   List[str]							      =				k[index + len(" / ") :]
   SCREAMING_SNAKE_CASE :   Any							      =				v
 with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
  json.dump(job_links, fp, ensure_ascii=False, indent=4)

 SCREAMING_SNAKE_CASE :   Dict							      =				get_artifacts_links(args.workflow_run_id, token=args.token)
 with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
  json.dump(artifacts, fp, ensure_ascii=False, indent=4)

 for idx, (name, url) in enumerate(artifacts.items()):
  download_artifact(name, url, args.output_dir, args.token)
  # Be gentle to GitHub
  time.sleep(1)

 SCREAMING_SNAKE_CASE :   Optional[int]							      =				get_all_errors(args.output_dir, job_links=job_links)

 # `e[1]` is the error
 SCREAMING_SNAKE_CASE :   Optional[int]							      =				Counter()
 counter.update([e[1] for e in errors])

 # print the top 30 most common test errors
 SCREAMING_SNAKE_CASE :   List[Any]							      =				counter.most_common(30)
 for item in most_common:
  print(item)

 with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
  json.dump(errors, fp, ensure_ascii=False, indent=4)

 SCREAMING_SNAKE_CASE :   Optional[Any]							      =				reduce_by_error(errors)
 SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				reduce_by_model(errors)

 SCREAMING_SNAKE_CASE :   Any							      =				make_github_table(reduced_by_error)
 SCREAMING_SNAKE_CASE :   Tuple							      =				make_github_table_per_model(reduced_by_model)

 with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
  fp.write(sa)
 with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
  fp.write(sa)






import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     DebertaVaForMaskedLM,
     DebertaVaForMultipleChoice,
     DebertaVaForQuestionAnswering,
     DebertaVaForSequenceClassification,
     DebertaVaForTokenClassification,
     DebertaVaModel,
 )
 from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST






class _lowerCamelCase(		_a     ):
       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=False,   lowerCamelCase=True,   lowerCamelCase="None",   lowerCamelCase=3,   lowerCamelCase=4,   lowerCamelCase=None,   ) ->     Optional[Any]:




           """simple docstring"""

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




           """simple docstring"""

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

           _lowercase :							Union[str, Any]   	=	None
           if self.use_input_mask:
               _lowercase :							int   	=	ids_tensor([self.batch_size, self.seq_length],   vocab_size=2)

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

           _lowercase :							Union[str, Any]   	=	None
           _lowercase :							Tuple   	=	None
           _lowercase :							str   	=	None
           if self.use_labels:
               _lowercase :							List[Any]   	=	ids_tensor([self.batch_size],   self.type_sequence_label_size)
               _lowercase :							Optional[int]   	=	ids_tensor([self.batch_size, self.seq_length],   self.num_labels)
               _lowercase :							str   	=	ids_tensor([self.batch_size],   self.num_choices)

           _lowercase :							Union[str, Any]   	=	self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           return DebertaVaConfig(
               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,   initializer_range=self.initializer_range,   relative_attention=self.relative_attention,   position_biased_input=self.position_biased_input,   pos_att_type=self.pos_att_type,   )
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     int:




           """simple docstring"""

           self.parent.assertListEqual(list(result.loss.size()),   [])
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							List[str]   	=	DebertaVaModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							int   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase)[0]
           _lowercase :							Optional[int]   	=	model(lowerCamelCase,   token_type_ids=lowerCamelCase)[0]
           _lowercase :							Dict   	=	model(lowerCamelCase)[0]

           self.parent.assertListEqual(list(sequence_output.size()),   [self.batch_size, self.seq_length, self.hidden_size])
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     List[Any]:




           """simple docstring"""

           _lowercase :							List[Any]   	=	DebertaVaForMaskedLM(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							str   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)

           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.vocab_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	self.num_labels
           _lowercase :							Any   	=	DebertaVaForSequenceClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Any   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertListEqual(list(result.logits.size()),   [self.batch_size, self.num_labels])
           self.check_loss_output(lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.num_labels
           _lowercase :							Optional[int]   	=	DebertaVaForTokenClassification(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Optional[Any]   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.num_labels))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	DebertaVaForQuestionAnswering(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Union[str, Any]   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   start_positions=lowerCamelCase,   end_positions=lowerCamelCase,   )
           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     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Any   	=	DebertaVaForMultipleChoice(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Optional[int]   	=	input_ids.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							List[Any]   	=	token_type_ids.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							int   	=	input_mask.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							str   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase,   )
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_choices))




       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

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






@require_torch
class _lowerCamelCase(		_a,				_a,				unittest.TestCase     ):
       lowercase_				:			Any									=    (
           (
               DebertaVaModel,
               DebertaVaForMaskedLM,
               DebertaVaForSequenceClassification,
               DebertaVaForTokenClassification,
               DebertaVaForQuestionAnswering,
               DebertaVaForMultipleChoice,
           )
           if is_torch_available()
           else ()
       )
       lowercase_				:			Any									=    (
           {
               """feature-extraction""": DebertaVaModel,
               """fill-mask""": DebertaVaForMaskedLM,
               """question-answering""": DebertaVaForQuestionAnswering,
               """text-classification""": DebertaVaForSequenceClassification,
               """token-classification""": DebertaVaForTokenClassification,
               """zero-shot""": DebertaVaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )

       lowercase_				:			int									=    True
       lowercase_				:			str									=    False
       lowercase_				:			str									=    False
       lowercase_				:			str									=    False
       lowercase_				:			List[Any]									=    False
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							List[Any]   	=	DebertaVaModelTester(self)
           _lowercase :							List[Any]   	=	ConfigTester(self,   config_class=lowerCamelCase,   hidden_size=37)
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_model(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							int   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							List[Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCamelCase)




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




           """simple docstring"""

           for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase :							Dict   	=	DebertaVaModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)






@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase(		unittest.TestCase     ):
       @unittest.skip(reason='Model not available yet')
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           pass




       @slow
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Dict   	=	DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge')

           _lowercase :							str   	=	torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]])
           _lowercase :							Any   	=	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
           with torch.no_grad():
               _lowercase :							Tuple   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase)[0]
           # compare the actual values for a slice.
           _lowercase :							int   	=	torch.tensor(
               [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]])
           self.assertTrue(torch.allclose(output[:, 1:4, 1:4],   lowerCamelCase,   atol=1E-4),   F'''{output[:, 1:4, 1:4]}''')


