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

import requests



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	None
    if token is not None:
        __UpperCamelCase  :Dict    						=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}

    __UpperCamelCase  :List[str]    						=	f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
    __UpperCamelCase  :Optional[int]    						=	requests.get(a__						,    headers=a__			).json()
    __UpperCamelCase  :Dict    						=	{}

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

        for i in range(a__			):
            __UpperCamelCase  :str    						=	requests.get(url + f"""&page={i + 2}"""						,    headers=a__			).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 lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	None
    if token is not None:
        __UpperCamelCase  :Tuple    						=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}

    __UpperCamelCase  :Any    						=	f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
    __UpperCamelCase  :int    						=	requests.get(a__						,    headers=a__			).json()
    __UpperCamelCase  :Any    						=	{}

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

        for i in range(a__			):
            __UpperCamelCase  :Tuple    						=	requests.get(url + f"""&page={i + 2}"""						,    headers=a__			).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 lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	None
    if token is not None:
        __UpperCamelCase  :List[str]    						=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}

    __UpperCamelCase  :Any    						=	requests.get(a__						,    headers=a__						,    allow_redirects=a__			)
    __UpperCamelCase  :Tuple    						=	result.headers['''Location''']
    __UpperCamelCase  :Tuple    						=	requests.get(a__						,    allow_redirects=a__			)
    __UpperCamelCase  :List[Any]    						=	os.path.join(a__						,    f"""{artifact_name}.zip"""			)
    with open(a__						,    '''wb'''			) as fp:
        fp.write(response.content			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	[]
    __UpperCamelCase  :Optional[int]    						=	[]
    __UpperCamelCase  :Any    						=	None

    with zipfile.ZipFile(a__			) as z:
        for filename in z.namelist():
            if not os.path.isdir(a__			):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(a__			) as f:
                        for line in f:
                            __UpperCamelCase  :Any    						=	line.decode('''UTF-8'''			).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    __UpperCamelCase  :Optional[int]    						=	line[: line.index(''': '''			)]
                                    __UpperCamelCase  :Optional[Any]    						=	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
                                __UpperCamelCase  :Optional[int]    						=	line[len('''FAILED '''			) :]
                                failed_tests.append(a__			)
                            elif filename == "job_name.txt":
                                __UpperCamelCase  :Tuple    						=	line

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

    __UpperCamelCase  :List[Any]    						=	None
    if job_name and job_links:
        __UpperCamelCase  :Dict    						=	job_links.get(a__						,    a__			)

    # A list with elements of the form (line of error, error, failed test)
    __UpperCamelCase  :str    						=	[x + [y] + [job_link] for x, y in zip(a__						,    a__			)]

    return result



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	[]

    __UpperCamelCase  :int    						=	[os.path.join(a__						,    a__			) for p in os.listdir(a__			) if p.endswith('''.zip'''			)]
    for p in paths:
        errors.extend(get_errors_from_single_artifact(a__						,    job_links=a__			)			)

    return errors



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	Counter()
    counter.update([x[1] for x in logs]			)
    __UpperCamelCase  :str    						=	counter.most_common()
    __UpperCamelCase  :Union[str, Any]    						=	{}
    for error, count in counts:
        if error_filter is None or error not in error_filter:
            __UpperCamelCase  :Union[str, Any]    						=	{'''count''': count, '''failed_tests''': [(x[2], x[0]) for x in logs if x[1] == error]}

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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	test.split('''::'''			)[0]
    if test.startswith('''tests/models/'''			):
        __UpperCamelCase  :Tuple    						=	test.split('''/'''			)[2]
    else:
        __UpperCamelCase  :Optional[Any]    						=	None

    return test



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	[(x[0], x[1], get_model(x[2]			)) for x in logs]
    __UpperCamelCase  :int    						=	[x for x in logs if x[2] is not None]
    __UpperCamelCase  :Optional[int]    						=	{x[2] for x in logs}

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

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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	'''| no. | error | status |'''
    __UpperCamelCase  :Tuple    						=	'''|-:|:-|:-|'''
    __UpperCamelCase  :Optional[Any]    						=	[header, sep]
    for error in reduced_by_error:
        __UpperCamelCase  :Tuple    						=	reduced_by_error[error]['''count''']
        __UpperCamelCase  :List[Any]    						=	f"""| {count} | {error[:100]} |  |"""
        lines.append(a__			)

    return "\n".join(a__			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	'''| model | no. of errors | major error | count |'''
    __UpperCamelCase  :Dict    						=	'''|-:|-:|-:|-:|'''
    __UpperCamelCase  :Optional[int]    						=	[header, sep]
    for model in reduced_by_model:
        __UpperCamelCase  :Any    						=	reduced_by_model[model]['''count''']
        __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	list(reduced_by_model[model]['''errors'''].items()			)[0]
        __UpperCamelCase  :List[str]    						=	f"""| {model} | {count} | {error[:60]} | {_count} |"""
        lines.append(a__			)

    return "\n".join(a__			)


if __name__ == "__main__":
    __lowercase											=      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.''')
    __lowercase											=      parser.parse_args()

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

    __lowercase											=      get_job_links(args.workflow_run_id, token=args.token)
    __lowercase											=      {}
    # 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:
                __lowercase											=      k.find(''' / ''')
                __lowercase											=      k[index + len(''' / ''') :]
            __lowercase											=      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)

    __lowercase											=      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)

    __lowercase											=      get_all_errors(args.output_dir, job_links=job_links)

    # `e[1]` is the error
    __lowercase											=      Counter()
    counter.update([e[1] for e in errors])

    # print the top 30 most common test errors
    __lowercase											=      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)

    __lowercase											=      reduce_by_error(errors)
    __lowercase											=      reduce_by_model(errors)

    __lowercase											=      make_github_table(reduced_by_error)
    __lowercase											=      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 MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
    import torch

    from transformers import (
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraModel,
    )
    from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST


class 			lowerCamelCase_  :






    '''simple docstring'''






    def __init__(			self				,				__lowercase				,				__lowercase=2				,				__lowercase=8				,				__lowercase=True				,				__lowercase=True				,				__lowercase=True				,				__lowercase=True				,				__lowercase=99				,				__lowercase=16				,				__lowercase=5				,				__lowercase=2				,				__lowercase=36				,				__lowercase="gelu"				,				__lowercase=0.0				,				__lowercase=0.0				,				__lowercase=512				,				__lowercase=16				,				__lowercase=2				,				__lowercase=0.02				,				__lowercase=3				,				__lowercase=4				,				__lowercase=None				,				) ->			List[str]:
        __UpperCamelCase  :Union[str, Any]    						=	parent
        __UpperCamelCase  :str    						=	batch_size
        __UpperCamelCase  :Union[str, Any]    						=	seq_length
        __UpperCamelCase  :Optional[Any]    						=	is_training
        __UpperCamelCase  :Union[str, Any]    						=	use_input_mask
        __UpperCamelCase  :Any    						=	use_token_type_ids
        __UpperCamelCase  :List[str]    						=	use_labels
        __UpperCamelCase  :Tuple    						=	vocab_size
        __UpperCamelCase  :Tuple    						=	hidden_size
        __UpperCamelCase  :Optional[Any]    						=	num_hidden_layers
        __UpperCamelCase  :Tuple    						=	num_attention_heads
        __UpperCamelCase  :Any    						=	intermediate_size
        __UpperCamelCase  :Optional[Any]    						=	hidden_act
        __UpperCamelCase  :Any    						=	hidden_dropout_prob
        __UpperCamelCase  :str    						=	attention_probs_dropout_prob
        __UpperCamelCase  :Optional[Any]    						=	max_position_embeddings
        __UpperCamelCase  :int    						=	type_vocab_size
        __UpperCamelCase  :Optional[int]    						=	type_sequence_label_size
        __UpperCamelCase  :Any    						=	initializer_range
        __UpperCamelCase  :List[str]    						=	num_labels
        __UpperCamelCase  :Dict    						=	num_choices
        __UpperCamelCase  :Union[str, Any]    						=	scope







    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :int    						=	ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size)

        __UpperCamelCase  :List[Any]    						=	None
        if self.use_input_mask:
            __UpperCamelCase  :Any    						=	random_attention_mask([self.batch_size, self.seq_length])

        __UpperCamelCase  :Union[str, Any]    						=	None
        if self.use_token_type_ids:
            __UpperCamelCase  :Optional[int]    						=	ids_tensor([self.batch_size, self.seq_length]				,				self.type_vocab_size)

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

        __UpperCamelCase  :List[str]    						=	self.get_config()

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







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        return MraConfig(
            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=__lowercase				,				initializer_range=self.initializer_range				,				)







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :Dict    						=	self.get_config()
        __UpperCamelCase  :List[str]    						=	300
        return config







    def     UpperCamelCase__     (			self) ->			int:
        (
            (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, 
        )  :Tuple    						=	self.prepare_config_and_inputs()

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

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase) ->			int:
        __UpperCamelCase  :int    						=	MraModel(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase  :str    						=	model(__lowercase				,				attention_mask=__lowercase				,				token_type_ids=__lowercase)
        __UpperCamelCase  :Tuple    						=	model(__lowercase				,				token_type_ids=__lowercase)
        __UpperCamelCase  :str    						=	model(__lowercase)
        self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size))







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				) ->			List[Any]:
        __UpperCamelCase  :Tuple    						=	True
        __UpperCamelCase  :Dict    						=	MraModel(__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase  :int    						=	model(
            __lowercase				,				attention_mask=__lowercase				,				token_type_ids=__lowercase				,				encoder_hidden_states=__lowercase				,				encoder_attention_mask=__lowercase				,				)
        __UpperCamelCase  :Any    						=	model(
            __lowercase				,				attention_mask=__lowercase				,				token_type_ids=__lowercase				,				encoder_hidden_states=__lowercase				,				)
        __UpperCamelCase  :str    						=	model(__lowercase				,				attention_mask=__lowercase				,				token_type_ids=__lowercase)
        self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size))







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase) ->			Union[str, Any]:
        __UpperCamelCase  :Dict    						=	MraForMaskedLM(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase  :Optional[Any]    						=	model(__lowercase				,				attention_mask=__lowercase				,				token_type_ids=__lowercase				,				labels=__lowercase)
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size))







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase) ->			int:
        __UpperCamelCase  :Tuple    						=	MraForQuestionAnswering(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase  :Any    						=	model(
            __lowercase				,				attention_mask=__lowercase				,				token_type_ids=__lowercase				,				start_positions=__lowercase				,				end_positions=__lowercase				,				)
        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				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase) ->			List[str]:
        __UpperCamelCase  :List[Any]    						=	self.num_labels
        __UpperCamelCase  :str    						=	MraForSequenceClassification(__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase  :Dict    						=	model(__lowercase				,				attention_mask=__lowercase				,				token_type_ids=__lowercase				,				labels=__lowercase)
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_labels))







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase) ->			Optional[int]:
        __UpperCamelCase  :str    						=	self.num_labels
        __UpperCamelCase  :Optional[int]    						=	MraForTokenClassification(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase  :Optional[Any]    						=	model(__lowercase				,				attention_mask=__lowercase				,				token_type_ids=__lowercase				,				labels=__lowercase)
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.num_labels))







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase) ->			Optional[Any]:
        __UpperCamelCase  :Optional[Any]    						=	self.num_choices
        __UpperCamelCase  :Dict    						=	MraForMultipleChoice(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase  :Tuple    						=	input_ids.unsqueeze(1).expand(-1				,				self.num_choices				,				-1).contiguous()
        __UpperCamelCase  :int    						=	token_type_ids.unsqueeze(1).expand(-1				,				self.num_choices				,				-1).contiguous()
        __UpperCamelCase  :Optional[int]    						=	input_mask.unsqueeze(1).expand(-1				,				self.num_choices				,				-1).contiguous()
        __UpperCamelCase  :Union[str, Any]    						=	model(
            __lowercase				,				attention_mask=__lowercase				,				token_type_ids=__lowercase				,				labels=__lowercase				,				)
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_choices))







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :str    						=	self.prepare_config_and_inputs()
        (
            (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, (
            __UpperCamelCase
        )						, 
        )  :str    						=	config_and_inputs
        __UpperCamelCase  :List[str]    						=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
        return config, inputs_dict


@require_torch
class 			lowerCamelCase_  (     UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		List[str]      			=   (
        (
            MraModel,
            MraForMaskedLM,
            MraForMultipleChoice,
            MraForQuestionAnswering,
            MraForSequenceClassification,
            MraForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    a__			:		Optional[int]      			=   False
    a__			:		Optional[int]      			=   False
    a__			:		str      			=   False
    a__			:		Optional[Any]      			=   False

    a__			:		Union[str, Any]      			=   ()







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :str    						=	MraModelTester(self)
        __UpperCamelCase  :Tuple    						=	ConfigTester(self				,				config_class=__lowercase				,				hidden_size=37)







    def     UpperCamelCase__     (			self) ->			Dict:
        self.config_tester.run_common_tests()







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :List[str]    						=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase)







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :Union[str, Any]    						=	self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            __UpperCamelCase  :Optional[int]    						=	type
            self.model_tester.create_and_check_model(*__lowercase)







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :Union[str, Any]    						=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__lowercase)







    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :Dict    						=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__lowercase)







    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Union[str, Any]    						=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__lowercase)







    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :Optional[int]    						=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__lowercase)







    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :Dict    						=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__lowercase)







    @slow
    def     UpperCamelCase__     (			self) ->			Dict:
        for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __UpperCamelCase  :List[Any]    						=	MraModel.from_pretrained(__lowercase)
            self.assertIsNotNone(__lowercase)







    @unittest.skip(reason='''MRA does not output attentions''')
    def     UpperCamelCase__     (			self) ->			Any:
        return


@require_torch
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    @slow
    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :int    						=	MraModel.from_pretrained('''uw-madison/mra-base-512-4''')
        __UpperCamelCase  :Union[str, Any]    						=	torch.arange(256).unsqueeze(0)

        with torch.no_grad():
            __UpperCamelCase  :List[Any]    						=	model(__lowercase)[0]

        __UpperCamelCase  :Dict    						=	torch.Size((1, 256, 768))
        self.assertEqual(output.shape				,				__lowercase)

        __UpperCamelCase  :Optional[Any]    						=	torch.tensor(
            [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]])

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







    @slow
    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :str    						=	MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''')
        __UpperCamelCase  :str    						=	torch.arange(256).unsqueeze(0)

        with torch.no_grad():
            __UpperCamelCase  :Optional[Any]    						=	model(__lowercase)[0]

        __UpperCamelCase  :List[str]    						=	50_265

        __UpperCamelCase  :List[Any]    						=	torch.Size((1, 256, vocab_size))
        self.assertEqual(output.shape				,				__lowercase)

        __UpperCamelCase  :Optional[Any]    						=	torch.tensor(
            [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]])

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







    @slow
    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :Dict    						=	MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''')
        __UpperCamelCase  :Optional[int]    						=	torch.arange(4_096).unsqueeze(0)

        with torch.no_grad():
            __UpperCamelCase  :Tuple    						=	model(__lowercase)[0]

        __UpperCamelCase  :Optional[int]    						=	50_265

        __UpperCamelCase  :Optional[Any]    						=	torch.Size((1, 4_096, vocab_size))
        self.assertEqual(output.shape				,				__lowercase)

        __UpperCamelCase  :List[str]    						=	torch.tensor(
            [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]])

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





