
"""simple docstring"""



import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class 		a_						( _UpperCAmelCase     ,						_UpperCAmelCase     ,						unittest.TestCase	):
    a      :				Any     =				AutoencoderKL
    a      :				Dict     =				'sample'
    a      :				List[str]     =				1e-2







    @property
    def      _snake_case					(			self			:       Any     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   4
        _UpperCAmelCase          =   3
        _UpperCAmelCase          =   (32, 32)

        _UpperCAmelCase          =   floats_tensor((batch_size, num_channels) + sizes     ).to(__UpperCamelCase     )

        return {"sample": image}







    @property
    def      _snake_case					(			self			:       Tuple     )		->Optional[int]:






        '''simple docstring'''






        return (3, 32, 32)







    @property
    def      _snake_case					(			self			:       Tuple     )		->Any:






        '''simple docstring'''






        return (3, 32, 32)







    def      _snake_case					(			self			:       Dict     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   {
            """block_out_channels""": [32, 64],
            """in_channels""": 3,
            """out_channels""": 3,
            """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
            """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
            """latent_channels""": 4,
        }
        _UpperCAmelCase          =   self.dummy_input
        return init_dict, inputs_dict







    def      _snake_case					(			self			:       int     )		->Any:






        '''simple docstring'''






        pass







    def      _snake_case					(			self			:       Union[str, Any]     )		->Optional[Any]:






        '''simple docstring'''






        pass







    @unittest.skipIf(torch_device == """mps"""     ,					"""Gradient checkpointing skipped on MPS"""     )
    def      _snake_case					(			self			:       Optional[int]     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.prepare_init_args_and_inputs_for_common()
        _UpperCAmelCase          =   self.model_class(**__UpperCamelCase     )
        model.to(__UpperCamelCase     )

        assert not model.is_gradient_checkpointing and model.training

        _UpperCAmelCase          =   model(**__UpperCamelCase     ).sample
        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
        # we won't calculate the loss and rather backprop on out.sum()
        model.zero_grad()

        _UpperCAmelCase          =   torch.randn_like(__UpperCamelCase     )
        _UpperCAmelCase          =   (out - labels).mean()
        loss.backward()

        # re-instantiate the model now enabling gradient checkpointing
        _UpperCAmelCase          =   self.model_class(**__UpperCamelCase     )
        # clone model
        model_a.load_state_dict(model.state_dict()     )
        model_a.to(__UpperCamelCase     )
        model_a.enable_gradient_checkpointing()

        assert model_a.is_gradient_checkpointing and model_a.training

        _UpperCAmelCase          =   model_a(**__UpperCamelCase     ).sample
        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
        # we won't calculate the loss and rather backprop on out.sum()
        model_a.zero_grad()
        _UpperCAmelCase          =   (out_a - labels).mean()
        loss_a.backward()

        # compare the output and parameters gradients
        self.assertTrue((loss - loss_a).abs() < 1e-5     )
        _UpperCAmelCase          =   dict(model.named_parameters()     )
        _UpperCAmelCase          =   dict(model_a.named_parameters()     )
        for name, param in named_params.items():
            self.assertTrue(torch_all_close(param.grad.data     ,					named_params_a[name].grad.data     ,					atol=5e-5     )     )







    def      _snake_case					(			self			:       int     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""     ,					output_loading_info=__UpperCamelCase     )
        self.assertIsNotNone(__UpperCamelCase     )
        self.assertEqual(len(loading_info["""missing_keys"""]     )     ,					0     )

        model.to(__UpperCamelCase     )
        _UpperCAmelCase          =   model(**self.dummy_input     )

        assert image is not None, "Make sure output is not None"







    def      _snake_case					(			self			:       Union[str, Any]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""     )
        _UpperCAmelCase          =   model.to(__UpperCamelCase     )
        model.eval()

        if torch_device == "mps":
            _UpperCAmelCase          =   torch.manual_seed(0     )
        else:
            _UpperCAmelCase          =   torch.Generator(device=__UpperCamelCase     ).manual_seed(0     )

        _UpperCAmelCase          =   torch.randn(
            1     ,					model.config.in_channels     ,					model.config.sample_size     ,					model.config.sample_size     ,					generator=torch.manual_seed(0     )     ,					)
        _UpperCAmelCase          =   image.to(__UpperCamelCase     )
        with torch.no_grad():
            _UpperCAmelCase          =   model(__UpperCamelCase     ,					sample_posterior=__UpperCamelCase     ,					generator=__UpperCamelCase     ).sample

        _UpperCAmelCase          =   output[0, -1, -3:, -3:].flatten().cpu()

        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
        # the expected output slices are not the same for CPU and GPU.
        if torch_device == "mps":
            _UpperCAmelCase          =   torch.tensor(
                [
                    -4.0078e-01,
                    -3.8323e-04,
                    -1.2681e-01,
                    -1.1462e-01,
                    2.0095e-01,
                    1.0893e-01,
                    -8.8247e-02,
                    -3.0361e-01,
                    -9.8644e-03,
                ]     )
        elif torch_device == "cpu":
            _UpperCAmelCase          =   torch.tensor(
                [-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6]     )
        else:
            _UpperCAmelCase          =   torch.tensor(
                [-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5]     )

        self.assertTrue(torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					rtol=1e-2     )     )







@slow
class 		a_						( unittest.TestCase	):







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       str     )		->str:






        '''simple docstring'''






        return f"""gaussian_noise_s={seed}_shape={"_".join([str(__UpperCamelCase     ) for s in shape]     )}.npy"""







    def      _snake_case					(			self			:       List[str]     )		->str:






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       Tuple=0     ,					__UpperCamelCase			:       List[Any]=(4, 3, 5_12, 5_12)     ,					__UpperCamelCase			:       Optional[Any]=False     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   torch.floataa if fpaa else torch.floataa
        _UpperCAmelCase          =   torch.from_numpy(load_hf_numpy(self.get_file_format(__UpperCamelCase     ,					__UpperCamelCase     )     )     ).to(__UpperCamelCase     ).to(__UpperCamelCase     )
        return image







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       Optional[int]="CompVis/stable-diffusion-v1-4"     ,					__UpperCamelCase			:       int=False     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   """fp16""" if fpaa else None
        _UpperCAmelCase          =   torch.floataa if fpaa else torch.floataa

        _UpperCAmelCase          =   AutoencoderKL.from_pretrained(
            __UpperCamelCase     ,					subfolder="""vae"""     ,					torch_dtype=__UpperCamelCase     ,					revision=__UpperCamelCase     ,					)
        model.to(__UpperCamelCase     ).eval()

        return model







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       Tuple=0     )		->Optional[int]:






        '''simple docstring'''






        if torch_device == "mps":
            return torch.manual_seed(__UpperCamelCase     )
        return torch.Generator(device=__UpperCamelCase     ).manual_seed(__UpperCamelCase     )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
            [47, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
            # fmt: on
        ]     )
    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Any     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model()
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     )
        _UpperCAmelCase          =   self.get_generator(__UpperCamelCase     )

        with torch.no_grad():
            _UpperCAmelCase          =   model(__UpperCamelCase     ,					generator=__UpperCamelCase     ,					sample_posterior=__UpperCamelCase     ).sample

        assert sample.shape == image.shape

        _UpperCAmelCase          =   sample[-1, -2:, -2:, :2].flatten().float().cpu()
        _UpperCAmelCase          =   torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice     )

        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=3e-3     )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
            [47, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
            # fmt: on
        ]     )
    @require_torch_gpu
    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       str     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model(fpaa=__UpperCamelCase     )
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     ,					fpaa=__UpperCamelCase     )
        _UpperCAmelCase          =   self.get_generator(__UpperCamelCase     )

        with torch.no_grad():
            _UpperCAmelCase          =   model(__UpperCamelCase     ,					generator=__UpperCamelCase     ,					sample_posterior=__UpperCamelCase     ).sample

        assert sample.shape == image.shape

        _UpperCAmelCase          =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
        _UpperCAmelCase          =   torch.tensor(__UpperCamelCase     )

        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=1e-2     )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
            [47, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
            # fmt: on
        ]     )
    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Any     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model()
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     )

        with torch.no_grad():
            _UpperCAmelCase          =   model(__UpperCamelCase     ).sample

        assert sample.shape == image.shape

        _UpperCAmelCase          =   sample[-1, -2:, -2:, :2].flatten().float().cpu()
        _UpperCAmelCase          =   torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice     )

        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=3e-3     )







    @parameterized.expand(
        [
            # fmt: off
            [13, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
            [37, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
            # fmt: on
        ]     )
    @require_torch_gpu
    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       List[str]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model()
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     ,					shape=(3, 4, 64, 64)     )

        with torch.no_grad():
            _UpperCAmelCase          =   model.decode(__UpperCamelCase     ).sample

        assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

        _UpperCAmelCase          =   sample[-1, -2:, :2, -2:].flatten().cpu()
        _UpperCAmelCase          =   torch.tensor(__UpperCamelCase     )

        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=1e-3     )







    @parameterized.expand(
        [
            # fmt: off
            [27, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
            [16, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
            # fmt: on
        ]     )
    @require_torch_gpu
    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Union[str, Any]     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model(fpaa=__UpperCamelCase     )
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     ,					shape=(3, 4, 64, 64)     ,					fpaa=__UpperCamelCase     )

        with torch.no_grad():
            _UpperCAmelCase          =   model.decode(__UpperCamelCase     ).sample

        assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

        _UpperCAmelCase          =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
        _UpperCAmelCase          =   torch.tensor(__UpperCamelCase     )

        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=5e-3     )







    @parameterized.expand([(13,), (16,), (27,)]     )
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available()     ,					reason="""xformers is not required when using PyTorch 2.0."""     )
    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       List[str]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model(fpaa=__UpperCamelCase     )
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     ,					shape=(3, 4, 64, 64)     ,					fpaa=__UpperCamelCase     )

        with torch.no_grad():
            _UpperCAmelCase          =   model.decode(__UpperCamelCase     ).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
            _UpperCAmelCase          =   model.decode(__UpperCamelCase     ).sample

        assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=1e-1     )







    @parameterized.expand([(13,), (16,), (37,)]     )
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available()     ,					reason="""xformers is not required when using PyTorch 2.0."""     )
    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       Dict     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model()
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     ,					shape=(3, 4, 64, 64)     )

        with torch.no_grad():
            _UpperCAmelCase          =   model.decode(__UpperCamelCase     ).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
            _UpperCAmelCase          =   model.decode(__UpperCamelCase     ).sample

        assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=1e-2     )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
            [47, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
            # fmt: on
        ]     )
    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Optional[Any]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model()
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     )
        _UpperCAmelCase          =   self.get_generator(__UpperCamelCase     )

        with torch.no_grad():
            _UpperCAmelCase          =   model.encode(__UpperCamelCase     ).latent_dist
            _UpperCAmelCase          =   dist.sample(generator=__UpperCamelCase     )

        assert list(sample.shape     ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

        _UpperCAmelCase          =   sample[0, -1, -3:, -3:].flatten().cpu()
        _UpperCAmelCase          =   torch.tensor(__UpperCamelCase     )

        _UpperCAmelCase          =   3e-3 if torch_device != """mps""" else 1e-2
        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=__UpperCamelCase     )

"""simple docstring"""



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

import requests


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   None
    if token is not None:
        _UpperCAmelCase          =   {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase          =   F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
    _UpperCAmelCase          =   requests.get(_A					,      headers=_A      ).json()
    _UpperCAmelCase          =   {}

    try:
        job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}      )
        _UpperCAmelCase          =   math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0      )

        for i in range(_A      ):
            _UpperCAmelCase          =   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  _UpperCamelCase	(						_A					,      _A=None      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   None
    if token is not None:
        _UpperCAmelCase          =   {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase          =   F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
    _UpperCAmelCase          =   requests.get(_A					,      headers=_A      ).json()
    _UpperCAmelCase          =   {}

    try:
        artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}      )
        _UpperCAmelCase          =   math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0      )

        for i in range(_A      ):
            _UpperCAmelCase          =   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  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A      )    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase          =   None
    if token is not None:
        _UpperCAmelCase          =   {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase          =   requests.get(_A					,      headers=_A					,      allow_redirects=_A      )
    _UpperCAmelCase          =   result.headers["""Location"""]
    _UpperCAmelCase          =   requests.get(_A					,      allow_redirects=_A      )
    _UpperCAmelCase          =   os.path.join(_A					,      F"""{artifact_name}.zip"""      )
    with open(_A					,      """wb"""      ) as fp:
        fp.write(response.content      )


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   []
    _UpperCAmelCase          =   []
    _UpperCAmelCase          =   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          =   line.decode("""UTF-8"""      ).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    _UpperCAmelCase          =   line[: line.index(""": """      )]
                                    _UpperCAmelCase          =   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          =   line[len("""FAILED """      ) :]
                                failed_tests.append(_A      )
                            elif filename == "job_name.txt":
                                _UpperCAmelCase          =   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          =   None
    if job_name and job_links:
        _UpperCAmelCase          =   job_links.get(_A					,      _A      )

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

    return result


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   []

    _UpperCAmelCase          =   [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  _UpperCamelCase	(						_A					,      _A=None      )    ->  Optional[Any]:





    """simple docstring"""

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

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


def  _UpperCamelCase	(						_A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   test.split("""::"""      )[0]
    if test.startswith("""tests/models/"""      ):
        _UpperCAmelCase          =   test.split("""/"""      )[2]
    else:
        _UpperCAmelCase          =   None

    return test


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   [(x[0], x[1], get_model(x[2]      )) for x in logs]
    _UpperCAmelCase          =   [x for x in logs if x[2] is not None]
    _UpperCAmelCase          =   {x[2] for x in logs}

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

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


def  _UpperCamelCase	(						_A      )    ->  Optional[int]:





    """simple docstring"""

    _UpperCAmelCase          =   """| no. | error | status |"""
    _UpperCAmelCase          =   """|-:|:-|:-|"""
    _UpperCAmelCase          =   [header, sep]
    for error in reduced_by_error:
        _UpperCAmelCase          =   reduced_by_error[error]["""count"""]
        _UpperCAmelCase          =   F"""| {count} | {error[:1_0_0]} |  |"""
        lines.append(_A      )

    return "\n".join(_A      )


def  _UpperCamelCase	(						_A      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   """| model | no. of errors | major error | count |"""
    _UpperCAmelCase          =   """|-:|-:|-:|-:|"""
    _UpperCAmelCase          =   [header, sep]
    for model in reduced_by_model:
        _UpperCAmelCase          =   reduced_by_model[model]["""count"""]
        _UpperCAmelCase    ,_UpperCAmelCase          =   list(reduced_by_model[model]["""errors"""].items()      )[0]
        _UpperCAmelCase          =   F"""| {model} | {count} | {error[:6_0]} | {_count} |"""
        lines.append(_A      )

    return "\n".join(_A      )


if __name__ == "__main__":
 a					:	Tuple							=		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.''')
 a					:	Dict							=		parser.parse_args()

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

 a					:	Tuple							=		get_job_links(args.workflow_run_id, token=args.token)
 a					:	Tuple							=		{}
 # 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:
    a					:	List[Any]							=		k.find(''' / ''')
    a					:	Tuple							=		k[index + len(''' / ''') :]
   a					:	int							=		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)

 a					:	Tuple							=		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)

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

 # `e[1]` is the error
 a					:	Union[str, Any]							=		Counter()
 counter.update([e[1] for e in errors])

 # print the top 30 most common test errors
 a					:	Optional[int]							=		counter.most_common(3_0)
 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)

 a					:	int							=		reduce_by_error(errors)
 a					:	str							=		reduce_by_model(errors)

 a					:	int							=		make_github_table(reduced_by_error)
 a					:	Optional[int]							=		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)