

import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      KandinskyVaaControlnetPipeline
   lowerCamelCase__				=      ["image_embeds", "negative_image_embeds", "hint"]
   lowerCamelCase__				=      ["image_embeds", "negative_image_embeds", "hint"]
   lowerCamelCase__				=      [
       "generator",
       "height",
       "width",
       "latents",
       "guidance_scale",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   lowerCamelCase__				=      False





   @property
   def      _snake_case		(       self					:			Union[str, Any]			):
       return 32





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       return 32





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       return self.time_input_dim





   @property
   def      _snake_case		(       self					:			Tuple			):
       return self.time_input_dim * 4





   @property
   def      _snake_case		(       self					:			str			):
       return 100





   @property
   def      _snake_case		(       self					:			Any			):
       torch.manual_seed(0			)

       SCREAMING_SNAKE_CASE										=   {
           "in_channels": 8,
           # Out channels is double in channels because predicts mean and variance
           "out_channels": 8,
           "addition_embed_type": "image_hint",
           "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
           "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
           "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
           "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
           "layers_per_block": 1,
           "encoder_hid_dim": self.text_embedder_hidden_size,
           "encoder_hid_dim_type": "image_proj",
           "cross_attention_dim": self.cross_attention_dim,
           "attention_head_dim": 4,
           "resnet_time_scale_shift": "scale_shift",
           "class_embed_type": None,
       }

       SCREAMING_SNAKE_CASE										=   UNetaDConditionModel(**__lowerCamelCase			)
       return model





   @property
   def      _snake_case		(       self					:			Dict			):
       return {
           "block_out_channels": [32, 32, 64, 64],
           "down_block_types": [
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "AttnDownEncoderBlock2D",
           ],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
           "vq_embed_dim": 4,
       }





   @property
   def      _snake_case		(       self					:			Union[str, Any]			):
       torch.manual_seed(0			)
       SCREAMING_SNAKE_CASE										=   VQModel(**self.dummy_movq_kwargs			)
       return model





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   self.dummy_unet
       SCREAMING_SNAKE_CASE										=   self.dummy_movq

       SCREAMING_SNAKE_CASE										=   DDIMScheduler(
           num_train_timesteps=1000     , beta_schedule="linear"     , beta_start=0.00_085     , beta_end=0.012     , clip_sample=__lowerCamelCase     , set_alpha_to_one=__lowerCamelCase     , steps_offset=1     , prediction_type="epsilon"     , thresholding=__lowerCamelCase     , )

       SCREAMING_SNAKE_CASE										=   {
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }
       return components





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			Any     , __lowerCamelCase					:			Any=0			):
       SCREAMING_SNAKE_CASE										=   floats_tensor((1, self.text_embedder_hidden_size)     , rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   floats_tensor((1, self.text_embedder_hidden_size)     , rng=random.Random(seed + 1			)			).to(
           __lowerCamelCase			)

       # create hint
       SCREAMING_SNAKE_CASE										=   floats_tensor((1, 3, 64, 64)     , rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)

       if str(__lowerCamelCase			).startswith("mps"			):
           SCREAMING_SNAKE_CASE										=   torch.manual_seed(__lowerCamelCase			)
       else:
           SCREAMING_SNAKE_CASE										=   torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   {
           "image_embeds": image_embeds,
           "negative_image_embeds": negative_image_embeds,
           "hint": hint,
           "generator": generator,
           "height": 64,
           "width": 64,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   "cpu"

       SCREAMING_SNAKE_CASE										=   self.get_dummy_components()

       SCREAMING_SNAKE_CASE										=   self.pipeline_class(**__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   pipe.to(__lowerCamelCase			)

       pipe.set_progress_bar_config(disable=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   pipe(**self.get_dummy_inputs(__lowerCamelCase			)			)
       SCREAMING_SNAKE_CASE										=   output.images

       SCREAMING_SNAKE_CASE										=   pipe(
           **self.get_dummy_inputs(__lowerCamelCase			)     , return_dict=__lowerCamelCase     , )[0]

       SCREAMING_SNAKE_CASE										=   image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE										=   image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE										=   np.array(
           [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]			)

       assert (
           np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
       ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"

       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
       ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"





@slow
@require_torch_gpu
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			int			):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"			)

       SCREAMING_SNAKE_CASE										=   load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/hint_image_cat.png"			)
       SCREAMING_SNAKE_CASE										=   torch.from_numpy(np.array(__lowerCamelCase			)			).float() / 255.0
       SCREAMING_SNAKE_CASE										=   hint.permute(2     , 0     , 1			).unsqueeze(0			)

       SCREAMING_SNAKE_CASE										=   KandinskyVaaPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-prior"     , torch_dtype=torch.floataa			)
       pipe_prior.to(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   KandinskyVaaControlnetPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-controlnet-depth"     , torch_dtype=torch.floataa			)
       SCREAMING_SNAKE_CASE										=   pipeline.to(__lowerCamelCase			)
       pipeline.set_progress_bar_config(disable=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   "A robot, 4k photo"

       SCREAMING_SNAKE_CASE										=   torch.Generator(device="cuda"			).manual_seed(0			)
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   pipe_prior(
           __lowerCamelCase     , generator=__lowerCamelCase     , num_inference_steps=5     , negative_prompt=""     , ).to_tuple()

       SCREAMING_SNAKE_CASE										=   torch.Generator(device="cuda"			).manual_seed(0			)
       SCREAMING_SNAKE_CASE										=   pipeline(
           image_embeds=__lowerCamelCase     , negative_image_embeds=__lowerCamelCase     , hint=__lowerCamelCase     , generator=__lowerCamelCase     , num_inference_steps=100     , output_type="np"     , )

       SCREAMING_SNAKE_CASE										=   output.images[0]

       assert image.shape == (512, 512, 3)

       assert_mean_pixel_difference(__lowerCamelCase     , __lowerCamelCase			)


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

import requests





def 	__a     (      A__			:  str			,  A__			:  List[Any]=None       ):
    SCREAMING_SNAKE_CASE										=   None
    if token is not None:
        SCREAMING_SNAKE_CASE										=   {"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

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

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

        for i in range(A__       ):
            SCREAMING_SNAKE_CASE										=   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 	__a     (      A__			:  List[Any]			,  A__			:  Optional[int]=None       ):
    SCREAMING_SNAKE_CASE										=   None
    if token is not None:
        SCREAMING_SNAKE_CASE										=   {"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

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

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

        for i in range(A__       ):
            SCREAMING_SNAKE_CASE										=   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 	__a     (      A__			:  Any			,  A__			:  str			,  A__			:  List[str]			,  A__			:  Union[str, Any]       ):
    SCREAMING_SNAKE_CASE										=   None
    if token is not None:
        SCREAMING_SNAKE_CASE										=   {"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

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





def 	__a     (      A__			:  List[Any]			,  A__			:  List[Any]=None       ):
    SCREAMING_SNAKE_CASE										=   []
    SCREAMING_SNAKE_CASE										=   []
    SCREAMING_SNAKE_CASE										=   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:
                            SCREAMING_SNAKE_CASE										=   line.decode("UTF-8"       ).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    SCREAMING_SNAKE_CASE										=   line[: line.index(": "       )]
                                    SCREAMING_SNAKE_CASE										=   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
                                SCREAMING_SNAKE_CASE										=   line[len("FAILED "       ) :]
                                failed_tests.append(A__       )
                            elif filename == "job_name.txt":
                                SCREAMING_SNAKE_CASE										=   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."       )

    SCREAMING_SNAKE_CASE										=   None
    if job_name and job_links:
        SCREAMING_SNAKE_CASE										=   job_links.get(A__			,  A__       )

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

    return result





def 	__a     (      A__			:  Union[str, Any]			,  A__			:  Union[str, Any]=None       ):
    SCREAMING_SNAKE_CASE										=   []

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

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





def 	__a     (      A__			:  str       ):
    SCREAMING_SNAKE_CASE										=   test.split("::"       )[0]
    if test.startswith("tests/models/"       ):
        SCREAMING_SNAKE_CASE										=   test.split("/"       )[2]
    else:
        SCREAMING_SNAKE_CASE										=   None

    return test





def 	__a     (      A__			:  List[str]			,  A__			:  Dict=None       ):
    SCREAMING_SNAKE_CASE										=   [(x[0], x[1], get_model(x[2]       )) for x in logs]
    SCREAMING_SNAKE_CASE										=   [x for x in logs if x[2] is not None]
    SCREAMING_SNAKE_CASE										=   {x[2] for x in logs}

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

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





def 	__a     (      A__			:  Dict       ):
    SCREAMING_SNAKE_CASE										=   "| no. | error | status |"
    SCREAMING_SNAKE_CASE										=   "|-:|:-|:-|"
    SCREAMING_SNAKE_CASE										=   [header, sep]
    for error in reduced_by_error:
        SCREAMING_SNAKE_CASE										=   reduced_by_error[error]["count"]
        SCREAMING_SNAKE_CASE										=   F"| {count} | {error[:100]} |  |"
        lines.append(A__       )

    return "\n".join(A__       )





def 	__a     (      A__			:  Optional[Any]       ):
    SCREAMING_SNAKE_CASE										=   "| model | no. of errors | major error | count |"
    SCREAMING_SNAKE_CASE										=   "|-:|-:|-:|-:|"
    SCREAMING_SNAKE_CASE										=   [header, sep]
    for model in reduced_by_model:
        SCREAMING_SNAKE_CASE										=   reduced_by_model[model]["count"]
        SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   list(reduced_by_model[model]["errors"].items()       )[0]
        SCREAMING_SNAKE_CASE										=   F"| {model} | {count} | {error[:60]} | {_count} |"
        lines.append(A__       )

    return "\n".join(A__       )


if __name__ == "__main__":
    __A			:					List[str]						=			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			:					Union[str, Any]						=			parser.parse_args()

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

    __A			:					int						=			get_job_links(args.workflow_run_id, token=args.token)
    __A			:					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:
                __A			:					Union[str, Any]						=			k.find(' / ')
                __A			:					Optional[int]						=			k[index + len(' / ') :]
            __A			:					Optional[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			:					int						=			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			:					Dict						=			Counter()
    counter.update([e[1] for e in errors])

    # print the top 30 most common test errors
    __A			:					Optional[Any]						=			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			:					str						=			reduce_by_error(errors)
    __A			:					int						=			reduce_by_model(errors)

    __A			:					Any						=			make_github_table(reduced_by_error)
    __A			:					List[str]						=			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)