

import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTMSNForImageClassification, ViTMSNModel
    from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			List[Any]     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Dict=13     , __lowerCamelCase					:			Tuple=30     , __lowerCamelCase					:			List[Any]=2     , __lowerCamelCase					:			Optional[int]=3     , __lowerCamelCase					:			int=True     , __lowerCamelCase					:			Optional[Any]=True     , __lowerCamelCase					:			int=32     , __lowerCamelCase					:			int=5     , __lowerCamelCase					:			List[str]=4     , __lowerCamelCase					:			Any=37     , __lowerCamelCase					:			Tuple="gelu"     , __lowerCamelCase					:			Union[str, Any]=0.1     , __lowerCamelCase					:			Optional[int]=0.1     , __lowerCamelCase					:			str=10     , __lowerCamelCase					:			Union[str, Any]=0.02     , __lowerCamelCase					:			Any=None     , ):
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   batch_size
       SCREAMING_SNAKE_CASE										=   image_size
       SCREAMING_SNAKE_CASE										=   patch_size
       SCREAMING_SNAKE_CASE										=   num_channels
       SCREAMING_SNAKE_CASE										=   is_training
       SCREAMING_SNAKE_CASE										=   use_labels
       SCREAMING_SNAKE_CASE										=   hidden_size
       SCREAMING_SNAKE_CASE										=   num_hidden_layers
       SCREAMING_SNAKE_CASE										=   num_attention_heads
       SCREAMING_SNAKE_CASE										=   intermediate_size
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   hidden_dropout_prob
       SCREAMING_SNAKE_CASE										=   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE										=   type_sequence_label_size
       SCREAMING_SNAKE_CASE										=   initializer_range
       SCREAMING_SNAKE_CASE										=   scope

       # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       SCREAMING_SNAKE_CASE										=   (image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE										=   num_patches + 1





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

       SCREAMING_SNAKE_CASE										=   None
       if self.use_labels:
           SCREAMING_SNAKE_CASE										=   ids_tensor([self.batch_size]     , self.type_sequence_label_size			)

       SCREAMING_SNAKE_CASE										=   self.get_config()

       return config, pixel_values, labels





   def      _snake_case		(       self					:			Tuple			):
       return ViTMSNConfig(
           image_size=self.image_size     , patch_size=self.patch_size     , num_channels=self.num_channels     , 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     , initializer_range=self.initializer_range     , )





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   ViTMSNModel(config=__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase			)
       self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.seq_length, self.hidden_size)			)





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   self.type_sequence_label_size
       SCREAMING_SNAKE_CASE										=   ViTMSNForImageClassification(__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , labels=__lowerCamelCase			)
       print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}"			)
       print("Labels: {labels}"			)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.type_sequence_label_size)			)

       # test greyscale images
       SCREAMING_SNAKE_CASE										=   1
       SCREAMING_SNAKE_CASE										=   ViTMSNForImageClassification(__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()

       SCREAMING_SNAKE_CASE										=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.type_sequence_label_size)			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   config_and_inputs
       SCREAMING_SNAKE_CASE										=   {"pixel_values": pixel_values}
       return config, inputs_dict





@require_torch
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
   lowerCamelCase__				=      (
       {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
       if is_torch_available()
       else {}
   )

   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False





   def      _snake_case		(       self					:			Dict			):
       SCREAMING_SNAKE_CASE										=   ViTMSNModelTester(self			)
       SCREAMING_SNAKE_CASE										=   ConfigTester(self     , config_class=__lowerCamelCase     , has_text_modality=__lowerCamelCase     , hidden_size=37			)





   def      _snake_case		(       self					:			Optional[Any]			):
       self.config_tester.run_common_tests()





   @unittest.skip(reason="ViTMSN does not use inputs_embeds"			)
   def      _snake_case		(       self					:			Tuple			):
       pass





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)
           self.assertIsInstance(model.get_input_embeddings()     , (nn.Module)			)
           SCREAMING_SNAKE_CASE										=   model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(__lowerCamelCase     , nn.Linear			)			)





   def      _snake_case		(       self					:			List[str]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   inspect.signature(model.forward			)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           SCREAMING_SNAKE_CASE										=   [*signature.parameters.keys()]

           SCREAMING_SNAKE_CASE										=   ["pixel_values"]
           self.assertListEqual(arg_names[:1]     , __lowerCamelCase			)





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





   @slow
   def      _snake_case		(       self					:			List[str]			):
       for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           SCREAMING_SNAKE_CASE										=   ViTMSNModel.from_pretrained(__lowerCamelCase			)
           self.assertIsNotNone(__lowerCamelCase			)





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image





@require_torch
@require_vision
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   @cached_property
   def      _snake_case		(       self					:			Any			):
       return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"			) if is_vision_available() else None





   @slow
   def      _snake_case		(       self					:			List[str]			):
       torch.manual_seed(2			)
       SCREAMING_SNAKE_CASE										=   ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"			).to(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   self.default_image_processor
       SCREAMING_SNAKE_CASE										=   prepare_img()
       SCREAMING_SNAKE_CASE										=   image_processor(images=__lowerCamelCase     , return_tensors="pt"			).to(__lowerCamelCase			)

       # forward pass
       with torch.no_grad():
           SCREAMING_SNAKE_CASE										=   model(**__lowerCamelCase			)

       # verify the logits
       SCREAMING_SNAKE_CASE										=   torch.Size((1, 1000)			)
       self.assertEqual(outputs.logits.shape     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   torch.tensor([-0.0_803, -0.4_454, -0.2_375]			).to(__lowerCamelCase			)

       self.assertTrue(torch.allclose(outputs.logits[0, :3]     , __lowerCamelCase     , atol=1e-4			)			)


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)