






"""simple docstring"""



import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
		from transformers import (
		    FlaxBertModel,
		    FlaxCLIPVisionModel,
		    FlaxVisionTextDualEncoderModel,
		    FlaxViTModel,
		    VisionTextDualEncoderConfig,
		    VisionTextDualEncoderProcessor,
		)
		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)


if is_torch_available():
		import torch

		from transformers import VisionTextDualEncoderModel

if is_vision_available():
		from PIL import Image




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->Optional[Any]:
						if isinstance(_A,						collections.abc.Iterable      ):
												return x
						return (x, x)






@require_flax
class 							__SCREAMING_SNAKE_CASE						:


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						Optional[Any]			,    snake_case      :						Dict							):
													'''simple docstring'''





													pass


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													pass


							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													pass


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						Tuple			,    snake_case      :						int			,    snake_case      :						Optional[int]							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  np.abs((a - b)							).max()
													self.assertLessEqual(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    F'Difference between torch and flax is {diff} (>= {tol}).'							)


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						Dict			,    snake_case      :						Tuple			,    snake_case      :						Union[str, Any]			,    snake_case      :						List[Any]			,    snake_case      :						List[str]=None			,    **snake_case      :						Dict							):
													'''simple docstring'''





													A__						:  Any		  =  VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE							)

													A__						:  int		  =  FlaxVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE							)

													A__						:  str		  =  model(input_ids=_SCREAMING_SNAKE_CASE			,    pixel_values=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE							)

													self.assertEqual(output["""text_embeds"""].shape			,    (input_ids.shape[0], config.projection_dim)							)
													self.assertEqual(output["""image_embeds"""].shape			,    (pixel_values.shape[0], config.projection_dim)							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						Union[str, Any]			,    snake_case      :						Union[str, Any]			,    snake_case      :						Dict			,    snake_case      :						Dict			,    snake_case      :						Union[str, Any]=None			,    **snake_case      :						Tuple							):
													'''simple docstring'''





													A__  ,  A__						:  Union[str, Any]		  =  self.get_vision_text_model(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE							)
													A__						:  Dict		  =  {"""vision_model""": vision_model, """text_model""": text_model}
													A__						:  int		  =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE							)

													A__						:  Optional[Any]		  =  model(input_ids=_SCREAMING_SNAKE_CASE			,    pixel_values=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE							)

													self.assertEqual(output["""text_embeds"""].shape			,    (input_ids.shape[0], model.config.projection_dim)							)
													self.assertEqual(output["""image_embeds"""].shape			,    (pixel_values.shape[0], model.config.projection_dim)							)


							def 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						Union[str, Any]			,    snake_case      :						List[str]			,    snake_case      :						Optional[Any]			,    snake_case      :						Tuple			,    snake_case      :						Optional[Any]=None			,    **snake_case      :						Dict							):
													'''simple docstring'''





													A__  ,  A__						:  Any		  =  self.get_vision_text_model(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE							)
													A__						:  Union[str, Any]		  =  {"""vision_model""": vision_model, """text_model""": text_model}
													A__						:  List[Any]		  =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE							)

													A__						:  Dict		  =  model(input_ids=_SCREAMING_SNAKE_CASE			,    pixel_values=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE							)
													A__						:  int		  =  output[0]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			model.save_pretrained(_SCREAMING_SNAKE_CASE							)
																			A__						:  Tuple		  =  FlaxVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE							)

																			A__						:  Tuple		  =  model(input_ids=_SCREAMING_SNAKE_CASE			,    pixel_values=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE							)
																			A__						:  int		  =  after_output[0]
																			A__						:  Optional[Any]		  =  np.amax(np.abs(out_a - out_a							)							)
																			self.assertLessEqual(_SCREAMING_SNAKE_CASE			,    1e-3							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Tuple			,    snake_case      :						Dict			,    snake_case      :						Union[str, Any]			,    snake_case      :						List[Any]			,    snake_case      :						Optional[int]=None			,    **snake_case      :						int							):
													'''simple docstring'''





													A__  ,  A__						:  Optional[Any]		  =  self.get_vision_text_model(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE							)
													A__						:  str		  =  {"""vision_model""": vision_model, """text_model""": text_model}
													A__						:  Any		  =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE							)

													A__						:  Tuple		  =  model(
													    input_ids=_SCREAMING_SNAKE_CASE			,    pixel_values=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE			,    output_attentions=_SCREAMING_SNAKE_CASE							)

													A__						:  List[str]		  =  output.vision_model_output.attentions
													self.assertEqual(len(_SCREAMING_SNAKE_CASE							)			,    vision_config.num_hidden_layers							)

													# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
													A__						:  Any		  =  to_atuple(vision_model.config.image_size							)
													A__						:  int		  =  to_atuple(vision_model.config.patch_size							)
													A__						:  str		  =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
													A__						:  int		  =  num_patches + 1
													self.assertEqual(vision_attentions[0].shape[-3:]			,    (vision_config.num_attention_heads, seq_len, seq_len)							)

													A__						:  List[Any]		  =  output.text_model_output.attentions
													self.assertEqual(len(_SCREAMING_SNAKE_CASE							)			,    text_config.num_hidden_layers							)

													self.assertEqual(
													    text_attentions[0].shape[-3:]			,    (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])			,    )


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						str			,    snake_case      :						List[str]			,    snake_case      :						str							):
													'''simple docstring'''





													pt_model.to(_SCREAMING_SNAKE_CASE							)
													pt_model.eval()

													# prepare inputs
													A__						:  Union[str, Any]		  =  inputs_dict
													A__						:  Any		  =  {k: torch.tensor(v.tolist()							) for k, v in flax_inputs.items()}

													with torch.no_grad():
																			A__						:  Optional[int]		  =  pt_model(**_SCREAMING_SNAKE_CASE							).to_tuple()

													A__						:  str		  =  fx_model(**_SCREAMING_SNAKE_CASE							).to_tuple()
													self.assertEqual(len(_SCREAMING_SNAKE_CASE							)			,    len(_SCREAMING_SNAKE_CASE							)			,    """Output lengths differ between Flax and PyTorch"""							)
													for fx_output, pt_output in zip(fx_outputs[:4]			,    pt_outputs[:4]							):
																			self.assert_almost_equals(_SCREAMING_SNAKE_CASE			,    pt_output.numpy()			,    4e-2							)

													# PT -> Flax
													with tempfile.TemporaryDirectory() as tmpdirname:
																			pt_model.save_pretrained(_SCREAMING_SNAKE_CASE							)
																			A__						:  Optional[int]		  =  FlaxVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE			,    from_pt=_SCREAMING_SNAKE_CASE							)

													A__						:  Dict		  =  fx_model_loaded(**_SCREAMING_SNAKE_CASE							).to_tuple()
													self.assertEqual(len(_SCREAMING_SNAKE_CASE							)			,    len(_SCREAMING_SNAKE_CASE							)			,    """Output lengths differ between Flax and PyTorch"""							)
													for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]			,    pt_outputs[:4]							):
																			self.assert_almost_equals(_SCREAMING_SNAKE_CASE			,    pt_output.numpy()			,    4e-2							)

													# Flax -> PT
													with tempfile.TemporaryDirectory() as tmpdirname:
																			fx_model.save_pretrained(_SCREAMING_SNAKE_CASE							)
																			A__						:  str		  =  VisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE			,    from_flax=_SCREAMING_SNAKE_CASE							)

													pt_model_loaded.to(_SCREAMING_SNAKE_CASE							)
													pt_model_loaded.eval()

													with torch.no_grad():
																			A__						:  List[Any]		  =  pt_model_loaded(**_SCREAMING_SNAKE_CASE							).to_tuple()

													self.assertEqual(len(_SCREAMING_SNAKE_CASE							)			,    len(_SCREAMING_SNAKE_CASE							)			,    """Output lengths differ between Flax and PyTorch"""							)
													for fx_output, pt_output_loaded in zip(fx_outputs[:4]			,    pt_outputs_loaded[:4]							):
																			self.assert_almost_equals(_SCREAMING_SNAKE_CASE			,    pt_output_loaded.numpy()			,    4e-2							)


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						List[str]			,    snake_case      :						Any			,    snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE							)

													A__						:  List[str]		  =  VisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE							)
													A__						:  Union[str, Any]		  =  FlaxVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE							)

													A__						:  Union[str, Any]		  =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,    _SCREAMING_SNAKE_CASE							)
													A__						:  int		  =  fx_state

													self.check_pt_flax_equivalence(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE							)


							def 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						Union[str, Any]			,    snake_case      :						Optional[Any]			,    snake_case      :						int							):
													'''simple docstring'''





													A__						:  Tuple		  =  VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE							)

													A__						:  Optional[Any]		  =  VisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE							)
													A__						:  Dict		  =  FlaxVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE							)

													A__						:  str		  =  load_flax_weights_in_pytorch_model(_SCREAMING_SNAKE_CASE			,    fx_model.params							)

													self.check_pt_flax_equivalence(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE							)


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													A__						:  int		  =  self.prepare_config_and_inputs()
													self.check_model_from_pretrained_configs(**_SCREAMING_SNAKE_CASE							)


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													A__						:  Any		  =  self.prepare_config_and_inputs()
													self.check_vision_text_dual_encoder_from_pretrained(**_SCREAMING_SNAKE_CASE							)


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  Tuple		  =  self.prepare_config_and_inputs()
													self.check_save_load(**_SCREAMING_SNAKE_CASE							)


							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													A__						:  List[Any]		  =  self.prepare_config_and_inputs()
													self.check_vision_text_output_attention(**_SCREAMING_SNAKE_CASE							)


							@is_pt_flax_cross_test
							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													A__						:  Tuple		  =  self.prepare_config_and_inputs()
													A__						:  int		  =  config_inputs_dict.pop("""vision_config"""							)
													A__						:  str		  =  config_inputs_dict.pop("""text_config"""							)

													A__						:  Optional[int]		  =  config_inputs_dict

													self.check_equivalence_pt_to_flax(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE							)
													self.check_equivalence_flax_to_pt(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE							)


							@slow
							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__  ,  A__						:  Tuple		  =  self.get_pretrained_model_and_inputs()

													A__						:  Union[str, Any]		  =  model_a(**_SCREAMING_SNAKE_CASE							)
													A__						:  str		  =  outputs[0]

													with tempfile.TemporaryDirectory() as tmp_dirname:
																			model_a.save_pretrained(_SCREAMING_SNAKE_CASE							)
																			A__						:  Optional[int]		  =  FlaxVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE							)

																			A__						:  str		  =  model_a(**_SCREAMING_SNAKE_CASE							)
																			A__						:  Union[str, Any]		  =  after_outputs[0]
																			A__						:  int		  =  np.amax(np.abs(out_a - out_a							)							)
																			self.assertLessEqual(_SCREAMING_SNAKE_CASE			,    1e-5							)






@require_flax
class 							__SCREAMING_SNAKE_CASE						(				lowerCAmelCase__       ,		unittest.TestCase		):


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													A__						:  str		  =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
													    """hf-internal-testing/tiny-random-vit"""			,    """hf-internal-testing/tiny-bert"""			,    vision_from_pt=_SCREAMING_SNAKE_CASE			,    text_from_pt=_SCREAMING_SNAKE_CASE			,    )
													A__						:  Optional[int]		  =  13
													A__						:  Tuple		  =  floats_tensor(
													    [
													        batch_size,
													        model.config.vision_config.num_channels,
													        model.config.vision_config.image_size,
													        model.config.vision_config.image_size,
													    ]							)
													A__						:  str		  =  ids_tensor([batch_size, 4]			,    model.config.text_config.vocab_size							)
													A__						:  Optional[int]		  =  random_attention_mask([batch_size, 4]							)
													A__						:  Dict		  =  {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

													return model, inputs


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						List[str]			,    snake_case      :						Optional[int]							):
													'''simple docstring'''





													A__						:  List[Any]		  =  FlaxViTModel(_SCREAMING_SNAKE_CASE							)
													A__						:  Any		  =  FlaxBertModel(_SCREAMING_SNAKE_CASE							)
													return vision_model, text_model


							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													A__						:  List[str]		  =  FlaxViTModelTester(self							)
													A__						:  Tuple		  =  FlaxBertModelTester(self							)
													A__						:  Optional[int]		  =  vit_model_tester.prepare_config_and_inputs()
													A__						:  str		  =  bert_model_tester.prepare_config_and_inputs()

													A__  ,  A__						:  Optional[Any]		  =  vision_config_and_inputs

													A__  ,  A__  ,  A__  ,  A__						:  Tuple		  =  text_config_and_inputs

													# make sure that cross attention layers are added
													return {
													    "text_config": text_config,
													    "vision_config": vision_config,
													    "pixel_values": pixel_values,
													    "attention_mask": attention_mask,
													    "input_ids": input_ids,
													    "token_type_ids": token_type_ids,
													}






@require_torch
class 							__SCREAMING_SNAKE_CASE						(				lowerCAmelCase__       ,		unittest.TestCase		):


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  int		  =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
													    """hf-internal-testing/tiny-random-clip"""			,    """hf-internal-testing/tiny-bert"""			,    vision_from_pt=_SCREAMING_SNAKE_CASE			,    text_from_pt=_SCREAMING_SNAKE_CASE			,    )
													A__						:  List[str]		  =  13
													A__						:  int		  =  floats_tensor(
													    [
													        batch_size,
													        model.config.vision_config.num_channels,
													        model.config.vision_config.image_size,
													        model.config.vision_config.image_size,
													    ]							)
													A__						:  Dict		  =  ids_tensor([batch_size, 4]			,    model.config.text_config.vocab_size							)
													A__						:  str		  =  random_attention_mask([batch_size, 4]							)
													A__						:  Any		  =  {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

													return model, inputs


							def 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						int			,    snake_case      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  FlaxCLIPVisionModel(_SCREAMING_SNAKE_CASE							)
													A__						:  List[str]		  =  FlaxBertModel(_SCREAMING_SNAKE_CASE							)
													return vision_model, text_model


							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													A__						:  int		  =  FlaxCLIPVisionModelTester(self							)
													A__						:  str		  =  FlaxBertModelTester(self							)
													A__						:  Optional[int]		  =  clip_model_tester.prepare_config_and_inputs()
													A__						:  Optional[int]		  =  bert_model_tester.prepare_config_and_inputs()

													A__  ,  A__						:  List[Any]		  =  vision_config_and_inputs

													A__  ,  A__  ,  A__  ,  A__						:  Optional[Any]		  =  text_config_and_inputs

													# make sure that cross attention layers are added
													return {
													    "text_config": text_config,
													    "vision_config": vision_config,
													    "pixel_values": pixel_values,
													    "attention_mask": attention_mask,
													    "input_ids": input_ids,
													    "token_type_ids": token_type_ids,
													}






@require_flax
@require_vision
class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							@slow
							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian"""			,    logit_scale_init_value=1.0							)
													A__						:  str		  =  VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""							)

													A__						:  Optional[Any]		  =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
													A__						:  Any		  =  processor(
													    text=["""una foto di un gatto""", """una foto di un cane"""]			,    images=_SCREAMING_SNAKE_CASE			,    padding=_SCREAMING_SNAKE_CASE			,    return_tensors="""np"""							)

													A__						:  List[str]		  =  model(**_SCREAMING_SNAKE_CASE							)

													# verify the logits
													self.assertEqual(outputs.logits_per_image.shape			,    (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])							)
													self.assertEqual(
													    outputs.logits_per_text.shape			,    (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])			,    )

													A__						:  Optional[Any]		  =  np.array([[1.2284727, 0.3104122]]							)

													self.assertTrue(np.allclose(outputs.logits_per_image			,    _SCREAMING_SNAKE_CASE			,    atol=1e-3							)							)














"""simple docstring"""



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

import requests




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Union[str, Any]=None      )		->Tuple:
						A__						:  Dict		  =  None
						if token is not None:
												A__						:  str		  =  {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

						A__						:  Dict		  =  f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
						A__						:  Any		  =  requests.get(UpperCAmelCase__,						headers=UpperCAmelCase__      ).json()
						A__						:  Tuple		  =  {}

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

												for i in range(UpperCAmelCase__      ):
																		A__						:  str		  =  requests.get(url + f'&page={i + 2}',						headers=UpperCAmelCase__      ).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		(							UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					str=None      )		->List[str]:
						A__						:  Optional[Any]		  =  None
						if token is not None:
												A__						:  Any		  =  {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

						A__						:  str		  =  f'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
						A__						:  Dict		  =  requests.get(UpperCAmelCase__,						headers=UpperCAmelCase__      ).json()
						A__						:  Any		  =  {}

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

												for i in range(UpperCAmelCase__      ):
																		A__						:  Union[str, Any]		  =  requests.get(url + f'&page={i + 2}',						headers=UpperCAmelCase__      ).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		(							UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					Any      )		->Tuple:
						A__						:  Tuple		  =  None
						if token is not None:
												A__						:  List[Any]		  =  {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

						A__						:  Tuple		  =  requests.get(UpperCAmelCase__,						headers=UpperCAmelCase__,						allow_redirects=UpperCAmelCase__      )
						A__						:  Dict		  =  result.headers["""Location"""]
						A__						:  Union[str, Any]		  =  requests.get(UpperCAmelCase__,						allow_redirects=UpperCAmelCase__      )
						A__						:  int		  =  os.path.join(UpperCAmelCase__,						f'{artifact_name}.zip'      )
						with open(UpperCAmelCase__,						"""wb"""      ) as fp:
												fp.write(response.content      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Tuple=None      )		->Tuple:
						A__						:  int		  =  []
						A__						:  Union[str, Any]		  =  []
						A__						:  Optional[Any]		  =  None

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

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

						A__						:  List[str]		  =  None
						if job_name and job_links:
												A__						:  Any		  =  job_links.get(UpperCAmelCase__,						UpperCAmelCase__      )

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

						return result




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					int=None      )		->str:
						A__						:  List[Any]		  =  []

						A__						:  Dict		  =  [os.path.join(UpperCAmelCase__,						UpperCAmelCase__      ) for p in os.listdir(UpperCAmelCase__      ) if p.endswith(""".zip"""      )]
						for p in paths:
												errors.extend(get_errors_from_single_artifact(UpperCAmelCase__,						job_links=UpperCAmelCase__      )      )

						return errors




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Dict=None      )		->List[Any]:
						A__						:  Dict		  =  Counter()
						counter.update([x[1] for x in logs]      )
						A__						:  str		  =  counter.most_common()
						A__						:  Dict		  =  {}
						for error, count in counts:
												if error_filter is None or error not in error_filter:
																		A__						:  Optional[int]		  =  {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any]      )		->str:
						A__						:  List[str]		  =  test.split("""::"""      )[0]
						if test.startswith("""tests/models/"""      ):
												A__						:  Union[str, Any]		  =  test.split("""/"""      )[2]
						else:
												A__						:  int		  =  None

						return test




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					str=None      )		->Optional[Any]:
						A__						:  Any		  =  [(x[0], x[1], get_model(x[2]      )) for x in logs]
						A__						:  List[Any]		  =  [x for x in logs if x[2] is not None]
						A__						:  Union[str, Any]		  =  {x[2] for x in logs}

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

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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict      )		->List[Any]:
						A__						:  List[Any]		  =  """| no. | error | status |"""
						A__						:  Union[str, Any]		  =  """|-:|:-|:-|"""
						A__						:  Dict		  =  [header, sep]
						for error in reduced_by_error:
												A__						:  List[Any]		  =  reduced_by_error[error]["""count"""]
												A__						:  List[Any]		  =  f'| {count} | {error[:1_0_0]} |  |'
												lines.append(UpperCAmelCase__      )

						return "\n".join(UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->int:
						A__						:  str		  =  """| model | no. of errors | major error | count |"""
						A__						:  Optional[int]		  =  """|-:|-:|-:|-:|"""
						A__						:  Tuple		  =  [header, sep]
						for model in reduced_by_model:
												A__						:  Optional[Any]		  =  reduced_by_model[model]["""count"""]
												A__  ,  A__						:  Optional[Any]		  =  list(reduced_by_model[model]["""errors"""].items()      )[0]
												A__						:  Optional[int]		  =  f'| {model} | {count} | {error[:6_0]} | {_count} |'
												lines.append(UpperCAmelCase__      )

						return "\n".join(UpperCAmelCase__      )


if __name__ == "__main__":
						A_    =							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_    =							parser.parse_args()

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

						A_    =							get_job_links(args.workflow_run_id, token=args.token)
						A_    =							{}
						# 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_    =							k.find(''' / ''')
																								A_    =							k[index + len(''' / ''') :]
																		A_    =							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_    =							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_    =							get_all_errors(args.output_dir, job_links=job_links)

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

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

						A_    =							reduce_by_error(errors)
						A_    =							reduce_by_model(errors)

						A_    =							make_github_table(reduced_by_error)
						A_    =							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)






