






"""simple docstring"""



from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


A_    =							logging.get_logger(__name__)


# General docstring
A_    =							'''MobileNetV1Config'''

# Base docstring
A_    =							'''google/mobilenet_v1_1.0_224'''
A_    =							[1, 1024, 7, 7]

# Image classification docstring
A_    =							'''google/mobilenet_v1_1.0_224'''
A_    =							'''tabby, tabby cat'''


A_    =							[
    '''google/mobilenet_v1_1.0_224''',
    '''google/mobilenet_v1_0.75_192''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Tuple=None      )		->Dict:
						A__						:  Union[str, Any]		  =  {}

						if isinstance(UpperCAmelCase__,						UpperCAmelCase__      ):
												A__						:  Optional[Any]		  =  model.mobilenet_va
						else:
												A__						:  Optional[Any]		  =  model

						A__						:  Tuple		  =  """MobilenetV1/Conv2d_0/"""
						A__						:  int		  =  backbone.conv_stem.convolution.weight
						A__						:  Optional[Any]		  =  backbone.conv_stem.normalization.bias
						A__						:  Optional[Any]		  =  backbone.conv_stem.normalization.weight
						A__						:  Optional[Any]		  =  backbone.conv_stem.normalization.running_mean
						A__						:  Tuple		  =  backbone.conv_stem.normalization.running_var

						for i in range(1_3      ):
												A__						:  str		  =  i + 1
												A__						:  Optional[Any]		  =  i * 2

												A__						:  Optional[int]		  =  backbone.layer[pt_index]
												A__						:  List[Any]		  =  f'MobilenetV1/Conv2d_{tf_index}_depthwise/'
												A__						:  Tuple		  =  pointer.convolution.weight
												A__						:  Optional[Any]		  =  pointer.normalization.bias
												A__						:  str		  =  pointer.normalization.weight
												A__						:  Any		  =  pointer.normalization.running_mean
												A__						:  Dict		  =  pointer.normalization.running_var

												A__						:  str		  =  backbone.layer[pt_index + 1]
												A__						:  List[str]		  =  f'MobilenetV1/Conv2d_{tf_index}_pointwise/'
												A__						:  int		  =  pointer.convolution.weight
												A__						:  Any		  =  pointer.normalization.bias
												A__						:  Any		  =  pointer.normalization.weight
												A__						:  Any		  =  pointer.normalization.running_mean
												A__						:  Optional[Any]		  =  pointer.normalization.running_var

						if isinstance(UpperCAmelCase__,						UpperCAmelCase__      ):
												A__						:  Optional[int]		  =  """MobilenetV1/Logits/Conv2d_1c_1x1/"""
												A__						:  int		  =  model.classifier.weight
												A__						:  str		  =  model.classifier.bias

						return tf_to_pt_map




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Union[str, Any]      )		->Optional[Any]:
						try:
												import numpy as np
												import tensorflow as tf
						except ImportError:
												logger.error(
												    """Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see """
												    """https://www.tensorflow.org/install/ for installation instructions."""      )
												raise

						# Load weights from TF model
						A__						:  Optional[int]		  =  tf.train.list_variables(UpperCAmelCase__      )
						A__						:  int		  =  {}
						for name, shape in init_vars:
												logger.info(f'Loading TF weight {name} with shape {shape}'      )
												A__						:  Tuple		  =  tf.train.load_variable(UpperCAmelCase__,						UpperCAmelCase__      )
												A__						:  Tuple		  =  array

						# Build TF to PyTorch weights loading map
						A__						:  Union[str, Any]		  =  _build_tf_to_pytorch_map(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )

						for name, pointer in tf_to_pt_map.items():
												logger.info(f'Importing {name}'      )
												if name not in tf_weights:
																		logger.info(f'{name} not in tf pre-trained weights, skipping'      )
																		continue

												A__						:  Dict		  =  tf_weights[name]

												if "depthwise_weights" in name:
																		logger.info("""Transposing depthwise"""      )
																		A__						:  List[Any]		  =  np.transpose(UpperCAmelCase__,						(2, 3, 0, 1)      )
												elif "weights" in name:
																		logger.info("""Transposing"""      )
																		if len(pointer.shape      ) == 2:  # copying into linear layer
																								A__						:  int		  =  array.squeeze().transpose()
																		else:
																								A__						:  Dict		  =  np.transpose(UpperCAmelCase__,						(3, 2, 0, 1)      )

												if pointer.shape != array.shape:
																		raise ValueError(f'Pointer shape {pointer.shape} and array shape {array.shape} mismatched'      )

												logger.info(f'Initialize PyTorch weight {name} {array.shape}'      )
												A__						:  Optional[Any]		  =  torch.from_numpy(UpperCAmelCase__      )

												tf_weights.pop(UpperCAmelCase__,						UpperCAmelCase__      )
												tf_weights.pop(name + """/RMSProp""",						UpperCAmelCase__      )
												tf_weights.pop(name + """/RMSProp_1""",						UpperCAmelCase__      )
												tf_weights.pop(name + """/ExponentialMovingAverage""",						UpperCAmelCase__      )

						logger.info(f'Weights not copied to PyTorch model: {", ".join(tf_weights.keys()      )}'      )
						return model




def      _lowerCAmelCase		(							UpperCAmelCase__       :					torch.Tensor,						UpperCAmelCase__       :					nn.Convad      )		->torch.Tensor:
						A__  ,  A__						:  int		  =  features.shape[-2:]
						A__  ,  A__						:  Any		  =  conv_layer.stride
						A__  ,  A__						:  Optional[Any]		  =  conv_layer.kernel_size

						if in_height % stride_height == 0:
												A__						:  int		  =  max(kernel_height - stride_height,						0      )
						else:
												A__						:  List[str]		  =  max(kernel_height - (in_height % stride_height),						0      )

						if in_width % stride_width == 0:
												A__						:  Optional[Any]		  =  max(kernel_width - stride_width,						0      )
						else:
												A__						:  Union[str, Any]		  =  max(kernel_width - (in_width % stride_width),						0      )

						A__						:  List[Any]		  =  pad_along_width // 2
						A__						:  Dict		  =  pad_along_width - pad_left
						A__						:  Optional[Any]		  =  pad_along_height // 2
						A__						:  int		  =  pad_along_height - pad_top

						A__						:  Any		  =  (pad_left, pad_right, pad_top, pad_bottom)
						return nn.functional.pad(UpperCAmelCase__,						UpperCAmelCase__,						"""constant""",						0.0      )






class 							__SCREAMING_SNAKE_CASE						(				nn.Module		):


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						MobileNetVaConfig			,    snake_case      :						int			,    snake_case      :						int			,    snake_case      :						int			,    snake_case      :						Optional[int] = 1			,    snake_case      :						Optional[int] = 1			,    snake_case      :						bool = False			,    snake_case      :						Optional[bool] = True			,    snake_case      :						Optional[bool or str] = True			,    ):
													'''simple docstring'''





													super().__init__()
													A__						:  List[Any]		  =  config

													if in_channels % groups != 0:
																			raise ValueError(F'Input channels ({in_channels}) are not divisible by {groups} groups.'							)
													if out_channels % groups != 0:
																			raise ValueError(F'Output channels ({out_channels}) are not divisible by {groups} groups.'							)

													A__						:  List[str]		  =  0 if config.tf_padding else int((kernel_size - 1) / 2							)

													A__						:  int		  =  nn.Convad(
													    in_channels=snake_case			,    out_channels=snake_case			,    kernel_size=snake_case			,    stride=snake_case			,    padding=snake_case			,    groups=snake_case			,    bias=snake_case			,    padding_mode="""zeros"""			,    )

													if use_normalization:
																			A__						:  str		  =  nn.BatchNormad(
																			    num_features=snake_case			,    eps=config.layer_norm_eps			,    momentum=0.9997			,    affine=snake_case			,    track_running_stats=snake_case			,    )
													else:
																			A__						:  Any		  =  None

													if use_activation:
																			if isinstance(snake_case			,    snake_case							):
																									A__						:  List[str]		  =  ACTaFN[use_activation]
																			elif isinstance(config.hidden_act			,    snake_case							):
																									A__						:  Union[str, Any]		  =  ACTaFN[config.hidden_act]
																			else:
																									A__						:  Dict		  =  config.hidden_act
													else:
																			A__						:  Union[str, Any]		  =  None


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						torch.Tensor							):
													'''simple docstring'''





													if self.config.tf_padding:
																			A__						:  Union[str, Any]		  =  apply_tf_padding(snake_case			,    self.convolution							)
													A__						:  List[str]		  =  self.convolution(snake_case							)
													if self.normalization is not None:
																			A__						:  List[Any]		  =  self.normalization(snake_case							)
													if self.activation is not None:
																			A__						:  List[Any]		  =  self.activation(snake_case							)
													return features






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							MobileNetVaConfig
							snake_case_     =							load_tf_weights_in_mobilenet_va
							snake_case_     =							'mobilenet_v1'
							snake_case_     =							'pixel_values'
							snake_case_     =							False


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Union[nn.Linear, nn.Convad]							):
													'''simple docstring'''





													if isinstance(snake_case			,    (nn.Linear, nn.Convad)							):
																			module.weight.data.normal_(mean=0.0			,    std=self.config.initializer_range							)
																			if module.bias is not None:
																									module.bias.data.zero_()
													elif isinstance(snake_case			,    nn.BatchNormad							):
																			module.bias.data.zero_()
																			module.weight.data.fill_(1.0							)




A_    =							r'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

A_    =							r'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''






@add_start_docstrings(
    'The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'       ,		UpperCamelCase       ,		)
class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):


							def __init__(			self      :						int			,    snake_case      :						MobileNetVaConfig			,    snake_case      :						bool = True							):
													'''simple docstring'''





													super().__init__(snake_case							)
													A__						:  List[str]		  =  config

													A__						:  int		  =  32
													A__						:  str		  =  max(int(depth * config.depth_multiplier							)			,    config.min_depth							)

													A__						:  Tuple		  =  MobileNetVaConvLayer(
													    snake_case			,    in_channels=config.num_channels			,    out_channels=snake_case			,    kernel_size=3			,    stride=2			,    )

													A__						:  Union[str, Any]		  =  [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

													A__						:  Any		  =  nn.ModuleList()
													for i in range(13							):
																			A__						:  Union[str, Any]		  =  out_channels

																			if strides[i] == 2 or i == 0:
																									depth *= 2
																									A__						:  Optional[int]		  =  max(int(depth * config.depth_multiplier							)			,    config.min_depth							)

																			self.layer.append(
																			    MobileNetVaConvLayer(
																			        snake_case			,    in_channels=snake_case			,    out_channels=snake_case			,    kernel_size=3			,    stride=strides[i]			,    groups=snake_case			,    )							)

																			self.layer.append(
																			    MobileNetVaConvLayer(
																			        snake_case			,    in_channels=snake_case			,    out_channels=snake_case			,    kernel_size=1			,    )							)

													A__						:  Dict		  =  nn.AdaptiveAvgPoolad((1, 1)							) if add_pooling_layer else None

													# Initialize weights and apply final processing
													self.post_init()


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





													raise NotImplementedError


							@add_start_docstrings_to_model_forward(snake_case							)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC			,    output_type=snake_case			,    config_class=_CONFIG_FOR_DOC			,    modality="""vision"""			,    expected_output=_EXPECTED_OUTPUT_SHAPE			,    )
							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Optional[torch.Tensor] = None			,    snake_case      :						Optional[bool] = None			,    snake_case      :						Optional[bool] = None			,    ):
													'''simple docstring'''





													A__						:  Tuple		  =  (
													    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
													)
													A__						:  Tuple		  =  return_dict if return_dict is not None else self.config.use_return_dict

													if pixel_values is None:
																			raise ValueError("""You have to specify pixel_values"""							)

													A__						:  Union[str, Any]		  =  self.conv_stem(snake_case							)

													A__						:  List[Any]		  =  () if output_hidden_states else None

													for i, layer_module in enumerate(self.layer							):
																			A__						:  Optional[Any]		  =  layer_module(snake_case							)

																			if output_hidden_states:
																									A__						:  Optional[int]		  =  all_hidden_states + (hidden_states,)

													A__						:  Tuple		  =  hidden_states

													if self.pooler is not None:
																			A__						:  Any		  =  torch.flatten(self.pooler(snake_case							)			,    start_dim=1							)
													else:
																			A__						:  Tuple		  =  None

													if not return_dict:
																			return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None							)

													return BaseModelOutputWithPoolingAndNoAttention(
													    last_hidden_state=snake_case			,    pooler_output=snake_case			,    hidden_states=snake_case			,    )






@add_start_docstrings(
    '\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '       ,		UpperCamelCase       ,		)
class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):


							def __init__(			self      :						Any			,    snake_case      :						MobileNetVaConfig							):
													'''simple docstring'''





													super().__init__(snake_case							)

													A__						:  Tuple		  =  config.num_labels
													A__						:  Any		  =  MobileNetVaModel(snake_case							)

													A__						:  Union[str, Any]		  =  self.mobilenet_va.layer[-1].convolution.out_channels

													# Classifier head
													A__						:  Union[str, Any]		  =  nn.Dropout(config.classifier_dropout_prob			,    inplace=snake_case							)
													A__						:  Tuple		  =  nn.Linear(snake_case			,    config.num_labels							) if config.num_labels > 0 else nn.Identity()

													# Initialize weights and apply final processing
													self.post_init()


							@add_start_docstrings_to_model_forward(snake_case							)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT			,    output_type=snake_case			,    config_class=_CONFIG_FOR_DOC			,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT			,    )
							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						Optional[torch.Tensor] = None			,    snake_case      :						Optional[bool] = None			,    snake_case      :						Optional[torch.Tensor] = None			,    snake_case      :						Optional[bool] = None			,    ):
													'''simple docstring'''





													A__						:  str		  =  return_dict if return_dict is not None else self.config.use_return_dict

													A__						:  Any		  =  self.mobilenet_va(snake_case			,    output_hidden_states=snake_case			,    return_dict=snake_case							)

													A__						:  Optional[Any]		  =  outputs.pooler_output if return_dict else outputs[1]

													A__						:  Optional[Any]		  =  self.classifier(self.dropout(snake_case							)							)

													A__						:  List[str]		  =  None
													if labels is not None:
																			if self.config.problem_type is None:
																									if self.num_labels == 1:
																															A__						:  Optional[Any]		  =  """regression"""
																									elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																															A__						:  str		  =  """single_label_classification"""
																									else:
																															A__						:  Dict		  =  """multi_label_classification"""

																			if self.config.problem_type == "regression":
																									A__						:  Dict		  =  MSELoss()
																									if self.num_labels == 1:
																															A__						:  Any		  =  loss_fct(logits.squeeze()			,    labels.squeeze()							)
																									else:
																															A__						:  Dict		  =  loss_fct(snake_case			,    snake_case							)
																			elif self.config.problem_type == "single_label_classification":
																									A__						:  List[Any]		  =  CrossEntropyLoss()
																									A__						:  Union[str, Any]		  =  loss_fct(logits.view(-1			,    self.num_labels							)			,    labels.view(-1							)							)
																			elif self.config.problem_type == "multi_label_classification":
																									A__						:  Any		  =  BCEWithLogitsLoss()
																									A__						:  Dict		  =  loss_fct(snake_case			,    snake_case							)

													if not return_dict:
																			A__						:  int		  =  (logits,) + outputs[2:]
																			return ((loss,) + output) if loss is not None else output

													return ImageClassifierOutputWithNoAttention(
													    loss=snake_case			,    logits=snake_case			,    hidden_states=outputs.hidden_states			,    )














"""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)






