

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

import requests







def      snake_case		(							UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       Any=None   ):
				A	 =      None
				if token is not None:
								A	 =      {'Accept': 'application/vnd.github+json', 'Authorization': f'Bearer {token}'}

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

				try:
								job_links.update({job['name']: job['html_url'] for job in result['jobs']}   )
								A	 =      math.ceil((result['total_count'] - 1_00) / 1_00   )

								for i in range(UpperCAmelCase   ):
												A	 =      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      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       int=None   ):
				A	 =      None
				if token is not None:
								A	 =      {'Accept': 'application/vnd.github+json', 'Authorization': f'Bearer {token}'}

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

				try:
								artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}   )
								A	 =      math.ceil((result['total_count'] - 1_00) / 1_00   )

								for i in range(UpperCAmelCase   ):
												A	 =      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      snake_case		(							UpperCAmelCase	:       Any,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       int,						UpperCAmelCase	:       int   ):
				A	 =      None
				if token is not None:
								A	 =      {'Accept': 'application/vnd.github+json', 'Authorization': f'Bearer {token}'}

				A	 =      requests.get(UpperCAmelCase,						headers=UpperCAmelCase,						allow_redirects=UpperCAmelCase   )
				A	 =      result.headers['Location']
				A	 =      requests.get(UpperCAmelCase,						allow_redirects=UpperCAmelCase   )
				A	 =      os.path.join(UpperCAmelCase,						f'{artifact_name}.zip'   )
				with open(UpperCAmelCase,						'wb'   ) as fp:
								fp.write(response.content   )







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

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

				return result







def      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       str=None   ):
				A	 =      []

				A	 =      [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      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       Optional[Any]=None   ):
				A	 =      Counter()
				counter.update([x[1] for x in logs]   )
				A	 =      counter.most_common()
				A	 =      {}
				for error, count in counts:
								if error_filter is None or error not in error_filter:
												A	 =      {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

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







def      snake_case		(							UpperCAmelCase	:       List[str]   ):
				A	 =      test.split('::'   )[0]
				if test.startswith('tests/models/'   ):
								A	 =      test.split('/'   )[2]
				else:
								A	 =      None

				return test







def      snake_case		(							UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       Union[str, Any]=None   ):
				A	 =      [(x[0], x[1], get_model(x[2]   )) for x in logs]
				A	 =      [x for x in logs if x[2] is not None]
				A	 =      {x[2] for x in logs}

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

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







def      snake_case		(							UpperCAmelCase	:       List[str]   ):
				A	 =      '| no. | error | status |'
				A	 =      '|-:|:-|:-|'
				A	 =      [header, sep]
				for error in reduced_by_error:
								A	 =      reduced_by_error[error]['count']
								A	 =      f'| {count} | {error[:1_00]} |  |'
								lines.append(UpperCAmelCase   )

				return "\n".join(UpperCAmelCase   )







def      snake_case		(							UpperCAmelCase	:       Any   ):
				A	 =      '| model | no. of errors | major error | count |'
				A	 =      '|-:|-:|-:|-:|'
				A	 =      [header, sep]
				for model in reduced_by_model:
								A	 =      reduced_by_model[model]['count']
								A ,		A	 =      list(reduced_by_model[model]['errors'].items()   )[0]
								A	 =      f'| {model} | {count} | {error[:60]} | {_count} |'
								lines.append(UpperCAmelCase   )

				return "\n".join(UpperCAmelCase   )


if __name__ == "__main__":
					lowerCAmelCase_           = 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.')
					lowerCAmelCase_           = parser.parse_args()

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

					lowerCAmelCase_           = get_job_links(args.workflow_run_id, token=args.token)
					lowerCAmelCase_           = {}
					# 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:
																				lowerCAmelCase_           = k.find(' / ')
																				lowerCAmelCase_           = k[index + len(' / ') :]
															lowerCAmelCase_           = 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)

					lowerCAmelCase_           = 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)

					lowerCAmelCase_           = get_all_errors(args.output_dir, job_links=job_links)

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

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

					lowerCAmelCase_           = reduce_by_error(errors)
					lowerCAmelCase_           = reduce_by_model(errors)

					lowerCAmelCase_           = make_github_table(reduced_by_error)
					lowerCAmelCase_           = 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)













from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class      UpperCamelCase (				snake_case__			,       snake_case__			,       snake_case__ ):







		"""simple docstring"""



		snake_case    =   [R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]
		@register_to_config
		def __init__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       int = 5_0_2_5_7						,_SCREAMING_SNAKE_CASE		:       int = 1_0_2_4						,_SCREAMING_SNAKE_CASE		:       int = 7_6_8						,_SCREAMING_SNAKE_CASE		:       int = 1_2						,_SCREAMING_SNAKE_CASE		:       int = 1_2						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       str = "gelu_new"						,_SCREAMING_SNAKE_CASE		:       float = 0.1						,_SCREAMING_SNAKE_CASE		:       float = 0.1						,_SCREAMING_SNAKE_CASE		:       float = 0.1						,_SCREAMING_SNAKE_CASE		:       float = 1E-5						,_SCREAMING_SNAKE_CASE		:       float = 0.02						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,)    ->   Tuple:


						'''simple docstring'''
						super().__init__()

						A	 =      prefix_length

						if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
										raise ValueError(
										    f'`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'
										    f' `n_embd`: {n_embd} are not equal.'		)

						A	 =      prefix_inner_dim
						A	 =      prefix_hidden_dim

						A	 =      (
						    nn.Linear(self.prefix_inner_dim						,self.prefix_hidden_dim		)
						    if self.prefix_hidden_dim is not None
						    else nn.Identity()
						)
						A	 =      (
						    nn.Linear(self.prefix_hidden_dim						,_SCREAMING_SNAKE_CASE		) if self.prefix_hidden_dim is not None else nn.Identity()
						)

						A	 =      GPTaConfig(
						    vocab_size=_SCREAMING_SNAKE_CASE						,n_positions=_SCREAMING_SNAKE_CASE						,n_embd=_SCREAMING_SNAKE_CASE						,n_layer=_SCREAMING_SNAKE_CASE						,n_head=_SCREAMING_SNAKE_CASE						,n_inner=_SCREAMING_SNAKE_CASE						,activation_function=_SCREAMING_SNAKE_CASE						,resid_pdrop=_SCREAMING_SNAKE_CASE						,embd_pdrop=_SCREAMING_SNAKE_CASE						,attn_pdrop=_SCREAMING_SNAKE_CASE						,layer_norm_epsilon=_SCREAMING_SNAKE_CASE						,initializer_range=_SCREAMING_SNAKE_CASE						,scale_attn_weights=_SCREAMING_SNAKE_CASE						,use_cache=_SCREAMING_SNAKE_CASE						,scale_attn_by_inverse_layer_idx=_SCREAMING_SNAKE_CASE						,reorder_and_upcast_attn=_SCREAMING_SNAKE_CASE						,)
						A	 =      GPTaLMHeadModel(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       torch.Tensor						,_SCREAMING_SNAKE_CASE		:       torch.Tensor						,_SCREAMING_SNAKE_CASE		:       Optional[torch.Tensor] = None						,_SCREAMING_SNAKE_CASE		:       Optional[torch.Tensor] = None						,)    ->   Tuple:


						'''simple docstring'''
						A	 =      self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE		)
						A	 =      self.encode_prefix(_SCREAMING_SNAKE_CASE		)
						A	 =      self.decode_prefix(_SCREAMING_SNAKE_CASE		)
						A	 =      torch.cat((prefix_embeds, embedding_text)						,dim=1		)

						if labels is not None:
										A	 =      self.get_dummy_token(input_ids.shape[0]						,input_ids.device		)
										A	 =      torch.cat((dummy_token, input_ids)						,dim=1		)
						A	 =      self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE						,labels=_SCREAMING_SNAKE_CASE						,attention_mask=_SCREAMING_SNAKE_CASE		)
						if self.prefix_hidden_dim is not None:
										return out, hidden
						else:
										return out
		def       A(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       torch.device		)    ->   torch.Tensor:


						'''simple docstring'''
						return torch.zeros(_SCREAMING_SNAKE_CASE						,self.prefix_length						,dtype=torch.intaa						,device=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Optional[Any]:


						'''simple docstring'''
						return self.encode_prefix(_SCREAMING_SNAKE_CASE		)
		@torch.no_grad()
		def       A(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   str:


						'''simple docstring'''
						A	 =      torch.split(_SCREAMING_SNAKE_CASE						,1						,dim=0		)
						A	 =      []
						A	 =      []
						for feature in features:
										A	 =      self.decode_prefix(feature.to(_SCREAMING_SNAKE_CASE		)		)  # back to the clip feature
										# Only support beam search for now
										A ,		A	 =      self.generate_beam(
										    input_embeds=_SCREAMING_SNAKE_CASE						,device=_SCREAMING_SNAKE_CASE						,eos_token_id=_SCREAMING_SNAKE_CASE		)
										generated_tokens.append(output_tokens[0]		)
										generated_seq_lengths.append(seq_lengths[0]		)
						A	 =      torch.stack(_SCREAMING_SNAKE_CASE		)
						A	 =      torch.stack(_SCREAMING_SNAKE_CASE		)
						return generated_tokens, generated_seq_lengths







		@torch.no_grad()
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       List[str]=None						,_SCREAMING_SNAKE_CASE		:       str=None						,_SCREAMING_SNAKE_CASE		:       Any=None						,_SCREAMING_SNAKE_CASE		:       int = 5						,_SCREAMING_SNAKE_CASE		:       int = 6_7						,_SCREAMING_SNAKE_CASE		:       float = 1.0						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,)    ->   str:


						'''simple docstring'''
						A	 =      eos_token_id
						A	 =      None
						A	 =      None
						A	 =      torch.ones(_SCREAMING_SNAKE_CASE						,device=_SCREAMING_SNAKE_CASE						,dtype=torch.int		)
						A	 =      torch.zeros(_SCREAMING_SNAKE_CASE						,device=_SCREAMING_SNAKE_CASE						,dtype=torch.bool		)

						if input_embeds is not None:
										A	 =      input_embeds
						else:
										A	 =      self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE		)

						for i in range(_SCREAMING_SNAKE_CASE		):
										A	 =      self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE		)
										A	 =      outputs.logits
										A	 =      logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
										A	 =      logits.softmax(-1		).log()

										if scores is None:
														A ,		A	 =      logits.topk(_SCREAMING_SNAKE_CASE						,-1		)
														A	 =      generated.expand(_SCREAMING_SNAKE_CASE						,*generated.shape[1:]		)
														A ,		A	 =      next_tokens.permute(1						,0		), scores.squeeze(0		)
														if tokens is None:
																		A	 =      next_tokens
														else:
																		A	 =      tokens.expand(_SCREAMING_SNAKE_CASE						,*tokens.shape[1:]		)
																		A	 =      torch.cat((tokens, next_tokens)						,dim=1		)
										else:
														A	 =      -float(np.inf		)
														A	 =      0
														A	 =      scores[:, None] + logits
														seq_lengths[~is_stopped] += 1
														A	 =      scores_sum / seq_lengths[:, None]
														A ,		A	 =      scores_sum_average.view(-1		).topk(_SCREAMING_SNAKE_CASE						,-1		)
														A	 =      next_tokens // scores_sum.shape[1]
														A	 =      seq_lengths[next_tokens_source]
														A	 =      next_tokens % scores_sum.shape[1]
														A	 =      next_tokens.unsqueeze(1		)
														A	 =      tokens[next_tokens_source]
														A	 =      torch.cat((tokens, next_tokens)						,dim=1		)
														A	 =      generated[next_tokens_source]
														A	 =      scores_sum_average * seq_lengths
														A	 =      is_stopped[next_tokens_source]

										A	 =      self.transformer.transformer.wte(next_tokens.squeeze()		).view(generated.shape[0]						,1						,-1		)
										A	 =      torch.cat((generated, next_token_embed)						,dim=1		)
										A	 =      is_stopped + next_tokens.eq(_SCREAMING_SNAKE_CASE		).squeeze()
										if is_stopped.all():
														break

						A	 =      scores / seq_lengths
						A	 =      scores.argsort(descending=_SCREAMING_SNAKE_CASE		)
						# tokens tensors are already padded to max_seq_length
						A	 =      [tokens[i] for i in order]
						A	 =      torch.stack(_SCREAMING_SNAKE_CASE						,dim=0		)
						A	 =      torch.tensor([seq_lengths[i] for i in order]						,dtype=seq_lengths.dtype		)
						return output_texts, seq_lengths





