import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


A_:   Tuple   =      logging.get_logger(__name__)


def        __lowerCAmelCase      (	_A			,_A       ):



							"""simple docstring"""


							_lowercase					=						set()
							_lowercase					=						[]

							def parse_line(_A       ):
														for line in fp:
																					if isinstance(_A			,_A       ):
																												_lowercase					=						line.decode("""UTF-8"""       )
																					if "warnings summary (final)" in line:
																												continue
																					# This means we are outside the body of a warning
																					elif not line.startswith(""" """       ):
																												# process a single warning and move it to `selected_warnings`.
																												if len(_A       ) > 0:
																																			_lowercase					=						"""\n""".join(_A       )
																																			# Only keep the warnings specified in `targets`
																																			if any(f''': {x}: ''' in warning for x in targets       ):
																																										selected_warnings.add(_A       )
																																			buffer.clear()
																												continue
																					else:
																												_lowercase					=						line.strip()
																												buffer.append(_A       )

							if from_gh:
														for filename in os.listdir(_A       ):
																					_lowercase					=						os.path.join(_A			,_A       )
																					if not os.path.isdir(_A       ):
																												# read the file
																												if filename != "warnings.txt":
																																			continue
																												with open(_A       ) as fp:
																																			parse_line(_A       )
							else:
														try:
																					with zipfile.ZipFile(_A       ) as z:
																												for filename in z.namelist():
																																			if not os.path.isdir(_A       ):
																																										# read the file
																																										if filename != "warnings.txt":
																																																	continue
																																										with z.open(_A       ) as fp:
																																																	parse_line(_A       )
														except Exception:
																					logger.warning(
																					    f'''{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'''       )

							return selected_warnings


def        __lowerCAmelCase      (	_A			,_A       ):



							"""simple docstring"""


							_lowercase					=						set()

							_lowercase					=						[os.path.join(_A			,_A       ) for p in os.listdir(_A       ) if (p.endswith(""".zip"""       ) or from_gh)]
							for p in paths:
														selected_warnings.update(extract_warnings_from_single_artifact(_A			,_A       )       )

							return selected_warnings


if __name__ == "__main__":


				def        __lowerCAmelCase      (	_A       ):



											"""simple docstring"""


											return values.split(""","""       )

				A_:   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.')
				# optional parameters
				parser.add_argument(
				    '--targets',
				    default='DeprecationWarning,UserWarning,FutureWarning',
				    type=list_str,
				    help='Comma-separated list of target warning(s) which we want to extract.',
				)
				parser.add_argument(
				    '--from_gh',
				    action='store_true',
				    help='If running from a GitHub action workflow and collecting warnings from its artifacts.',
				)

				A_:   Dict   =      parser.parse_args()

				A_:   List[str]   =      args.from_gh
				if from_gh:
								# The artifacts have to be downloaded using `actions/download-artifact@v3`
								pass
				else:
								os.makedirs(args.output_dir, exist_ok=True)

								# get download links
								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)

								# download artifacts
								for idx, (name, url) in enumerate(artifacts.items()):
												print(name)
												print(url)
												print('=' * 80)
												download_artifact(name, url, args.output_dir, args.token)
												# Be gentle to GitHub
												time.sleep(1)

    # extract warnings from artifacts
				A_:   int   =      extract_warnings(args.output_dir, args.targets)
				A_:   Dict   =      sorted(selected_warnings)
				with open(os.path.join(args.output_dir, 'selected_warnings.json'), 'w', encoding='UTF-8') as fp:
								json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)




import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class     lowerCamelCase      (       __lowerCamelCase ,		__lowerCamelCase					):







	@register_to_config
	def __init__(     self      :Tuple   ,     *,
	    lowercase      :int = 4   ,     lowercase      :int = 7_6_8   ,     lowercase      :int   ,     lowercase      :Any   ,     )		->      Optional[Any]:







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE     						= nn.Parameter(torch.zeros(lowercase			)			)

				# parameters for additional clip time embeddings
				SCREAMING_SNAKE_CASE     						= nn.Linear(lowercase   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= nn.Linear(lowercase   ,     lowercase			)

				# parameters for encoder hidden states
				SCREAMING_SNAKE_CASE     						= clip_extra_context_tokens
				SCREAMING_SNAKE_CASE     						= nn.Linear(
				    lowercase   ,     self.clip_extra_context_tokens * cross_attention_dim			)
				SCREAMING_SNAKE_CASE     						= nn.Linear(lowercase   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= nn.LayerNorm(lowercase			)







	def  snake_case__     (     self      :Union[str, Any]   ,     *, lowercase      :Optional[int]   ,     lowercase      :Union[str, Any]   ,     lowercase      :Union[str, Any]   ,     lowercase      :Optional[Any]			)		->      Union[str, Any]:







				"""simple docstring"""






				if do_classifier_free_guidance:
							# Add the classifier free guidance embeddings to the image embeddings
							SCREAMING_SNAKE_CASE     						= image_embeddings.shape[0]
							SCREAMING_SNAKE_CASE     						= self.learned_classifier_free_guidance_embeddings.unsqueeze(0			)
							SCREAMING_SNAKE_CASE     						= classifier_free_guidance_embeddings.expand(
							    lowercase   ,     -1			)
							SCREAMING_SNAKE_CASE     						= torch.cat([classifier_free_guidance_embeddings, image_embeddings]   ,     dim=0			)

				# The image embeddings batch size and the text embeddings batch size are equal
				assert image_embeddings.shape[0] == prompt_embeds.shape[0]

				SCREAMING_SNAKE_CASE     						= prompt_embeds.shape[0]

				# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
				# adding CLIP embeddings to the existing timestep embedding, ...
				SCREAMING_SNAKE_CASE     						= self.embedding_proj(lowercase			)
				SCREAMING_SNAKE_CASE     						= self.clip_image_embeddings_project_to_time_embeddings(lowercase			)
				SCREAMING_SNAKE_CASE     						= time_projected_image_embeddings + time_projected_prompt_embeds

				# ... and by projecting CLIP embeddings into four
				# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
				SCREAMING_SNAKE_CASE     						= self.clip_extra_context_tokens_proj(lowercase			)
				SCREAMING_SNAKE_CASE     						= clip_extra_context_tokens.reshape(lowercase   ,     -1   ,     self.clip_extra_context_tokens			)
				SCREAMING_SNAKE_CASE     						= clip_extra_context_tokens.permute(0   ,     2   ,     1			)

				SCREAMING_SNAKE_CASE     						= self.encoder_hidden_states_proj(lowercase			)
				SCREAMING_SNAKE_CASE     						= self.text_encoder_hidden_states_norm(lowercase			)
				SCREAMING_SNAKE_CASE     						= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]   ,     dim=1			)

				return text_encoder_hidden_states, additive_clip_time_embeddings