



import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=7   ):
	__lowercase		       =			None
	if token is not None:
		__lowercase		       =			{"Accept": "application/vnd.github+json", "Authorization": F"""Bearer {token}"""}

	# The id of a workflow (not of a workflow run)
	__lowercase		       =			"636036"

	__lowercase		       =			F"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
	# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
	url += F"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

	__lowercase		       =			requests.get(_SCREAMING_SNAKE_CASE  ,	headers=_SCREAMING_SNAKE_CASE   ).json()

	return result["workflow_runs"]



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			get_daily_ci_runs(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			None
	for workflow_run in workflow_runs:
		if workflow_run["status"] == "completed":
			__lowercase		       =			workflow_run["id"]
			break

	return workflow_run_id



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			get_last_daily_ci_runs(_SCREAMING_SNAKE_CASE   )
	if workflow_run_id is not None:
		__lowercase		       =			get_artifacts_links(worflow_run_id=_SCREAMING_SNAKE_CASE  ,	token=_SCREAMING_SNAKE_CASE   )
		for artifact_name in artifact_names:
			if artifact_name in artifacts_links:
				__lowercase		       =			artifacts_links[artifact_name]
				download_artifact(
				    artifact_name=_SCREAMING_SNAKE_CASE  ,	artifact_url=_SCREAMING_SNAKE_CASE  ,	output_dir=_SCREAMING_SNAKE_CASE  ,	token=_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	get_last_daily_ci_artifacts(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

	__lowercase		       =			{}
	for artifact_name in artifact_names:
		__lowercase		       =			os.path.join(_SCREAMING_SNAKE_CASE  ,	F"""{artifact_name}.zip"""   )
		if os.path.isfile(_SCREAMING_SNAKE_CASE   ):
			__lowercase		       =			{}
			with zipfile.ZipFile(_SCREAMING_SNAKE_CASE   ) as z:
				for filename in z.namelist():
					if not os.path.isdir(_SCREAMING_SNAKE_CASE   ):
						# read the file
						with z.open(_SCREAMING_SNAKE_CASE   ) as f:
							__lowercase		       =			f.read().decode("UTF-8"   )

	return results




import torch
from torch import nn

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

class   _A   ( _lowercase  ,						_lowercase	):


						'''simple docstring'''





						@register_to_config
						def __init__(	self	:							Optional[Any]							,      *,
						    lowerCamelCase	:							int = 4							,      lowerCamelCase	:							int = 768							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Optional[int]							,      ):






							'''simple docstring'''



							super().__init__()

							__lowercase		       =			nn.Parameter(torch.zeros(lowerCamelCase	)	)

							# parameters for additional clip time embeddings
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)

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



						def     _snake_case	(	self	:							Union[str, Any]							,      *, lowerCamelCase	:							Any							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Tuple	):






							'''simple docstring'''



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

							__lowercase		       =			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, ...
							__lowercase		       =			self.embedding_proj(lowerCamelCase	)
							__lowercase		       =			self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase	)
							__lowercase		       =			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"
							__lowercase		       =			self.clip_extra_context_tokens_proj(lowerCamelCase	)
							__lowercase		       =			clip_extra_context_tokens.reshape(lowerCamelCase							,      -1							,      self.clip_extra_context_tokens	)
							__lowercase		       =			clip_extra_context_tokens.permute(0							,      2							,      1	)

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

							return text_encoder_hidden_states, additive_clip_time_embeddings



