





"""simple docstring"""
import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links






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

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

							A		=  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}"

							A		=  requests.get(UpperCamelCase					,  headers=UpperCamelCase       ).json()

							return result["workflow_runs"]






def 					A__				(			UpperCamelCase       ):
							A		=  get_daily_ci_runs(UpperCamelCase       )
							A		=  None
							for workflow_run in workflow_runs:
														if workflow_run["status"] == "completed":
																					A		=  workflow_run["id"]
																					break

							return workflow_run_id






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






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							get_last_daily_ci_artifacts(UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       )

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

							return results









"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration






def 					A__				(			UpperCamelCase       ):
							A		=  [
							    "encoder.version",
							    "decoder.version",
							    "model.encoder.version",
							    "model.decoder.version",
							    "decoder.output_projection.weight",
							    "_float_tensor",
							    "encoder.embed_positions._float_tensor",
							    "decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase					,  UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A		=  list(s_dict.keys()       )
							for key in keys:
														if "transformer_layers" in key:
																					A		=  s_dict.pop(UpperCamelCase       )
														elif "subsample" in key:
																					A		=  s_dict.pop(UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A,	A		=  emb.weight.shape
							A		=  nn.Linear(UpperCamelCase					,  UpperCamelCase					,  bias=UpperCamelCase       )
							A		=  emb.weight.data
							return lin_layer






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  torch.load(UpperCamelCase					,  map_location="cpu"       )
							A		=  mam_aaa["args"]
							A		=  mam_aaa["model"]
							A		=  state_dict["decoder.output_projection.weight"]

							remove_ignore_keys_(UpperCamelCase       )
							rename_keys(UpperCamelCase       )

							A		=  state_dict["decoder.embed_tokens.weight"].shape[0]

							A		=  args.share_decoder_input_output_embed

							A		=  [int(UpperCamelCase       ) for i in args.conv_kernel_sizes.split(","       )]
							A		=  SpeechaTextConfig(
							    vocab_size=UpperCamelCase					,  max_source_positions=args.max_source_positions					,  max_target_positions=args.max_target_positions					,  encoder_layers=args.encoder_layers					,  decoder_layers=args.decoder_layers					,  encoder_attention_heads=args.encoder_attention_heads					,  decoder_attention_heads=args.decoder_attention_heads					,  encoder_ffn_dim=args.encoder_ffn_embed_dim					,  decoder_ffn_dim=args.decoder_ffn_embed_dim					,  d_model=args.encoder_embed_dim					,  dropout=args.dropout					,  attention_dropout=args.attention_dropout					,  activation_dropout=args.activation_dropout					,  activation_function="relu"					,  num_conv_layers=len(UpperCamelCase       )					,  conv_channels=args.conv_channels					,  conv_kernel_sizes=UpperCamelCase					,  input_feat_per_channel=args.input_feat_per_channel					,  input_channels=args.input_channels					,  tie_word_embeddings=UpperCamelCase					,  num_beams=5					,  max_length=200					,  use_cache=UpperCamelCase					,  decoder_start_token_id=2					,  early_stopping=UpperCamelCase					,  )

							A		=  SpeechaTextForConditionalGeneration(UpperCamelCase       )
							A,	A		=  model.model.load_state_dict(UpperCamelCase					,  strict=UpperCamelCase       )
							if len(UpperCamelCase       ) > 0 and not set(UpperCamelCase       ) <= {
							    "encoder.embed_positions.weights",
							    "decoder.embed_positions.weights",
							}:
														raise ValueError(
														    "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
														    F" but all the following weights are missing {missing}"       )

							if tie_embeds:
														A		=  make_linear_from_emb(model.model.decoder.embed_tokens       )
							else:
														A		=  lm_head_weights

							model.save_pretrained(UpperCamelCase       )


if __name__ == "__main__":
		_snake_case     :		Optional[int]       						= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		_snake_case     :		str       						= parser.parse_args()
		convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)


