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

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links





def    _lowerCAmelCase    (     lowerCamelCase__							:							int,						lowerCamelCase__							:							Tuple=7				)  ->    str:
		_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = None
		if token is not None:
				_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = {"Accept": "application/vnd.github+json", "Authorization": f'''Bearer {token}'''}

		# The id of a workflow (not of a workflow run)
		_SCREAMING_SNAKE_CASE   :				Any							    = "636036"

		_SCREAMING_SNAKE_CASE   :				Optional[int]							    = 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}'''

		_SCREAMING_SNAKE_CASE   :				List[Any]							    = requests.get(lowerCamelCase__,						headers=lowerCamelCase__				).json()

		return result["workflow_runs"]





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[int]				)  ->    Any:
		_SCREAMING_SNAKE_CASE   :				Any							    = get_daily_ci_runs(lowerCamelCase__				)
		_SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = None
		for workflow_run in workflow_runs:
				if workflow_run["status"] == "completed":
						_SCREAMING_SNAKE_CASE   :				Optional[int]							    = workflow_run["id"]
						break

		return workflow_run_id





def    _lowerCAmelCase    (     lowerCamelCase__							:							Tuple,						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							str				)  ->    Union[str, Any]:
		_SCREAMING_SNAKE_CASE   :				Dict							    = get_last_daily_ci_runs(lowerCamelCase__				)
		if workflow_run_id is not None:
				_SCREAMING_SNAKE_CASE   :				List[str]							    = get_artifacts_links(worflow_run_id=lowerCamelCase__,						token=lowerCamelCase__				)
				for artifact_name in artifact_names:
						if artifact_name in artifacts_links:
								_SCREAMING_SNAKE_CASE   :				str							    = artifacts_links[artifact_name]
								download_artifact(
								    artifact_name=lowerCamelCase__,						artifact_url=lowerCamelCase__,						output_dir=lowerCamelCase__,						token=lowerCamelCase__				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Dict,						lowerCamelCase__							:							Any				)  ->    Optional[int]:
		get_last_daily_ci_artifacts(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)

		_SCREAMING_SNAKE_CASE   :				List[str]							    = {}
		for artifact_name in artifact_names:
				_SCREAMING_SNAKE_CASE   :				Any							    = os.path.join(lowerCamelCase__,						f'''{artifact_name}.zip'''				)
				if os.path.isfile(lowerCamelCase__				):
						_SCREAMING_SNAKE_CASE   :				List[str]							    = {}
						with zipfile.ZipFile(lowerCamelCase__				) as z:
								for filename in z.namelist():
										if not os.path.isdir(lowerCamelCase__				):
												# read the file
												with z.open(lowerCamelCase__				) as f:
														_SCREAMING_SNAKE_CASE   :				Optional[int]							    = f.read().decode("UTF-8"				)

		return results





"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase      (							unittest.TestCase  ):
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = TFCamembertModel.from_pretrained("jplu/tf-camembert-base"				)

       _SCREAMING_SNAKE_CASE   :				str							    = tf.convert_to_tensor(
           [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]  ,     dtype=tf.intaa  ,     )  # J'aime le camembert !"

       _SCREAMING_SNAKE_CASE   :				str							    = model(snake_case__				)["last_hidden_state"]
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tf.TensorShape((1, 10, 768)				)
       self.assertEqual(output.shape  ,     snake_case__				)
       # compare the actual values for a slice.
       _SCREAMING_SNAKE_CASE   :				int							    = tf.convert_to_tensor(
           [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]  ,     dtype=tf.floataa  ,     )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,     expected_slice.numpy()  ,     atol=1E-4				)				)



