



"""simple docstring"""






import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
	from transformers.models.esm.modeling_esm import (
	    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
	    EsmEmbeddings,
	    create_position_ids_from_input_ids,
	)



class     A__     :




	'''simple docstring'''





	def __init__(  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	List[Any]=13 ,    _SCREAMING_SNAKE_CASE:	Any=7 ,    _SCREAMING_SNAKE_CASE:	Dict=False ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=True ,    _SCREAMING_SNAKE_CASE:	Any=False ,    _SCREAMING_SNAKE_CASE:	List[str]=True ,    _SCREAMING_SNAKE_CASE:	Any=33 ,    _SCREAMING_SNAKE_CASE:	Any=32 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=5 ,    _SCREAMING_SNAKE_CASE:	str=4 ,    _SCREAMING_SNAKE_CASE:	Any=37 ,    _SCREAMING_SNAKE_CASE:	str="gelu" ,    _SCREAMING_SNAKE_CASE:	Any=0.1 ,    _SCREAMING_SNAKE_CASE:	Any=0.1 ,    _SCREAMING_SNAKE_CASE:	int=512 ,    _SCREAMING_SNAKE_CASE:	int=16 ,    _SCREAMING_SNAKE_CASE:	Tuple=2 ,    _SCREAMING_SNAKE_CASE:	Any=0.02 ,    _SCREAMING_SNAKE_CASE:	str=3 ,    _SCREAMING_SNAKE_CASE:	Dict=4 ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=None ,    )       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= parent
				__lowerCAmelCase   :						Optional[int]				= batch_size
				__lowerCAmelCase   :						Optional[Any]				= seq_length
				__lowerCAmelCase   :						int				= is_training
				__lowerCAmelCase   :						List[Any]				= use_input_mask
				__lowerCAmelCase   :						Tuple				= use_token_type_ids
				__lowerCAmelCase   :						Optional[Any]				= use_labels
				__lowerCAmelCase   :						int				= vocab_size
				__lowerCAmelCase   :						int				= hidden_size
				__lowerCAmelCase   :						Tuple				= num_hidden_layers
				__lowerCAmelCase   :						Dict				= num_attention_heads
				__lowerCAmelCase   :						Optional[Any]				= intermediate_size
				__lowerCAmelCase   :						Optional[Any]				= hidden_act
				__lowerCAmelCase   :						Optional[Any]				= hidden_dropout_prob
				__lowerCAmelCase   :						Dict				= attention_probs_dropout_prob
				__lowerCAmelCase   :						Union[str, Any]				= max_position_embeddings
				__lowerCAmelCase   :						Optional[Any]				= type_vocab_size
				__lowerCAmelCase   :						Tuple				= type_sequence_label_size
				__lowerCAmelCase   :						Optional[Any]				= initializer_range
				__lowerCAmelCase   :						Union[str, Any]				= num_labels
				__lowerCAmelCase   :						Any				= num_choices
				__lowerCAmelCase   :						Optional[int]				= scope

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= ids_tensor([self.batch_size, self.seq_length] ,    self.vocab_size)

				__lowerCAmelCase   :						int				= None
				if self.use_input_mask:
							__lowerCAmelCase   :						Optional[Any]				= random_attention_mask([self.batch_size, self.seq_length])

				__lowerCAmelCase   :						List[Any]				= None
				__lowerCAmelCase   :						str				= None
				__lowerCAmelCase   :						Optional[Any]				= None
				if self.use_labels:
							__lowerCAmelCase   :						Dict				= ids_tensor([self.batch_size] ,    self.type_sequence_label_size)
							__lowerCAmelCase   :						Dict				= ids_tensor([self.batch_size, self.seq_length] ,    self.num_labels)
							__lowerCAmelCase   :						Optional[int]				= ids_tensor([self.batch_size] ,    self.num_choices)

				__lowerCAmelCase   :						Union[str, Any]				= self.get_config()

				return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> List[Any]:

				"""simple docstring"""

				return EsmConfig(
				    vocab_size=self.vocab_size ,    hidden_size=self.hidden_size ,    pad_token_id=1 ,    num_hidden_layers=self.num_hidden_layers ,    num_attention_heads=self.num_attention_heads ,    intermediate_size=self.intermediate_size ,    hidden_act=self.hidden_act ,    hidden_dropout_prob=self.hidden_dropout_prob ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob ,    max_position_embeddings=self.max_position_embeddings ,    type_vocab_size=self.type_vocab_size ,    initializer_range=self.initializer_range ,    )

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	List[str])       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= EsmModel(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase   :						Tuple				= model(_SCREAMING_SNAKE_CASE ,    attention_mask=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= model(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Union[str, Any]				= model(_SCREAMING_SNAKE_CASE)

				self.parent.assertEqual(result.last_hidden_state.shape ,    (self.batch_size, self.seq_length, self.hidden_size))
				self.parent.assertEqual(result.pooler_output.shape ,    (self.batch_size, self.hidden_size))

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	int)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= EsmForMaskedLM(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase   :						Dict				= model(_SCREAMING_SNAKE_CASE ,    attention_mask=_SCREAMING_SNAKE_CASE ,    labels=_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.seq_length, self.vocab_size))

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	Any)       -> Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= self.num_labels
				__lowerCAmelCase   :						Tuple				= EsmForTokenClassification(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase   :						Tuple				= model(_SCREAMING_SNAKE_CASE ,    attention_mask=_SCREAMING_SNAKE_CASE ,    labels=_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.seq_length, self.num_labels))







	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= self.prepare_config_and_inputs()
				(
				    (
				    __lowerCAmelCase
				)		,			(
				    __lowerCAmelCase
				)		,			(
				    __lowerCAmelCase
				)		,			(
				    __lowerCAmelCase
				)		,			(
				    __lowerCAmelCase
				)		,			(
				    __lowerCAmelCase
				)		,			
				)   :						Union[str, Any]				= config_and_inputs
				__lowerCAmelCase   :						Union[str, Any]				= {"input_ids": input_ids, "attention_mask": input_mask}
				return config, inputs_dict






@require_torch
class     A__     (						__SCREAMING_SNAKE_CASE						,	__SCREAMING_SNAKE_CASE						,	unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			False

	SCREAMING_SNAKE_CASE					   =			(
	    (
	        EsmForMaskedLM,
	        EsmModel,
	        EsmForSequenceClassification,
	        EsmForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE					   =			()
	SCREAMING_SNAKE_CASE					   =			(
	    {
	        'feature-extraction': EsmModel,
	        'fill-mask': EsmForMaskedLM,
	        'text-classification': EsmForSequenceClassification,
	        'token-classification': EsmForTokenClassification,
	        'zero-shot': EsmForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE					   =			True

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= EsmModelTester(self)
				__lowerCAmelCase   :						int				= ConfigTester(self ,    config_class=_SCREAMING_SNAKE_CASE ,    hidden_size=37)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Optional[int]:

				"""simple docstring"""

				self.config_tester.run_common_tests()

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
							__lowerCAmelCase   :						Optional[Any]				= type
							self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE)

	@slow
	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Optional[Any]:

				"""simple docstring"""

				for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__lowerCAmelCase   :						Union[str, Any]				= EsmModel.from_pretrained(_SCREAMING_SNAKE_CASE)
							self.assertIsNotNone(_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= self.model_tester.prepare_config_and_inputs()[0]
				__lowerCAmelCase   :						Optional[int]				= EsmEmbeddings(config=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Tuple				= torch.as_tensor([[12, 31, 13, model.padding_idx]])
				__lowerCAmelCase   :						List[str]				= torch.as_tensor(
				    [
				        [
				            0 + model.padding_idx + 1,
				            1 + model.padding_idx + 1,
				            2 + model.padding_idx + 1,
				            model.padding_idx,
				        ]
				    ])
				__lowerCAmelCase   :						Dict				= create_position_ids_from_input_ids(_SCREAMING_SNAKE_CASE ,    model.padding_idx)
				self.assertEqual(position_ids.shape ,    expected_positions.shape)
				self.assertTrue(torch.all(torch.eq(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)))

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= self.model_tester.prepare_config_and_inputs()[0]
				__lowerCAmelCase   :						Dict				= EsmEmbeddings(config=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						int				= torch.empty(2 ,    4 ,    30)
				__lowerCAmelCase   :						Optional[Any]				= [
				    0 + embeddings.padding_idx + 1,
				    1 + embeddings.padding_idx + 1,
				    2 + embeddings.padding_idx + 1,
				    3 + embeddings.padding_idx + 1,
				]
				__lowerCAmelCase   :						Optional[int]				= torch.as_tensor([expected_single_positions, expected_single_positions])
				__lowerCAmelCase   :						Union[str, Any]				= embeddings.create_position_ids_from_inputs_embeds(_SCREAMING_SNAKE_CASE)
				self.assertEqual(position_ids.shape ,    expected_positions.shape)
				self.assertTrue(torch.all(torch.eq(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)))

	@unittest.skip("Esm does not support embedding resizing")
	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Dict:

				"""simple docstring"""

				pass

	@unittest.skip("Esm does not support embedding resizing")
	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> List[str]:

				"""simple docstring"""

				pass







	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Tuple:

				"""simple docstring"""

				pass






@require_torch
class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Optional[int]:

				"""simple docstring"""

				with torch.no_grad():
							__lowerCAmelCase   :						str				= EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D")
							model.eval()
							__lowerCAmelCase   :						Dict				= torch.tensor([[0, 1, 2, 3, 4, 5]])
							__lowerCAmelCase   :						List[str]				= model(_SCREAMING_SNAKE_CASE)[0]

							__lowerCAmelCase   :						Any				= 33

							__lowerCAmelCase   :						Dict				= torch.Size((1, 6, vocab_size))
							self.assertEqual(output.shape ,    _SCREAMING_SNAKE_CASE)

							__lowerCAmelCase   :						Optional[Any]				= torch.tensor(
							    [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]])
							self.assertTrue(torch.allclose(output[:, :3, :3] ,    _SCREAMING_SNAKE_CASE ,    atol=1e-4))







	@slow
	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> str:

				"""simple docstring"""

				with torch.no_grad():
							__lowerCAmelCase   :						str				= EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D")
							model.eval()

							__lowerCAmelCase   :						List[Any]				= torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
							__lowerCAmelCase   :						List[Any]				= model(_SCREAMING_SNAKE_CASE)[0]
							# compare the actual values for a slice.
							__lowerCAmelCase   :						Optional[int]				= torch.tensor(
							    [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]])
							self.assertTrue(torch.allclose(output[:, :3, :3] ,    _SCREAMING_SNAKE_CASE ,    atol=1e-4))




"""simple docstring"""






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


__snake_case							:     Tuple   =					logging.get_logger(__name__)


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Dict:
			__lowerCAmelCase   :						Optional[Any]				= set()
			__lowerCAmelCase   :						List[Any]				= []

			def parse_line(__snake_case			):
						for line in fp:
									if isinstance(__snake_case      ,__snake_case			):
												__lowerCAmelCase   :						Tuple				= 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(__snake_case			) > 0:
															__lowerCAmelCase   :						List[Any]				= "\n".join(__snake_case			)
															# Only keep the warnings specified in `targets`
															if any(F""": {x}: """ in warning for x in targets			):
																		selected_warnings.add(__snake_case			)
															buffer.clear()
												continue
									else:
												__lowerCAmelCase   :						List[str]				= line.strip()
												buffer.append(__snake_case			)

			if from_gh:
						for filename in os.listdir(__snake_case			):
									__lowerCAmelCase   :						List[str]				= os.path.join(__snake_case      ,__snake_case			)
									if not os.path.isdir(__snake_case			):
												# read the file
												if filename != "warnings.txt":
															continue
												with open(__snake_case			) as fp:
															parse_line(__snake_case			)
			else:
						try:
									with zipfile.ZipFile(__snake_case			) as z:
												for filename in z.namelist():
															if not os.path.isdir(__snake_case			):
																		# read the file
																		if filename != "warnings.txt":
																					continue
																		with z.open(__snake_case			) as fp:
																					parse_line(__snake_case			)
						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     _lowercase       (						__snake_case      ,__snake_case			)		->     Any:
			__lowerCAmelCase   :						Any				= set()

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

			return selected_warnings


if __name__ == "__main__":


	def     _lowercase       (						__snake_case			)		->     Optional[Any]:
				return values.split(","			)

	__snake_case							:     Optional[int]   =					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.',
	)

	__snake_case							:     Tuple   =					parser.parse_args()

	__snake_case							:     Any   =					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
		__snake_case							:     str   =					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
	__snake_case							:     Tuple   =					extract_warnings(args.output_dir, args.targets)
	__snake_case							:     List[str]   =					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)