


import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
	    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
	    SqueezeBertForMaskedLM,
	    SqueezeBertForMultipleChoice,
	    SqueezeBertForQuestionAnswering,
	    SqueezeBertForSequenceClassification,
	    SqueezeBertForTokenClassification,
	    SqueezeBertModel,
	)



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					def __init__(self       :		Any ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Tuple=13 ,   _UpperCAmelCase       :		str=7 ,   _UpperCAmelCase       :		Optional[int]=True ,   _UpperCAmelCase       :		Union[str, Any]=True ,   _UpperCAmelCase       :		Union[str, Any]=False ,   _UpperCAmelCase       :		Optional[int]=True ,   _UpperCAmelCase       :		Any=99 ,   _UpperCAmelCase       :		Optional[Any]=32 ,   _UpperCAmelCase       :		Optional[Any]=5 ,   _UpperCAmelCase       :		Optional[Any]=4 ,   _UpperCAmelCase       :		Optional[Any]=64 ,   _UpperCAmelCase       :		Any="gelu" ,   _UpperCAmelCase       :		Optional[int]=0.1 ,   _UpperCAmelCase       :		Optional[int]=0.1 ,   _UpperCAmelCase       :		Optional[Any]=512 ,   _UpperCAmelCase       :		Any=16 ,   _UpperCAmelCase       :		int=2 ,   _UpperCAmelCase       :		Union[str, Any]=0.02 ,   _UpperCAmelCase       :		Tuple=3 ,   _UpperCAmelCase       :		Optional[int]=4 ,   _UpperCAmelCase       :		int=None ,   _UpperCAmelCase       :		Tuple=2 ,   _UpperCAmelCase       :		Tuple=2 ,   _UpperCAmelCase       :		str=2 ,   _UpperCAmelCase       :		Dict=2 ,   _UpperCAmelCase       :		str=4 ,   _UpperCAmelCase       :		Tuple=1 ,   )		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						parent
							lowercase__	     =						batch_size
							lowercase__	     =						seq_length
							lowercase__	     =						is_training
							lowercase__	     =						use_input_mask
							lowercase__	     =						use_token_type_ids
							lowercase__	     =						use_labels
							lowercase__	     =						vocab_size
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						intermediate_size
							lowercase__	     =						hidden_act
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						max_position_embeddings
							lowercase__	     =						type_vocab_size
							lowercase__	     =						type_sequence_label_size
							lowercase__	     =						initializer_range
							lowercase__	     =						num_labels
							lowercase__	     =						num_choices
							lowercase__	     =						scope
							lowercase__	     =						q_groups
							lowercase__	     =						k_groups
							lowercase__	     =						v_groups
							lowercase__	     =						post_attention_groups
							lowercase__	     =						intermediate_groups
							lowercase__	     =						output_groups

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size						)

							lowercase__	     =						None
							if self.use_input_mask:
									lowercase__	     =						random_attention_mask([self.batch_size, self.seq_length]						)

							lowercase__	     =						None
							lowercase__	     =						None
							lowercase__	     =						None
							if self.use_labels:
									lowercase__	     =						ids_tensor([self.batch_size] ,   self.type_sequence_label_size						)
									lowercase__	     =						ids_tensor([self.batch_size, self.seq_length] ,   self.num_labels						)
									lowercase__	     =						ids_tensor([self.batch_size] ,   self.num_choices						)

							lowercase__	     =						self.get_config()

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

					def     lowerCamelCase__	(self       :		List[str]						)		->  Optional[int]:




							"""simple docstring"""


							return SqueezeBertConfig(
							    embedding_size=self.hidden_size ,   vocab_size=self.vocab_size ,   hidden_size=self.hidden_size ,   num_hidden_layers=self.num_hidden_layers ,   num_attention_heads=self.num_attention_heads ,   intermediate_size=self.intermediate_size ,   hidden_act=self.hidden_act ,   attention_probs_dropout_prob=self.hidden_dropout_prob ,   attention_dropout=self.attention_probs_dropout_prob ,   max_position_embeddings=self.max_position_embeddings ,   initializer_range=self.initializer_range ,   q_groups=self.q_groups ,   k_groups=self.k_groups ,   v_groups=self.v_groups ,   post_attention_groups=self.post_attention_groups ,   intermediate_groups=self.intermediate_groups ,   output_groups=self.output_groups ,   )

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




							"""simple docstring"""


							lowercase__	     =						SqueezeBertModel(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						model(_UpperCAmelCase ,   _UpperCAmelCase						)
							lowercase__	     =						model(_UpperCAmelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)						)

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




							"""simple docstring"""


							lowercase__	     =						SqueezeBertForMaskedLM(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						model(_UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   labels=_UpperCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size)						)

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




							"""simple docstring"""


							lowercase__	     =						SqueezeBertForQuestionAnswering(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						model(
							    _UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase						)
							self.parent.assertEqual(result.start_logits.shape ,   (self.batch_size, self.seq_length)						)
							self.parent.assertEqual(result.end_logits.shape ,   (self.batch_size, self.seq_length)						)

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




							"""simple docstring"""


							lowercase__	     =						self.num_labels
							lowercase__	     =						SqueezeBertForSequenceClassification(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						model(_UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   labels=_UpperCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_labels)						)

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




							"""simple docstring"""


							lowercase__	     =						self.num_labels
							lowercase__	     =						SqueezeBertForTokenClassification(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   labels=_UpperCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.num_labels)						)

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




							"""simple docstring"""


							lowercase__	     =						self.num_choices
							lowercase__	     =						SqueezeBertForMultipleChoice(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						input_ids.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						input_mask.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						model(
							    _UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   labels=_UpperCAmelCase ,   )
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_choices)						)


					def     lowerCamelCase__	(self       :		str						)		->  str:




							"""simple docstring"""


							lowercase__	     =						self.prepare_config_and_inputs()
							((lowercase__)     ,      (lowercase__)     ,      (lowercase__)     ,      (lowercase__)     ,      (lowercase__)     ,      (lowercase__))	     =						config_and_inputs
							lowercase__	     =						{"""input_ids""": input_ids, """attention_mask""": input_mask}
							return config, inputs_dict






@require_torch
class 				A       (			UpperCAmelCase__					,    UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					(
					    (
					        SqueezeBertModel,
					        SqueezeBertForMaskedLM,
					        SqueezeBertForMultipleChoice,
					        SqueezeBertForQuestionAnswering,
					        SqueezeBertForSequenceClassification,
					        SqueezeBertForTokenClassification,
					    )
					    if is_torch_available()
					    else None
					)
					A__     =					(
					    {
					        '''feature-extraction''': SqueezeBertModel,
					        '''fill-mask''': SqueezeBertForMaskedLM,
					        '''question-answering''': SqueezeBertForQuestionAnswering,
					        '''text-classification''': SqueezeBertForSequenceClassification,
					        '''token-classification''': SqueezeBertForTokenClassification,
					        '''zero-shot''': SqueezeBertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					A__     =					False
					A__     =					True
					A__     =					False

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




							"""simple docstring"""


							lowercase__	     =						SqueezeBertModelTester(self						)
							lowercase__	     =						ConfigTester(self ,   config_class=_UpperCAmelCase ,   dim=37						)

					def     lowerCamelCase__	(self       :		Any						)		->  Any:




							"""simple docstring"""


							self.config_tester.run_common_tests()

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_model(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_masked_lm(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		str						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_question_answering(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		str						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_token_classification(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Tuple						)		->  str:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_UpperCAmelCase						)


					@slow
					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  List[str]:




							"""simple docstring"""


							for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase__	     =						SqueezeBertModel.from_pretrained(_UpperCAmelCase						)
									self.assertIsNotNone(_UpperCAmelCase						)






@require_sentencepiece
@require_tokenizers
@require_torch
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					@slow
					def     lowerCamelCase__	(self       :		int						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli"""						)

							lowercase__	     =						torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]]						)
							lowercase__	     =						model(_UpperCAmelCase						)[0]
							lowercase__	     =						torch.Size((1, 3)						)
							self.assertEqual(output.shape ,   _UpperCAmelCase						)
							lowercase__	     =						torch.tensor([[0.6_401, -0.0_349, -0.6_041]]						)
							self.assertTrue(torch.allclose(_UpperCAmelCase ,   _UpperCAmelCase ,   atol=1E-4						)						)





import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links






def 					UpperCamelCase   (      __magic_name__	:				Dict       ,       __magic_name__	:				List[str]=7	)  ->    Dict:
		"""simple docstring"""




		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(__magic_name__       ,       headers=__magic_name__	).json()

		return result["workflow_runs"]






def 					UpperCamelCase   (      __magic_name__	:				str	)  ->    Dict:
		"""simple docstring"""




		lowercase__	     =						get_daily_ci_runs(__magic_name__	)
		lowercase__	     =						None
		for workflow_run in workflow_runs:
				if workflow_run["status"] == "completed":
						lowercase__	     =						workflow_run["""id"""]
						break

		return workflow_run_id






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				List[Any]       ,       __magic_name__	:				Dict	)  ->    str:
		"""simple docstring"""




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







def 					UpperCamelCase   (      __magic_name__	:				List[str]       ,       __magic_name__	:				str       ,       __magic_name__	:				Union[str, Any]	)  ->    Tuple:
		"""simple docstring"""




		get_last_daily_ci_artifacts(__magic_name__       ,       __magic_name__       ,       __magic_name__	)

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

		return results

