'''simple docstring'''




import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


_lowercase						: List[Any]						=						TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow("", "|", "|"),
    datarow=DataRow("", "|", "|"),
    padding=1,
    with_header_hide=None,
)


_lowercase						: str						=						[]
_lowercase						: Dict						=						[]

_lowercase						: Tuple						=						{"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}

_lowercase						: str						=						[
    {
        "type": "header",
        "text": {
            "type": "plain_text",
            "text": f"""🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results""",
            "emoji": True,
        },
    }
]

_lowercase						: int						=						0
for log in Path().glob("*.log"):
						_lowercase						: Optional[int]						=						0
						with open(log, "r") as f:
												for line in f:
																		_lowercase						: Optional[Any]						=						json.loads(line)
																		if line.get("nodeid", "") != "":
																								_lowercase						: Optional[Any]						=						line["nodeid"]
																								if line.get("duration", None) is not None:
																														_lowercase						: Tuple						=						f"""{line["duration"]:.4f}"""
																														if line.get("outcome", "") == "failed":
																																				section_num_failed += 1
																																				failed.append([test, duration, log.name.split("_")[0]])
																																				total_num_failed += 1
						group_info.append([str(log), section_num_failed, failed])
						_lowercase						: Dict						=						[]
						log.unlink()

_lowercase						: List[Any]						=						""
_lowercase						: List[Any]						=						[]
if total_num_failed > 0:
						for name, num_failed, failed_tests in group_info:
												if num_failed > 0:
																		if num_failed == 1:
																								message += f"*{name[1:]}: {num_failed} failed test*\n"
																		else:
																								message += f"*{name[1:]}: {num_failed} failed tests*\n"
																		_lowercase						: Tuple						=						[]
																		_lowercase						: Dict						=						{}
																		for test in failed_tests:
																								_lowercase						: int						=						test[0].split("::")
																								_lowercase						: Optional[int]						=						data[0].split("/")[-1]
																								if data[0] not in filesafailed:
																														_lowercase						: int						=						[data[1:]]
																								else:
																														filesafailed[data[0]] += [data[1:]]
																								failed_table.append(data)

																		_lowercase						: Union[str, Any]						=						[test[0] for test in failed_table]
																		_lowercase						: Tuple						=						list(set(files))
																		# Count number of instances in failed_tests
																		_lowercase						: int						=						[]
																		for file in individual_files:
																								table.append([file, len(filesafailed[file])])

																		_lowercase						: Any						=						tabulate(
																		    table,
																		    headers=["Test Location", "Num Failed"],
																		    tablefmt=hf_table_format,
																		    stralign="right",
																		)
																		message += f"\n```\n{failed_table}\n```"
																		all_filesafailed.append(filesafailed)
						if len(message) > 3000:
												_lowercase						: Optional[int]						=						"Too many failed tests, please see the full report in the Action results."
												_lowercase						: List[str]						=						len(err) + 10
												_lowercase						: Optional[int]						=						message[: 3000 - offset] + f"""\n...\n```\n{err}"""
						print(f"""### {message}""")
else:
						_lowercase						: Union[str, Any]						=						"No failed tests! 🤗"
						print(f"""## {message}""")
						payload.append(no_error_payload)

if os.environ.get("TEST_TYPE", "") != "":
						from slack_sdk import WebClient

						_lowercase						: Optional[int]						=						WebClient(token=os.environ["SLACK_API_TOKEN"])
						if message != "No failed tests! 🤗":
												_lowercase						: Union[str, Any]						=						{
												    "type": "section",
												    "text": {
												        "type": "mrkdwn",
												        "text": message,
												    },
												}
												payload.append(md_report)
												_lowercase						: Tuple						=						{
												    "type": "section",
												    "text": {
												        "type": "mrkdwn",
												        "text": "*For more details:*",
												    },
												    "accessory": {
												        "type": "button",
												        "text": {
												            "type": "plain_text",
												            "text": "Check Action results",
												            "emoji": True,
												        },
												        "url": f"""https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
												    },
												}
												payload.append(action_button)
												_lowercase						: Optional[Any]						=						{
												    "type": "context",
												    "elements": [
												        {
												            "type": "plain_text",
												            "text": f"""Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}""",
												        }
												    ],
												}
												payload.append(date_report)
						_lowercase						: List[Any]						=						client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
						_lowercase						: int						=						response.data["ts"]
						for failed_file in all_filesafailed:
												for test_location, test_failures in failed_file.items():
																		# Keep only the first instance of the test name
																		_lowercase						: Optional[int]						=						""
																		for i, row in enumerate(test_failures):
																								if row[0] != test_class:
																														_lowercase						: Tuple						=						row[0]
																								else:
																														_lowercase						: Tuple						=						""

																		_lowercase						: Optional[int]						=						{
																		    "type": "section",
																		    "text": {
																		        "type": "mrkdwn",
																		        "text": f"""Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```""",
																		    },
																		}

																		client.chat_postMessage(
																		    channel="#accelerate-ci-daily",
																		    thread_ts=ts,
																		    blocks=[payload],
																		)






'''simple docstring'''




import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import 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 (
						    NystromformerForMaskedLM,
						    NystromformerForMultipleChoice,
						    NystromformerForQuestionAnswering,
						    NystromformerForSequenceClassification,
						    NystromformerForTokenClassification,
						    NystromformerModel,
						)
						from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST





class     __magic_name__						:







			def __init__(	self				:  int				,			lowercase_				:  Optional[int]				,			lowercase_				:  Any=13				,			lowercase_				:  List[str]=7				,			lowercase_				:  List[Any]=True				,			lowercase_				:  str=True				,			lowercase_				:  Dict=True				,			lowercase_				:  List[str]=True				,			lowercase_				:  List[str]=99				,			lowercase_				:  Dict=32				,			lowercase_				:  List[Any]=5				,			lowercase_				:  List[str]=4				,			lowercase_				:  Dict=37				,			lowercase_				:  List[Any]="gelu"				,			lowercase_				:  Dict=0.1				,			lowercase_				:  Any=0.1				,			lowercase_				:  int=512				,			lowercase_				:  Tuple=16				,			lowercase_				:  str=2				,			lowercase_				:  Optional[Any]=0.02				,			lowercase_				:  Any=3				,			lowercase_				:  Any=4				,			lowercase_				:  Dict=None				,			):
										lowercase_					:			Tuple										=       parent
										lowercase_					:			Tuple										=       batch_size
										lowercase_					:			Optional[int]										=       seq_length
										lowercase_					:			Union[str, Any]										=       is_training
										lowercase_					:			int										=       use_input_mask
										lowercase_					:			Union[str, Any]										=       use_token_type_ids
										lowercase_					:			Tuple										=       use_labels
										lowercase_					:			Tuple										=       vocab_size
										lowercase_					:			int										=       hidden_size
										lowercase_					:			int										=       num_hidden_layers
										lowercase_					:			Optional[int]										=       num_attention_heads
										lowercase_					:			Union[str, Any]										=       intermediate_size
										lowercase_					:			List[Any]										=       hidden_act
										lowercase_					:			int										=       hidden_dropout_prob
										lowercase_					:			Union[str, Any]										=       attention_probs_dropout_prob
										lowercase_					:			List[Any]										=       max_position_embeddings
										lowercase_					:			Union[str, Any]										=       type_vocab_size
										lowercase_					:			List[Any]										=       type_sequence_label_size
										lowercase_					:			Optional[int]										=       initializer_range
										lowercase_					:			str										=       num_labels
										lowercase_					:			int										=       num_choices
										lowercase_					:			List[Any]										=       scope







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			int										=       ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size    )

										lowercase_					:			str										=       None
										if self.use_input_mask:
																	lowercase_					:			List[str]										=       random_attention_mask([self.batch_size, self.seq_length]    )

										lowercase_					:			Optional[int]										=       None
										if self.use_token_type_ids:
																	lowercase_					:			Tuple										=       ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size    )

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

										lowercase_					:			Tuple										=       self.get_config()

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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										return NystromformerConfig(
										    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				,			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				,			is_decoder=lowercase_				,			initializer_range=self.initializer_range				,			)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]				,			lowercase_				:  Any				,			lowercase_				:  List[str]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  Tuple				,			lowercase_				:  Tuple				,			lowercase_				:  Optional[int]    ):
										lowercase_					:			Optional[Any]										=       NystromformerModel(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Optional[int]										=       model(lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_    )
										lowercase_					:			Optional[Any]										=       model(lowercase_				,			token_type_ids=lowercase_    )
										lowercase_					:			Union[str, Any]										=       model(lowercase_    )
										self.parent.assertEqual(result.last_hidden_state.shape				,			(self.batch_size, self.seq_length, self.hidden_size)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Tuple				,			lowercase_				:  List[Any]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  List[str]				,			lowercase_				:  List[Any]				,			lowercase_				:  Optional[int]				,			lowercase_				:  Any    ):
										lowercase_					:			List[Any]										=       NystromformerForMaskedLM(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Optional[int]										=       model(lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_    )
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.vocab_size)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  int				,			lowercase_				:  List[Any]				,			lowercase_				:  Any				,			lowercase_				:  str				,			lowercase_				:  Dict				,			lowercase_				:  Any				,			lowercase_				:  Tuple    ):
										lowercase_					:			Any										=       NystromformerForQuestionAnswering(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Union[str, Any]										=       model(
										    lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			start_positions=lowercase_				,			end_positions=lowercase_				,			)
										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 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  List[str]				,			lowercase_				:  List[Any]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  str				,			lowercase_				:  Optional[Any]				,			lowercase_				:  str				,			lowercase_				:  int    ):
										lowercase_					:			Any										=       self.num_labels
										lowercase_					:			Union[str, Any]										=       NystromformerForSequenceClassification(lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Any										=       model(lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_    )
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_labels)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  List[str]				,			lowercase_				:  int				,			lowercase_				:  List[str]				,			lowercase_				:  Dict				,			lowercase_				:  Tuple				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  List[str]    ):
										lowercase_					:			int										=       self.num_labels
										lowercase_					:			int										=       NystromformerForTokenClassification(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Tuple										=       model(lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_    )
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.num_labels)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]				,			lowercase_				:  Tuple				,			lowercase_				:  Dict				,			lowercase_				:  int				,			lowercase_				:  List[str]				,			lowercase_				:  Optional[Any]				,			lowercase_				:  str				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			str										=       self.num_choices
										lowercase_					:			Union[str, Any]										=       NystromformerForMultipleChoice(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			str										=       input_ids.unsqueeze(1    ).expand(-1				,			self.num_choices				,			-1    ).contiguous()
										lowercase_					:			Tuple										=       token_type_ids.unsqueeze(1    ).expand(-1				,			self.num_choices				,			-1    ).contiguous()
										lowercase_					:			Dict										=       input_mask.unsqueeze(1    ).expand(-1				,			self.num_choices				,			-1    ).contiguous()
										lowercase_					:			Union[str, Any]										=       model(
										    lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_				,			)
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_choices)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										lowercase_					:			Union[str, Any]										=       self.prepare_config_and_inputs()
										(
										    (
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						
										)					:			Tuple										=       config_and_inputs
										lowercase_					:			Optional[Any]										=       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
										return config, inputs_dict





@require_torch
class     __magic_name__						(    _UpperCAmelCase,     _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       (
			    (
			        NystromformerModel,
			        NystromformerForMaskedLM,
			        NystromformerForMultipleChoice,
			        NystromformerForQuestionAnswering,
			        NystromformerForSequenceClassification,
			        NystromformerForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			UpperCamelCase__				=       (
			    {
			        '''feature-extraction''': NystromformerModel,
			        '''fill-mask''': NystromformerForMaskedLM,
			        '''question-answering''': NystromformerForQuestionAnswering,
			        '''text-classification''': NystromformerForSequenceClassification,
			        '''token-classification''': NystromformerForTokenClassification,
			        '''zero-shot''': NystromformerForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			UpperCamelCase__				=       False
			UpperCamelCase__				=       False







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Any										=       NystromformerModelTester(self    )
										lowercase_					:			Optional[Any]										=       ConfigTester(self				,			config_class=lowercase_				,			hidden_size=37    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										self.config_tester.run_common_tests()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										lowercase_					:			Dict										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			Union[str, Any]										=       self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																	lowercase_					:			int										=       type
																	self.model_tester.create_and_check_model(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			Tuple										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			int										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			Union[str, Any]										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			Any										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			Optional[int]										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowercase_    )







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase_					:			List[Any]										=       NystromformerModel.from_pretrained(lowercase_    )
																	self.assertIsNotNone(lowercase_    )





@require_torch
class     __magic_name__						(    unittest.TestCase):







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										lowercase_					:			List[str]										=       NystromformerModel.from_pretrained("""uw-madison/nystromformer-512"""    )
										lowercase_					:			List[str]										=       torch.tensor([[0, 1, 2, 3, 4, 5]]    )

										with torch.no_grad():
																	lowercase_					:			Tuple										=       model(lowercase_    )[0]

										lowercase_					:			Tuple										=       torch.Size((1, 6, 768)    )
										self.assertEqual(output.shape				,			lowercase_    )

										lowercase_					:			Dict										=       torch.tensor(
										    [[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]]    )

										self.assertTrue(torch.allclose(output[:, :3, :3]				,			lowercase_				,			atol=1E-4    )    )







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			Optional[int]										=       """the [MASK] of Belgium is Brussels"""

										lowercase_					:			Optional[Any]										=       AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512"""    )
										lowercase_					:			List[Any]										=       NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512"""    )

										lowercase_					:			str										=       tokenizer(lowercase_				,			return_tensors="""pt"""    )

										with torch.no_grad():
																	lowercase_					:			Tuple										=       model(encoding.input_ids    ).logits

										lowercase_					:			Optional[int]										=       token_logits[:, 2, :].argmax(-1    )[0]

										self.assertEqual(tokenizer.decode(lowercase_    )				,			"""capital"""    )





