


'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFLEDForConditionalGeneration, TFLEDModel




@require_tf
class 		a__    :





							'''simple docstring'''






							lowercase__      :       Tuple     						=				LEDConfig
							lowercase__      :       Optional[Any]     						=				{}
							lowercase__      :       str     						=				"gelu"







							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_=13 ,   lowerCamelCase_=7 ,   lowerCamelCase_=True ,   lowerCamelCase_=False ,   lowerCamelCase_=99 ,   lowerCamelCase_=32 ,   lowerCamelCase_=2 ,   lowerCamelCase_=4 ,   lowerCamelCase_=37 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=20 ,   lowerCamelCase_=2 ,   lowerCamelCase_=1 ,   lowerCamelCase_=0 ,   lowerCamelCase_=4 ,   )	->							str:
														lowerCAmelCase__	       =     parent
														lowerCAmelCase__	       =     batch_size
														lowerCAmelCase__	       =     seq_length
														lowerCAmelCase__	       =     is_training
														lowerCAmelCase__	       =     use_labels
														lowerCAmelCase__	       =     vocab_size
														lowerCAmelCase__	       =     hidden_size
														lowerCAmelCase__	       =     num_hidden_layers
														lowerCAmelCase__	       =     num_attention_heads
														lowerCAmelCase__	       =     intermediate_size

														lowerCAmelCase__	       =     hidden_dropout_prob
														lowerCAmelCase__	       =     attention_probs_dropout_prob
														lowerCAmelCase__	       =     max_position_embeddings
														lowerCAmelCase__	       =     eos_token_id
														lowerCAmelCase__	       =     pad_token_id
														lowerCAmelCase__	       =     bos_token_id
														lowerCAmelCase__	       =     attention_window

														# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
														# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
														# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
														# because its local attention only attends to `self.attention_window` and one before and one after
														lowerCAmelCase__	       =     self.attention_window + 2

														# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
														# the `test_attention_outputs` and `test_hidden_states_output` tests
														lowerCAmelCase__	       =     (
														    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
														)







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														lowerCAmelCase__	       =     ids_tensor([self.batch_size, self.seq_length - 1] ,   self.vocab_size       )
														lowerCAmelCase__	       =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       ) ,   1       )
														lowerCAmelCase__	       =     tf.concat([input_ids, eos_tensor] ,   axis=1       )

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

														lowerCAmelCase__	       =     self.config_cls(
														    vocab_size=self.vocab_size ,   d_model=self.hidden_size ,   encoder_layers=self.num_hidden_layers ,   decoder_layers=self.num_hidden_layers ,   encoder_attention_heads=self.num_attention_heads ,   decoder_attention_heads=self.num_attention_heads ,   encoder_ffn_dim=self.intermediate_size ,   decoder_ffn_dim=self.intermediate_size ,   dropout=self.hidden_dropout_prob ,   attention_dropout=self.attention_probs_dropout_prob ,   max_position_embeddings=self.max_position_embeddings ,   eos_token_ids=[2] ,   bos_token_id=self.bos_token_id ,   pad_token_id=self.pad_token_id ,   decoder_start_token_id=self.pad_token_id ,   attention_window=self.attention_window ,   **self.config_updates ,   )
														lowerCAmelCase__	       =     prepare_led_inputs_dict(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )
														lowerCAmelCase__	       =     tf.concat(
														    [tf.zeros_like(lowerCamelCase_       )[:, :-1], tf.ones_like(lowerCamelCase_       )[:, -1:]] ,   axis=-1 ,   )
														lowerCAmelCase__	       =     global_attention_mask
														return config, inputs_dict







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							int:
														lowerCAmelCase__	       =     TFLEDModel(config=lowerCamelCase_       ).get_decoder()
														lowerCAmelCase__	       =     inputs_dict['''input_ids''']

														lowerCAmelCase__	       =     input_ids[:1, :]
														lowerCAmelCase__	       =     inputs_dict['''attention_mask'''][:1, :]
														lowerCAmelCase__	       =     1

														# first forward pass
														lowerCAmelCase__	       =     model(lowerCamelCase_ ,   attention_mask=lowerCamelCase_ ,   use_cache=lowerCamelCase_       )

														lowerCAmelCase__       , lowerCAmelCase__	       =     outputs.to_tuple()

														# create hypothetical next token and extent to next_input_ids
														lowerCAmelCase__	       =     ids_tensor((self.batch_size, 3) ,   config.vocab_size       )
														lowerCAmelCase__	       =     tf.cast(ids_tensor((self.batch_size, 3) ,   2       ) ,   tf.inta       )

														# append to next input_ids and
														lowerCAmelCase__	       =     tf.concat([input_ids, next_tokens] ,   axis=-1       )
														lowerCAmelCase__	       =     tf.concat([attention_mask, next_attn_mask] ,   axis=-1       )

														lowerCAmelCase__	       =     model(lowerCamelCase_ ,   attention_mask=lowerCamelCase_       )[0]
														lowerCAmelCase__	       =     model(lowerCamelCase_ ,   attention_mask=lowerCamelCase_ ,   past_key_values=lowerCamelCase_       )[0]

														self.parent.assertEqual(next_tokens.shape[1] ,   output_from_past.shape[1]       )

														# select random slice
														lowerCAmelCase__	       =     int(ids_tensor((1,) ,   output_from_past.shape[-1]       )       )
														lowerCAmelCase__	       =     output_from_no_past[:, -3:, random_slice_idx]
														lowerCAmelCase__	       =     output_from_past[:, :, random_slice_idx]

														# test that outputs are equal for slice
														tf.debugging.assert_near(lowerCamelCase_ ,   lowerCamelCase_ ,   rtol=1e-3       )


def  _snake_case       (					A			,   A			,   A			,   A=None			,   A=None			,   A=None			,   A=None			,   )						->   List[str]:
							if attention_mask is None:
														lowerCAmelCase__	       =     tf.cast(tf.math.not_equal(A			,   config.pad_token_id	)			,   tf.inta	)
							if decoder_attention_mask is None:
														lowerCAmelCase__	       =     tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape			,   dtype=tf.inta	),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]			,   config.pad_token_id	)			,   tf.inta	),
														    ]			,   axis=-1			,   )
							if head_mask is None:
														lowerCAmelCase__	       =     tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
							if decoder_head_mask is None:
														lowerCAmelCase__	       =     tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
							return {
							    "input_ids": input_ids,
							    "attention_mask": attention_mask,
							    "decoder_input_ids": decoder_input_ids,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							}




@require_tf
class 		a__    (							a__    ,    a__    ,    unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       str     						=				(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
							lowercase__      :       Union[str, Any]     						=				(TFLEDForConditionalGeneration,) if is_tf_available() else ()
							lowercase__      :       str     						=				(
							    {
							        "conversational": TFLEDForConditionalGeneration,
							        "feature-extraction": TFLEDModel,
							        "summarization": TFLEDForConditionalGeneration,
							        "text2text-generation": TFLEDForConditionalGeneration,
							        "translation": TFLEDForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowercase__      :       Optional[int]     						=				True
							lowercase__      :       int     						=				False
							lowercase__      :       str     						=				False
							lowercase__      :       Any     						=				False







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														lowerCAmelCase__	       =     TFLEDModelTester(self       )
														lowerCAmelCase__	       =     ConfigTester(self ,   config_class=lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														self.config_tester.run_common_tests()







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														lowerCAmelCase__       , lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs_for_common()
														lowerCAmelCase__	       =     tf.zeros_like(inputs_dict['''attention_mask''']       )
														lowerCAmelCase__	       =     2
														lowerCAmelCase__	       =     tf.where(
														    tf.range(self.model_tester.seq_length       )[None, :] < num_global_attn_indices ,   1 ,   inputs_dict['''global_attention_mask'''] ,   )

														lowerCAmelCase__	       =     True
														lowerCAmelCase__	       =     self.model_tester.seq_length
														lowerCAmelCase__	       =     self.model_tester.encoder_seq_length

														def check_decoder_attentions_output(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     outputs.decoder_attentions
																					self.assertEqual(len(lowerCamelCase_       ) ,   self.model_tester.num_hidden_layers       )
																					self.assertListEqual(
																					    list(decoder_attentions[0].shape[-3:]       ) ,   [self.model_tester.num_attention_heads, seq_length, seq_length] ,   )

														def check_encoder_attentions_output(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     [t.numpy() for t in outputs.encoder_attentions]
																					lowerCAmelCase__	       =     [t.numpy() for t in outputs.encoder_global_attentions]
																					self.assertEqual(len(lowerCamelCase_       ) ,   self.model_tester.num_hidden_layers       )
																					self.assertEqual(len(lowerCamelCase_       ) ,   self.model_tester.num_hidden_layers       )
																					self.assertListEqual(
																					    list(attentions[0].shape[-3:]       ) ,   [self.model_tester.num_attention_heads, seq_length, seq_length] ,   )
																					self.assertListEqual(
																					    list(global_attentions[0].shape[-3:]       ) ,   [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] ,   )

														for model_class in self.all_model_classes:
																					lowerCAmelCase__	       =     True
																					lowerCAmelCase__	       =     False
																					lowerCAmelCase__	       =     False
																					lowerCAmelCase__	       =     model_class(lowerCamelCase_       )
																					lowerCAmelCase__	       =     model(self._prepare_for_class(lowerCamelCase_ ,   lowerCamelCase_       )       )
																					lowerCAmelCase__	       =     len(lowerCamelCase_       )
																					self.assertEqual(config.output_hidden_states ,   lowerCamelCase_       )
																					check_encoder_attentions_output(lowerCamelCase_       )

																					if self.is_encoder_decoder:
																												lowerCAmelCase__	       =     model_class(lowerCamelCase_       )
																												lowerCAmelCase__	       =     model(self._prepare_for_class(lowerCamelCase_ ,   lowerCamelCase_       )       )
																												self.assertEqual(config.output_hidden_states ,   lowerCamelCase_       )
																												check_decoder_attentions_output(lowerCamelCase_       )

																					# Check that output attentions can also be changed via the config
																					del inputs_dict["output_attentions"]
																					lowerCAmelCase__	       =     True
																					lowerCAmelCase__	       =     model_class(lowerCamelCase_       )
																					lowerCAmelCase__	       =     model(self._prepare_for_class(lowerCamelCase_ ,   lowerCamelCase_       )       )
																					self.assertEqual(config.output_hidden_states ,   lowerCamelCase_       )
																					check_encoder_attentions_output(lowerCamelCase_       )

																					# Check attention is always last and order is fine
																					lowerCAmelCase__	       =     True
																					lowerCAmelCase__	       =     True
																					lowerCAmelCase__	       =     model_class(lowerCamelCase_       )
																					lowerCAmelCase__	       =     model(self._prepare_for_class(lowerCamelCase_ ,   lowerCamelCase_       )       )

																					self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) ,   len(lowerCamelCase_       )       )
																					self.assertEqual(model.config.output_hidden_states ,   lowerCamelCase_       )
																					check_encoder_attentions_output(lowerCamelCase_       )







							@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''       )
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														pass







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														# TODO: Head-masking not yet implement
														pass


def  _snake_case       (					A	)						->   Optional[int]:
							return tf.constant(A			,   dtype=tf.intaa	)


__UpperCAmelCase       =							1e-4






@slow
@require_tf
class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														lowerCAmelCase__	       =     TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''       ).led

														# change to intended input here
														lowerCAmelCase__	       =     _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]       )
														lowerCAmelCase__	       =     _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]       )
														lowerCAmelCase__	       =     prepare_led_inputs_dict(model.config ,   lowerCamelCase_ ,   lowerCamelCase_       )
														lowerCAmelCase__	       =     model(**lowerCamelCase_       )[0]
														lowerCAmelCase__	       =     (1, 10_24, 7_68)
														self.assertEqual(output.shape ,   lowerCamelCase_       )
														# change to expected output here
														lowerCAmelCase__	       =     tf.convert_to_tensor(
														    [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]] ,   )
														tf.debugging.assert_near(output[:, :3, :3] ,   lowerCamelCase_ ,   atol=1e-3       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														lowerCAmelCase__	       =     TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''       )

														# change to intended input here
														lowerCAmelCase__	       =     _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]       )
														lowerCAmelCase__	       =     _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]       )
														lowerCAmelCase__	       =     prepare_led_inputs_dict(model.config ,   lowerCamelCase_ ,   lowerCamelCase_       )
														lowerCAmelCase__	       =     model(**lowerCamelCase_       )[0]
														lowerCAmelCase__	       =     (1, 10_24, model.config.vocab_size)
														self.assertEqual(output.shape ,   lowerCamelCase_       )
														# change to expected output here
														lowerCAmelCase__	       =     tf.convert_to_tensor(
														    [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]] ,   )
														tf.debugging.assert_near(output[:, :3, :3] ,   lowerCamelCase_ ,   atol=1e-3 ,   rtol=1e-3       )



'''simple docstring'''






import argparse
import json
import subprocess


def  _snake_case       (					A			,   A	)						->   Tuple:
							lowerCAmelCase__	       =     []

							lowerCAmelCase__	       =     (
							    F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""""
							    ''' https://api.github.com/repos/huggingface/transformers/actions/runners'''
							)
							lowerCAmelCase__	       =     subprocess.run(A			,   shell=A			,   stdout=subprocess.PIPE	)
							lowerCAmelCase__	       =     output.stdout.decode('''utf-8'''	)
							lowerCAmelCase__	       =     json.loads(A	)

							lowerCAmelCase__	       =     status['''runners''']
							for runner in runners:
														if runner["name"] in target_runners:
																					if runner["status"] == "offline":
																												offline_runners.append(A	)

    # save the result so we can report them on Slack
							with open('''offline_runners.txt'''			,   '''w'''	) as fp:
														fp.write(json.dumps(A	)	)

							if len(A	) > 0:
														lowerCAmelCase__	       =     '''\n'''.join([x['''name'''] for x in offline_runners]	)
														raise ValueError(F"""The following runners are offline:\n{failed}"""	)


if __name__ == "__main__":


		def  _snake_case       (					A	)						->   Optional[Any]:
									return values.split(''','''	)

		__UpperCAmelCase       =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--target_runners''',
		    default=None,
		    type=list_str,
		    required=True,
		    help='''Comma-separated list of runners to check status.''',
		)

		parser.add_argument(
		    '''--token''', default=None, type=str, required=True, help='''A token that has actions:read permission.'''
		)
		__UpperCAmelCase       =							parser.parse_args()






		get_runner_status(args.target_runners, args.token)