
from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, 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 (
         TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFTransfoXLForSequenceClassification,
         TFTransfoXLLMHeadModel,
         TFTransfoXLModel,
     )




class        _SCREAMING_SNAKE_CASE	:
      def __init__(      self		,						lowerCamelCase		,						):
          snake_case__    =					parent
          snake_case__    =					13
          snake_case__    =					7
          snake_case__    =					30
          snake_case__    =					self.seq_length + self.mem_len
          snake_case__    =					15
          snake_case__    =					True
          snake_case__    =					True
          snake_case__    =					99
          snake_case__    =					[10, 50, 80]
          snake_case__    =					32
          snake_case__    =					32
          snake_case__    =					4
          snake_case__    =					8
          snake_case__    =					1_28
          snake_case__    =					2
          snake_case__    =					2
          snake_case__    =					None
          snake_case__    =					1
          snake_case__    =					0
          snake_case__    =					3
          snake_case__    =					self.vocab_size - 1
          snake_case__    =					0.0_1
      def A_      (      self			):
          snake_case__    =					ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size			)
          snake_case__    =					ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size			)

          snake_case__    =					None
          if self.use_labels:
              snake_case__    =					ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size			)

          snake_case__    =					TransfoXLConfig(
              vocab_size=self.vocab_size		,						mem_len=self.mem_len		,						clamp_len=self.clamp_len		,						cutoffs=self.cutoffs		,						d_model=self.hidden_size		,						d_embed=self.d_embed		,						n_head=self.num_attention_heads		,						d_head=self.d_head		,						d_inner=self.d_inner		,						div_val=self.div_val		,						n_layer=self.num_hidden_layers		,						eos_token_id=self.eos_token_id		,						pad_token_id=self.vocab_size - 1		,						init_range=self.init_range		,						num_labels=self.num_labels		,						)

          return (config, input_ids_a, input_ids_a, lm_labels)
      def A_      (      self			):
          random.seed(self.seed			)
          tf.random.set_seed(self.seed			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					TFTransfoXLModel(lowerCamelCase			)

          snake_case__    ,       snake_case__    =					model(lowerCamelCase			).to_tuple()

          snake_case__    =					{"input_ids": input_ids_a, "mems": mems_a}

          snake_case__    ,       snake_case__    =					model(lowerCamelCase			).to_tuple()

          self.parent.assertEqual(hidden_states_a.shape		,						(self.batch_size, self.seq_length, self.hidden_size)			)
          self.parent.assertEqual(hidden_states_a.shape		,						(self.batch_size, self.seq_length, self.hidden_size)			)
          self.parent.assertListEqual(
              [mem.shape for mem in mems_a]		,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,						)
          self.parent.assertListEqual(
              [mem.shape for mem in mems_a]		,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,						)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					TFTransfoXLLMHeadModel(lowerCamelCase			)

          snake_case__    ,       snake_case__    =					model(lowerCamelCase			).to_tuple()

          snake_case__    =					{"input_ids": input_ids_a, "labels": lm_labels}
          snake_case__    ,       snake_case__    =					model(lowerCamelCase			).to_tuple()

          snake_case__    ,       snake_case__    =					model([input_ids_a, mems_a]			).to_tuple()

          snake_case__    =					{"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

          snake_case__    ,       snake_case__    =					model(lowerCamelCase			).to_tuple()

          self.parent.assertEqual(lm_logits_a.shape		,						(self.batch_size, self.seq_length, self.vocab_size)			)
          self.parent.assertListEqual(
              [mem.shape for mem in mems_a]		,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,						)

          self.parent.assertEqual(lm_logits_a.shape		,						(self.batch_size, self.seq_length, self.vocab_size)			)
          self.parent.assertListEqual(
              [mem.shape for mem in mems_a]		,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,						)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					TFTransfoXLForSequenceClassification(lowerCamelCase			)
          snake_case__    =					model(lowerCamelCase			)
          self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels)			)



      def A_      (      self			):
          snake_case__    =					self.prepare_config_and_inputs()
          ((snake_case__)    ,       (snake_case__)    ,       (snake_case__)    ,       (snake_case__))    =					config_and_inputs
          snake_case__    =					{"input_ids": input_ids_a}
          return config, inputs_dict





@require_tf
class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       __UpperCamelCase							,       unittest.TestCase			):
      _A    :		Union[str, Any]     						=  (
          (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
      )
      _A    :		Union[str, Any]     						=  () if is_tf_available() else ()
      _A    :		Optional[int]     						=  (
          {
              'feature-extraction': TFTransfoXLModel,
              'text-classification': TFTransfoXLForSequenceClassification,
              'text-generation': TFTransfoXLLMHeadModel,
              'zero-shot': TFTransfoXLForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
      _A    :		List[str]     						=  False
      _A    :		Any     						=  False
      _A    :		List[str]     						=  False
      _A    :		str     						=  False
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          if pipeline_test_casse_name == "TextGenerationPipelineTests":
              # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
              # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
              # tokenizer.
              return True

          return False
      def A_      (      self			):
          snake_case__    =					TFTransfoXLModelTester(self			)
          snake_case__    =					ConfigTester(self		,						config_class=lowerCamelCase		,						d_embed=37			)
      def A_      (      self			):
          self.config_tester.run_common_tests()
      def A_      (      self			):
          self.model_tester.set_seed()
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_model(*lowerCamelCase			)
      def A_      (      self			):
          self.model_tester.set_seed()
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCamelCase			)
      def A_      (      self			):
          snake_case__    ,       snake_case__    =					self.model_tester.prepare_config_and_inputs_for_common()
          snake_case__    =					[TFTransfoXLForSequenceClassification]

          for model_class in self.all_model_classes:
              snake_case__    =					model_class(lowerCamelCase			)
              assert isinstance(model.get_input_embeddings()		,						tf.keras.layers.Layer			)
              if model_class in list_other_models_with_output_ebd:
                  snake_case__    =					model.get_output_embeddings()
                  assert isinstance(lowerCamelCase		,						tf.keras.layers.Layer			)
                  snake_case__    =					model.get_bias()
                  assert name is None
              else:
                  snake_case__    =					model.get_output_embeddings()
                  assert x is None
                  snake_case__    =					model.get_bias()
                  assert name is None
      def A_      (      self			):
          # TODO JP: Make TransfoXL XLA compliant
          pass
      @slow
      def A_      (      self			):
          for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case__    =					TFTransfoXLModel.from_pretrained(lowerCamelCase			)
              self.assertIsNotNone(lowerCamelCase			)



      @unittest.skip(reason="This model doesn't play well with fit() due to not returning a single loss."			)
      def A_      (      self			):
          pass





@require_tf
class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      @unittest.skip("Skip test until #12651 is resolved."			)
      @slow
      def A_      (      self			):
          snake_case__    =					TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"			)
          # fmt: off
          snake_case__    =					tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]]		,						dtype=tf.intaa			)  # noqa: E231
          # fmt: on
          #  In 1991 , the remains of Russian Tsar Nicholas II and his family
          #  ( except for Alexei and Maria ) are discovered .
          #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
          #  remainder of the story . 1883 Western Siberia ,
          #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
          #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
          #  father initially slaps him for making such an accusation , Rasputin watches as the
          #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
          #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
          #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

          # fmt: off
          snake_case__    =					[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # noqa: E231
          # fmt: on
          #  In 1991, the remains of Russian Tsar Nicholas II and his family (
          #  except for Alexei and Maria ) are discovered. The voice of young son,
          #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
          #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
          #  and a group of men to perform magic. Rasputin has a vision and
          #  denounces one of the men as a horse thief. Although his father initially
          #  slaps him for making such an accusation, Rasputin watches as the man
          #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
          #  of the Virgin Mary, prompting him to become a priest.
          #  Rasputin quickly becomes famous, with people, even a bishop, begging for
          #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
          # Nicholas II and his family were discovered. The voice of <unk> young son,
          # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

          snake_case__    =					model.generate(lowerCamelCase		,						max_length=2_00		,						do_sample=lowerCamelCase			)
          self.assertListEqual(output_ids[0].numpy().tolist()		,						lowerCamelCase			)





import os
from datetime import datetime as dt

from github import Github


__magic_name__  							=	[
    '''good first issue''',
    '''feature request''',
    '''wip''',
]





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					Github(os.environ["GITHUB_TOKEN"]				)
    snake_case__    =					g.get_repo("huggingface/accelerate"				)
    snake_case__    =					repo.get_issues(state="open"				)

    for issue in open_issues:
        snake_case__    =					sorted([comment for comment in issue.get_comments()]     ,		key=lambda __lowerCAmelCase				: i.created_at     ,		reverse=__lowerCAmelCase				)
        snake_case__    =					comments[0] if len(__lowerCAmelCase				) > 0 else None
        snake_case__    =					dt.utcnow()
        snake_case__    =					(current_time - issue.updated_at).days
        snake_case__    =					(current_time - issue.created_at).days
        if (
            last_comment is not None
            and last_comment.user.login == "github-actions[bot]"
            and days_since_updated > 7
            and days_since_creation >= 30
            and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()				)
        ):
            # Close issue since it has been 7 days of inactivity since bot mention.
            issue.edit(state="closed"				)
        elif (
            days_since_updated > 23
            and days_since_creation >= 30
            and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()				)
        ):
            # Add stale comment
            issue.create_comment(
                "This issue has been automatically marked as stale because it has not had "
                "recent activity. If you think this still needs to be addressed "
                "please comment on this thread.\n\nPlease note that issues that do not follow the "
                "[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) "
                "are likely to be ignored."				)


if __name__ == "__main__":
     main()



