
from datetime import datetime as dt
import os

from github import Github


UpperCAmelCase__		:     Union[str, Any]     			=      [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'feature request',
    'new model',
    'wip',
]






def   lowerCamelCase__							(					)      ->   int:
   _A:  Optional[Any]					=       Github(os.environ['''GITHUB_TOKEN''']      )
   _A:  List[str]					=       g.get_repo('''huggingface/transformers'''      )
   _A:  Optional[int]					=       repo.get_issues(state='''open'''      )

   for issue in open_issues:
      _A:  Union[str, Any]					=       sorted([comment for comment in issue.get_comments()]		,		key=lambda a      : i.created_at		,		reverse=a      )
      _A:  int					=       comments[0] if len(a      ) > 0 else None
      if (
          last_comment is not None
          and last_comment.user.login == "github-actions[bot]"
          and (dt.utcnow() - issue.updated_at).days > 7
          and (dt.utcnow() - issue.created_at).days >= 30
          and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
      ):
         # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
         issue.edit(state='''closed'''      )
      elif (
          (dt.utcnow() - issue.updated_at).days > 23
          and (dt.utcnow() - issue.created_at).days >= 30
          and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
      ):
         # print(f"Would add stale comment to {issue.number}")
         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/transformers/blob/main/CONTRIBUTING.md) '''
             '''are likely to be ignored.'''      )


if __name__ == "__main__":
   main()





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
   import jax
   import jax.numpy as jnp






@slow
@require_flax
class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Dict			,			lowerCAmelCase_     :							Optional[int]     ):

      """simple docstring"""
      return F"""gaussian_noise_s={seed}_shape={'_'.join([str(lowerCAmelCase_     ) for s in shape]     )}.npy"""


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Any=0			,			lowerCAmelCase_     :							Tuple=(4, 4, 6_4, 6_4)			,			lowerCAmelCase_     :							List[str]=False     ):

      """simple docstring"""
      _A:  List[str]					=       jnp.bfloataa if fpaa else jnp.floataa
      _A:  Optional[int]					=       jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_			,			lowerCAmelCase_     )     )			,			dtype=lowerCAmelCase_     )
      return image


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Optional[int]=False			,			lowerCAmelCase_     :							Optional[Any]="CompVis/stable-diffusion-v1-4"     ):

      """simple docstring"""
      _A:  Tuple					=       jnp.bfloataa if fpaa else jnp.floataa
      _A:  str					=       '''bf16''' if fpaa else None

      _A			,  _A:  Union[str, Any]					=       FlaxUNetaDConditionModel.from_pretrained(
          lowerCAmelCase_			,			subfolder='''unet'''			,			dtype=lowerCAmelCase_			,			revision=lowerCAmelCase_     )
      return model, params


   def  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							Optional[int]=0			,			lowerCAmelCase_     :							str=(4, 7_7, 7_6_8)			,			lowerCAmelCase_     :							Dict=False     ):

      """simple docstring"""
      _A:  Optional[int]					=       jnp.bfloataa if fpaa else jnp.floataa
      _A:  Union[str, Any]					=       jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_			,			lowerCAmelCase_     )     )			,			dtype=lowerCAmelCase_     )
      return hidden_states


   @parameterized.expand(
       [
           # fmt: off
           [8_3, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
           [1_7, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
           [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
           [3, 1_0_0_0, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
           # fmt: on
       ]     )
   def  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Any     ):

      """simple docstring"""
      _A			,  _A:  Optional[Any]					=       self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''			,			fpaa=lowerCAmelCase_     )
      _A:  List[str]					=       self.get_latents(lowerCAmelCase_			,			fpaa=lowerCAmelCase_     )
      _A:  Optional[int]					=       self.get_encoder_hidden_states(lowerCAmelCase_			,			fpaa=lowerCAmelCase_     )

      _A:  List[str]					=       model.apply(
          {'''params''': params}			,			lowerCAmelCase_			,			jnp.array(lowerCAmelCase_			,			dtype=jnp.intaa     )			,			encoder_hidden_states=lowerCAmelCase_			,			).sample

      assert sample.shape == latents.shape

      _A:  Any					=       jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())     )			,			dtype=jnp.floataa     )
      _A:  Tuple					=       jnp.array(lowerCAmelCase_			,			dtype=jnp.floataa     )

      # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
      assert jnp.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-2     )


   @parameterized.expand(
       [
           # fmt: off
           [8_3, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
           [1_7, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
           [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
           [3, 1_0_0_0, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
           # fmt: on
       ]     )
   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							Optional[Any]			,			lowerCAmelCase_     :							Tuple     ):

      """simple docstring"""
      _A			,  _A:  Union[str, Any]					=       self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''			,			fpaa=lowerCAmelCase_     )
      _A:  Dict					=       self.get_latents(lowerCAmelCase_			,			shape=(4, 4, 9_6, 9_6)			,			fpaa=lowerCAmelCase_     )
      _A:  Dict					=       self.get_encoder_hidden_states(lowerCAmelCase_			,			shape=(4, 7_7, 1_0_2_4)			,			fpaa=lowerCAmelCase_     )

      _A:  Optional[int]					=       model.apply(
          {'''params''': params}			,			lowerCAmelCase_			,			jnp.array(lowerCAmelCase_			,			dtype=jnp.intaa     )			,			encoder_hidden_states=lowerCAmelCase_			,			).sample

      assert sample.shape == latents.shape

      _A:  List[str]					=       jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())     )			,			dtype=jnp.floataa     )
      _A:  List[str]					=       jnp.array(lowerCAmelCase_			,			dtype=jnp.floataa     )

      # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
      assert jnp.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-2     )



