




'''simple docstring'''


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		UpperCAmelCase_ 		=      "platform"

		import jax
		import jax.numpy as jnp

		from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
		    FlaxBlenderbotSmallForConditionalGeneration,
		    FlaxBlenderbotSmallModel,
		    shift_tokens_right,
		)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict				,					a_	:							List[str]				,					a_	:							Optional[Any]=None				,					a_	:							Optional[Any]=None				,					a_	:							List[Any]=None				,					a_	:							Tuple=None				,					a_	:							List[Any]=None				,					a_	:							Optional[int]=None				,					):






		if attention_mask is None:
				__a			=					np.where(input_ids != config.pad_token_id				,					1				,					0			)
		if decoder_attention_mask is None:
				__a			=					np.where(decoder_input_ids != config.pad_token_id				,					1				,					0			)
		if head_mask is None:
				__a			=					np.ones((config.encoder_layers, config.encoder_attention_heads)			)
		if decoder_head_mask is None:
				__a			=					np.ones((config.decoder_layers, config.decoder_attention_heads)			)
		if cross_attn_head_mask is None:
				__a			=					np.ones((config.decoder_layers, config.decoder_attention_heads)			)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		}





class    __lowercase						:







			def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase=13      ,							UpperCamelCase=7      ,							UpperCamelCase=True      ,							UpperCamelCase=False      ,							UpperCamelCase=99      ,							UpperCamelCase=16      ,							UpperCamelCase=2      ,							UpperCamelCase=4      ,							UpperCamelCase=4      ,							UpperCamelCase="gelu"      ,							UpperCamelCase=0.1      ,							UpperCamelCase=0.1      ,							UpperCamelCase=32      ,							UpperCamelCase=2      ,							UpperCamelCase=1      ,							UpperCamelCase=0      ,							UpperCamelCase=0.02      ,							)				->    str:
					__a			=					parent
					__a			=					batch_size
					__a			=					seq_length
					__a			=					is_training
					__a			=					use_labels
					__a			=					vocab_size
					__a			=					hidden_size
					__a			=					num_hidden_layers
					__a			=					num_attention_heads
					__a			=					intermediate_size
					__a			=					hidden_act
					__a			=					hidden_dropout_prob
					__a			=					attention_probs_dropout_prob
					__a			=					max_position_embeddings
					__a			=					eos_token_id
					__a			=					pad_token_id
					__a			=					bos_token_id
					__a			=					initializer_range







			def 		UpperCamelCase__			(	self      )				->    Tuple:
					__a			=					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,							self.vocab_size      )      ,							3      ,							self.vocab_size      )
					__a			=					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,							dtype=np.intaa      ))      ,							-1      )

					__a			=					shift_tokens_right(lowerCamelCase_      ,							1      ,							2      )

					__a			=					BlenderbotSmallConfig(
					    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_id=self.eos_token_id      ,							bos_token_id=self.bos_token_id      ,							pad_token_id=self.pad_token_id      ,							initializer_range=self.initializer_range      ,							use_cache=lowerCamelCase_      ,							)
					__a			=					prepare_blenderbot_inputs_dict(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      )
					return config, inputs_dict







			def 		UpperCamelCase__			(	self      )				->    int:
					__a			=					self.prepare_config_and_inputs()
					return config, inputs_dict







			def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Union[str, Any]:
					__a			=					20
					__a			=					model_class_name(lowerCamelCase_      )

					__a			=					model.encode(inputs_dict['input_ids']      )

					__a			=					(
					    inputs_dict['''decoder_input_ids'''],
					    inputs_dict['''decoder_attention_mask'''],
					)

					__a			=					model.init_cache(decoder_input_ids.shape[0]      ,							lowerCamelCase_      ,							lowerCamelCase_      )
					__a			=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,							dtype='i4'      )

					__a			=					jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]      ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,							)
					__a			=					model.decode(
					    decoder_input_ids[:, :-1]      ,							lowerCamelCase_      ,							decoder_attention_mask=lowerCamelCase_      ,							past_key_values=lowerCamelCase_      ,							decoder_position_ids=lowerCamelCase_      ,							)

					__a			=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,							dtype='i4'      )
					__a			=					model.decode(
					    decoder_input_ids[:, -1:]      ,							lowerCamelCase_      ,							decoder_attention_mask=lowerCamelCase_      ,							past_key_values=outputs_cache.past_key_values      ,							decoder_position_ids=lowerCamelCase_      ,							)

					__a			=					model.decode(lowerCamelCase_      ,							lowerCamelCase_      )

					__a			=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
					self.parent.assertTrue(diff < 1e-3      ,							msg=f"Max diff is {diff}"      )







			def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Dict:
					__a			=					20
					__a			=					model_class_name(lowerCamelCase_      )

					__a			=					model.encode(inputs_dict['input_ids']      )

					__a			=					(
					    inputs_dict['''decoder_input_ids'''],
					    inputs_dict['''decoder_attention_mask'''],
					)

					__a			=					jnp.concatenate(
					    [
					        decoder_attention_mask,
					        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])      ),
					    ]      ,							axis=-1      ,							)

					__a			=					model.init_cache(decoder_input_ids.shape[0]      ,							lowerCamelCase_      ,							lowerCamelCase_      )
					__a			=					jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]      ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,							)

					__a			=					model.decode(
					    decoder_input_ids[:, :-1]      ,							lowerCamelCase_      ,							decoder_attention_mask=lowerCamelCase_      ,							past_key_values=lowerCamelCase_      ,							decoder_position_ids=lowerCamelCase_      ,							)
					__a			=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,							dtype='i4'      )
					__a			=					model.decode(
					    decoder_input_ids[:, -1:]      ,							lowerCamelCase_      ,							past_key_values=outputs_cache.past_key_values      ,							decoder_attention_mask=lowerCamelCase_      ,							decoder_position_ids=lowerCamelCase_      ,							)

					__a			=					model.decode(lowerCamelCase_      ,							lowerCamelCase_      ,							decoder_attention_mask=lowerCamelCase_      )

					__a			=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
					self.parent.assertTrue(diff < 1e-3      ,							msg=f"Max diff is {diff}"      )





@require_flax
class    __lowercase						(  unittest.TestCase ):
			_a							=      99







			def 		UpperCamelCase__			(	self      )				->    Dict:
					__a			=					np.array(
					    [
					        [71, 82, 18, 33, 46, 91, 2],
					        [68, 34, 26, 58, 30, 82, 2],
					        [5, 97, 17, 39, 94, 40, 2],
					        [76, 83, 94, 25, 70, 78, 2],
					        [87, 59, 41, 35, 48, 66, 2],
					        [55, 13, 16, 58, 5, 2, 1],  # note padding
					        [64, 27, 31, 51, 12, 75, 2],
					        [52, 64, 86, 17, 83, 39, 2],
					        [48, 61, 9, 24, 71, 82, 2],
					        [26, 1, 60, 48, 22, 13, 2],
					        [21, 5, 62, 28, 14, 76, 2],
					        [45, 98, 37, 86, 59, 48, 2],
					        [70, 70, 50, 9, 28, 0, 2],
					    ]      ,							dtype=np.intaa      ,							)

					__a			=					input_ids.shape[0]
					__a			=					BlenderbotSmallConfig(
					    vocab_size=self.vocab_size      ,							d_model=24      ,							encoder_layers=2      ,							decoder_layers=2      ,							encoder_attention_heads=2      ,							decoder_attention_heads=2      ,							encoder_ffn_dim=32      ,							decoder_ffn_dim=32      ,							max_position_embeddings=48      ,							eos_token_id=2      ,							pad_token_id=1      ,							bos_token_id=0      ,							)
					return config, input_ids, batch_size







			def 		UpperCamelCase__			(	self      )				->    Tuple:
					__a			=					self._get_config_and_data()
					__a			=					FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase_      )
					__a			=					lm_model(input_ids=lowerCamelCase_      )
					__a			=					(batch_size, input_ids.shape[1], config.vocab_size)
					self.assertEqual(outputs['logits'].shape      ,							lowerCamelCase_      )







			def 		UpperCamelCase__			(	self      )				->    int:
					__a			=					BlenderbotSmallConfig(
					    vocab_size=self.vocab_size      ,							d_model=14      ,							encoder_layers=2      ,							decoder_layers=2      ,							encoder_attention_heads=2      ,							decoder_attention_heads=2      ,							encoder_ffn_dim=8      ,							decoder_ffn_dim=8      ,							max_position_embeddings=48      ,							)
					__a			=					FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase_      )
					__a			=					np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]      ,							dtype=np.intaa      )
					__a			=					np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]      ,							dtype=np.intaa      )
					__a			=					lm_model(input_ids=lowerCamelCase_      ,							decoder_input_ids=lowerCamelCase_      )
					__a			=					(*summary.shape, config.vocab_size)
					self.assertEqual(outputs['logits'].shape      ,							lowerCamelCase_      )







			def 		UpperCamelCase__			(	self      )				->    List[str]:
					__a			=					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]      ,							dtype=np.intaa      )
					__a			=					shift_tokens_right(lowerCamelCase_      ,							1      ,							2      )
					__a			=					np.equal(lowerCamelCase_      ,							1      ).astype(np.floataa      ).sum()
					__a			=					np.equal(lowerCamelCase_      ,							1      ).astype(np.floataa      ).sum()
					self.assertEqual(shifted.shape      ,							input_ids.shape      )
					self.assertEqual(lowerCamelCase_      ,							n_pad_before - 1      )
					self.assertTrue(np.equal(shifted[:, 0]      ,							2      ).all()      )





@require_flax
class    __lowercase						(  _UpperCAmelCase       ,							unittest.TestCase       ,							_UpperCAmelCase ):
			_a							=      True
			_a							=      (
			    (
			        FlaxBlenderbotSmallModel,
			        FlaxBlenderbotSmallForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			_a							=      (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()







			def 		UpperCamelCase__			(	self      )				->    List[Any]:
					__a			=					FlaxBlenderbotSmallModelTester(self      )







			def 		UpperCamelCase__			(	self      )				->    int:
					__a			=					self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      )







			def 		UpperCamelCase__			(	self      )				->    Dict:
					__a			=					self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      )







			def 		UpperCamelCase__			(	self      )				->    Any:
					__a			=					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__      ):
									__a			=					self._prepare_for_class(lowerCamelCase_      ,							lowerCamelCase_      )
									__a			=					model_class(lowerCamelCase_      )

									@jax.jit
									def encode_jitted(UpperCamelCase      ,							UpperCamelCase=None      ,							**UpperCamelCase      ):
											return model.encode(input_ids=lowerCamelCase_      ,							attention_mask=lowerCamelCase_      )

									with self.subTest('JIT Enabled'      ):
											__a			=					encode_jitted(**lowerCamelCase_      ).to_tuple()

									with self.subTest('JIT Disabled'      ):
											with jax.disable_jit():
													__a			=					encode_jitted(**lowerCamelCase_      ).to_tuple()

									self.assertEqual(len(lowerCamelCase_      )      ,							len(lowerCamelCase_      )      )
									for jitted_output, output in zip(lowerCamelCase_      ,							lowerCamelCase_      ):
											self.assertEqual(jitted_output.shape      ,							output.shape      )







			def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
					__a			=					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__      ):
									__a			=					model_class(lowerCamelCase_      )
									__a			=					model.encode(inputs_dict['input_ids']      ,							inputs_dict['attention_mask']      )

									__a			=					{
									    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
									    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
									    '''encoder_outputs''': encoder_outputs,
									}

									@jax.jit
									def decode_jitted(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ):
											return model.decode(
											    decoder_input_ids=lowerCamelCase_      ,							decoder_attention_mask=lowerCamelCase_      ,							encoder_outputs=lowerCamelCase_      ,							)

									with self.subTest('JIT Enabled'      ):
											__a			=					decode_jitted(**lowerCamelCase_      ).to_tuple()

									with self.subTest('JIT Disabled'      ):
											with jax.disable_jit():
													__a			=					decode_jitted(**lowerCamelCase_      ).to_tuple()

									self.assertEqual(len(lowerCamelCase_      )      ,							len(lowerCamelCase_      )      )
									for jitted_output, output in zip(lowerCamelCase_      ,							lowerCamelCase_      ):
											self.assertEqual(jitted_output.shape      ,							output.shape      )







			@slow
			def 		UpperCamelCase__			(	self      )				->    Optional[int]:




					for model_class_name in self.all_model_classes:
							__a			=					model_class_name.from_pretrained('facebook/blenderbot_small-90M'      )
							# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
							__a			=					np.ones((1, 1)      ) * model.config.eos_token_id
							__a			=					model(lowerCamelCase_      )
							self.assertIsNotNone(lowerCamelCase_      )









'''simple docstring'''


import os
from datetime import datetime as dt

from github import Github


UpperCAmelCase_ 		=      [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "enhancement",
    "new pipeline/model",
    "new scheduler",
    "wip",
]


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					Github(os.environ['GITHUB_TOKEN']			)
  __a			=					g.get_repo('huggingface/diffusers'			)
  __a			=					repo.get_issues(state='open'			)

  for issue in open_issues:
    __a			=					sorted(issue.get_comments()				,					key=lambda a_			: i.created_at				,					reverse=a_			)
    __a			=					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()			)
    ):
      # Closes the issue after 7 days of inactivity since the Stalebot notification.
      issue.edit(state='closed'			)
    elif (
        "stale" in issue.get_labels()
        and last_comment is not None
        and last_comment.user.login != "github-actions[bot]"
    ):
      # Opens the issue if someone other than Stalebot commented.
      issue.edit(state='open'			)
      issue.remove_from_labels('stale'			)
    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()			)
    ):
      # Post a Stalebot notification after 23 days of inactivity.
      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/diffusers/blob/main/CONTRIBUTING.md) '
          'are likely to be ignored.'			)
      issue.add_to_labels('stale'			)


if __name__ == "__main__":
  main()



