



'''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
							__lowerCAmelCase						      =       '''platform'''

							import jax
							import jax.numpy as jnp

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



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=None						,      lowerCAmelCase_=None						,      lowerCAmelCase_=None						,      lowerCAmelCase_=None						,      lowerCAmelCase_=None						,      lowerCAmelCase_=None						,      )      ->				str:
		if attention_mask is None:
				_a					:				Dict				=							np.where(input_ids != config.pad_token_id						,      1						,      0  )
		if decoder_attention_mask is None:
				_a					:				List[str]				=							np.where(decoder_input_ids != config.pad_token_id						,      1						,      0  )
		if head_mask is None:
				_a					:				List[str]				=							np.ones((config.encoder_layers, config.encoder_attention_heads)  )
		if decoder_head_mask is None:
				_a					:				List[Any]				=							np.ones((config.decoder_layers, config.decoder_attention_heads)  )
		if cross_attn_head_mask is None:
				_a					:				List[Any]				=							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      __magic_name__						:







							def __init__(							self :	Union[str, Any]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	List[str]=13     ,_UpperCAmelCase :	int=7     ,_UpperCAmelCase :	Any=True     ,_UpperCAmelCase :	int=False     ,_UpperCAmelCase :	List[Any]=99     ,_UpperCAmelCase :	Optional[int]=16     ,_UpperCAmelCase :	Union[str, Any]=2     ,_UpperCAmelCase :	Dict=4     ,_UpperCAmelCase :	Dict=4     ,_UpperCAmelCase :	Union[str, Any]="gelu"     ,_UpperCAmelCase :	Dict=0.1     ,_UpperCAmelCase :	List[str]=0.1     ,_UpperCAmelCase :	Dict=32     ,_UpperCAmelCase :	int=2     ,_UpperCAmelCase :	str=1     ,_UpperCAmelCase :	List[Any]=0     ,_UpperCAmelCase :	List[str]=0.02     ,):
									_a					:				Tuple				=							parent
									_a					:				Tuple				=							batch_size
									_a					:				List[Any]				=							seq_length
									_a					:				Any				=							is_training
									_a					:				str				=							use_labels
									_a					:				int				=							vocab_size
									_a					:				Tuple				=							hidden_size
									_a					:				Tuple				=							num_hidden_layers
									_a					:				Optional[int]				=							num_attention_heads
									_a					:				Optional[Any]				=							intermediate_size
									_a					:				List[str]				=							hidden_act
									_a					:				Dict				=							hidden_dropout_prob
									_a					:				Any				=							attention_probs_dropout_prob
									_a					:				List[Any]				=							max_position_embeddings
									_a					:				Tuple				=							eos_token_id
									_a					:				Tuple				=							pad_token_id
									_a					:				Optional[int]				=							bos_token_id
									_a					:				Optional[int]				=							initializer_range







							def 							__lowercase      (							self :	List[str]		):
									_a					:				List[str]				=							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,self.vocab_size		)     ,3     ,self.vocab_size		)
									_a					:				Any				=							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,dtype=np.intaa		))     ,-1		)

									_a					:				Optional[int]				=							shift_tokens_right(_UpperCAmelCase     ,1     ,2		)

									_a					:				Optional[Any]				=							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=_UpperCAmelCase     ,)
									_a					:				str				=							prepare_blenderbot_inputs_dict(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)
									return config, inputs_dict







							def 							__lowercase      (							self :	int		):
									_a	, _a					:				Optional[int]				=							self.prepare_config_and_inputs()
									return config, inputs_dict







							def 							__lowercase      (							self :	Optional[int]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	str		):
									_a					:				List[Any]				=							20
									_a					:				List[Any]				=							model_class_name(_UpperCAmelCase		)

									_a					:				Any				=							model.encode(inputs_dict['input_ids']		)

									_a	, _a					:				List[str]				=							(
									    inputs_dict['decoder_input_ids'],
									    inputs_dict['decoder_attention_mask'],
									)

									_a					:				Optional[int]				=							model.init_cache(decoder_input_ids.shape[0]     ,_UpperCAmelCase     ,_UpperCAmelCase		)
									_a					:				Optional[Any]				=							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,dtype='i4'		)

									_a					:				Union[str, Any]				=							jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]     ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,)
									_a					:				Optional[Any]				=							model.decode(
									    decoder_input_ids[:, :-1]     ,_UpperCAmelCase     ,decoder_attention_mask=_UpperCAmelCase     ,past_key_values=_UpperCAmelCase     ,decoder_position_ids=_UpperCAmelCase     ,)

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

									_a					:				Tuple				=							model.decode(_UpperCAmelCase     ,_UpperCAmelCase		)

									_a					:				str				=							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 							__lowercase      (							self :	Optional[int]     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	int		):
									_a					:				Optional[int]				=							20
									_a					:				Dict				=							model_class_name(_UpperCAmelCase		)

									_a					:				str				=							model.encode(inputs_dict['input_ids']		)

									_a	, _a					:				List[str]				=							(
									    inputs_dict['decoder_input_ids'],
									    inputs_dict['decoder_attention_mask'],
									)

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

									_a					:				Dict				=							model.init_cache(decoder_input_ids.shape[0]     ,_UpperCAmelCase     ,_UpperCAmelCase		)
									_a					:				Dict				=							jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]     ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,)

									_a					:				Any				=							model.decode(
									    decoder_input_ids[:, :-1]     ,_UpperCAmelCase     ,decoder_attention_mask=_UpperCAmelCase     ,past_key_values=_UpperCAmelCase     ,decoder_position_ids=_UpperCAmelCase     ,)
									_a					:				str				=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,dtype='i4'		)
									_a					:				Optional[int]				=							model.decode(
									    decoder_input_ids[:, -1:]     ,_UpperCAmelCase     ,past_key_values=outputs_cache.past_key_values     ,decoder_attention_mask=_UpperCAmelCase     ,decoder_position_ids=_UpperCAmelCase     ,)

									_a					:				Union[str, Any]				=							model.decode(_UpperCAmelCase     ,_UpperCAmelCase     ,decoder_attention_mask=_UpperCAmelCase		)

									_a					:				Optional[int]				=							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      __magic_name__						(   unittest.TestCase       ):
							lowerCAmelCase	:   Union[str, Any]           = 9_9







							def 							__lowercase      (							self :	Tuple		):
									_a					:				Tuple				=							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					:				List[str]				=							input_ids.shape[0]
									_a					:				str				=							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 							__lowercase      (							self :	Optional[Any]		):
									_a	, _a	, _a					:				Optional[Any]				=							self._get_config_and_data()
									_a					:				Union[str, Any]				=							FlaxBlenderbotSmallForConditionalGeneration(_UpperCAmelCase		)
									_a					:				str				=							lm_model(input_ids=_UpperCAmelCase		)
									_a					:				Any				=							(batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs['logits'].shape     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				Tuple				=							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					:				Dict				=							FlaxBlenderbotSmallForConditionalGeneration(_UpperCAmelCase		)
									_a					:				int				=							np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,dtype=np.intaa		)
									_a					:				List[Any]				=							np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,dtype=np.intaa		)
									_a					:				int				=							lm_model(input_ids=_UpperCAmelCase     ,decoder_input_ids=_UpperCAmelCase		)
									_a					:				List[str]				=							(*summary.shape, config.vocab_size)
									self.assertEqual(outputs['logits'].shape     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Tuple		):
									_a					:				str				=							np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,dtype=np.intaa		)
									_a					:				Tuple				=							shift_tokens_right(_UpperCAmelCase     ,1     ,2		)
									_a					:				Union[str, Any]				=							np.equal(_UpperCAmelCase     ,1		).astype(np.floataa		).sum()
									_a					:				str				=							np.equal(_UpperCAmelCase     ,1		).astype(np.floataa		).sum()
									self.assertEqual(shifted.shape     ,input_ids.shape		)
									self.assertEqual(_UpperCAmelCase     ,n_pad_before - 1		)
									self.assertTrue(np.equal(shifted[:, 0]     ,2		).all()		)

@require_flax
class      __magic_name__						(   _UpperCamelCase      ,						unittest.TestCase      ,						_UpperCamelCase       ):
							lowerCAmelCase	:   Union[str, Any]           = True
							lowerCAmelCase	:   Union[str, Any]           = (
							    (
							        FlaxBlenderbotSmallModel,
							        FlaxBlenderbotSmallForConditionalGeneration,
							    )
							    if is_flax_available()
							    else ()
							)
							lowerCAmelCase	:   List[str]           = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()







							def 							__lowercase      (							self :	int		):
									_a					:				List[Any]				=							FlaxBlenderbotSmallModelTester(self		)







							def 							__lowercase      (							self :	Optional[int]		):
									_a	, _a					:				str				=							self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Dict		):
									_a	, _a					:				int				=							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(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Dict		):
									_a	, _a					:				Optional[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__		):
													_a					:				Optional[int]				=							self._prepare_for_class(_UpperCAmelCase     ,_UpperCAmelCase		)
													_a					:				Union[str, Any]				=							model_class(_UpperCAmelCase		)

													@jax.jit
													def encode_jitted(_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	List[str]=None     ,**_UpperCAmelCase :	str		):
															return model.encode(input_ids=_UpperCAmelCase     ,attention_mask=_UpperCAmelCase		)

													with self.subTest('JIT Enabled'		):
															_a					:				str				=							encode_jitted(**_UpperCAmelCase		).to_tuple()

													with self.subTest('JIT Disabled'		):
															with jax.disable_jit():
																	_a					:				Tuple				=							encode_jitted(**_UpperCAmelCase		).to_tuple()

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







							def 							__lowercase      (							self :	List[Any]		):
									_a	, _a					:				Optional[int]				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__		):
													_a					:				Union[str, Any]				=							model_class(_UpperCAmelCase		)
													_a					:				List[Any]				=							model.encode(inputs_dict['input_ids']     ,inputs_dict['attention_mask']		)

													_a					:				Union[str, Any]				=							{
													    '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 :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Tuple		):
															return model.decode(
															    decoder_input_ids=_UpperCAmelCase     ,decoder_attention_mask=_UpperCAmelCase     ,encoder_outputs=_UpperCAmelCase     ,)

													with self.subTest('JIT Enabled'		):
															_a					:				Union[str, Any]				=							decode_jitted(**_UpperCAmelCase		).to_tuple()

													with self.subTest('JIT Disabled'		):
															with jax.disable_jit():
																	_a					:				Dict				=							decode_jitted(**_UpperCAmelCase		).to_tuple()

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







							@slow
							def 							__lowercase      (							self :	Tuple		):
									for model_class_name in self.all_model_classes:
											_a					:				Dict				=							model_class_name.from_pretrained('facebook/blenderbot_small-90M'		)
											# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
											_a					:				Any				=							np.ones((1, 1)		) * model.config.eos_token_id
											_a					:				List[str]				=							model(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)









'''simple docstring'''

def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list:
		if any(not isinstance(lowerCAmelCase_						,      lowerCAmelCase_  ) or x < 0 for x in sequence  ):
				raise TypeError('Sequence must be list of non-negative integers'  )
		for _ in range(len(lowerCAmelCase_  )  ):
				for i, (rod_upper, rod_lower) in enumerate(zip(lowerCAmelCase_						,      sequence[1:]  )  ):
						if rod_upper > rod_lower:
								sequence[i] -= rod_upper - rod_lower
								sequence[i + 1] += rod_upper - rod_lower
		return sequence


if __name__ == "__main__":
							assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
							assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]




