






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, 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 import BlenderbotTokenizer
		from transformers.models.blenderbot.modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    shift_tokens_right,
		)




def 					lowercase__				(					__snake_case   :							List[str]					,	__snake_case   :							List[Any]					,	__snake_case   :							Tuple=None					,	__snake_case   :							Union[str, Any]=None					,	__snake_case   :							Optional[int]=None					,	__snake_case   :							List[str]=None					,	__snake_case   :							Optional[int]=None					,	__snake_case   :							Any=None					,	):


					'''simple docstring'''


					if attention_mask is None:
										UpperCAmelCase_   :				Dict						=				np.where(input_ids != config.pad_token_id					,	1					,	0  )
					if decoder_attention_mask is None:
										UpperCAmelCase_   :				List[str]						=				np.where(decoder_input_ids != config.pad_token_id					,	1					,	0  )
					if head_mask is None:
										UpperCAmelCase_   :				List[str]						=				np.ones((config.encoder_layers, config.encoder_attention_heads)  )
					if decoder_head_mask is None:
										UpperCAmelCase_   :				str						=				np.ones((config.decoder_layers, config.decoder_attention_heads)  )
					if cross_attn_head_mask is None:
										UpperCAmelCase_   :				Optional[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   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase=1_3    ,		_UpperCamelCase=7    ,		_UpperCamelCase=True    ,		_UpperCamelCase=False    ,		_UpperCamelCase=9_9    ,		_UpperCamelCase=1_6    ,		_UpperCamelCase=2    ,		_UpperCamelCase=4    ,		_UpperCamelCase=4    ,		_UpperCamelCase="gelu"    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=2    ,		_UpperCamelCase=1    ,		_UpperCamelCase=0    ,		_UpperCamelCase=0.02    ,		)     ->							List[str]:
							UpperCAmelCase_   :				Dict						=				parent
							UpperCAmelCase_   :				List[Any]						=				batch_size
							UpperCAmelCase_   :				Optional[Any]						=				seq_length
							UpperCAmelCase_   :				Optional[Any]						=				is_training
							UpperCAmelCase_   :				Any						=				use_labels
							UpperCAmelCase_   :				Union[str, Any]						=				vocab_size
							UpperCAmelCase_   :				Any						=				hidden_size
							UpperCAmelCase_   :				List[Any]						=				num_hidden_layers
							UpperCAmelCase_   :				Tuple						=				num_attention_heads
							UpperCAmelCase_   :				Tuple						=				intermediate_size
							UpperCAmelCase_   :				Optional[int]						=				hidden_act
							UpperCAmelCase_   :				str						=				hidden_dropout_prob
							UpperCAmelCase_   :				Any						=				attention_probs_dropout_prob
							UpperCAmelCase_   :				Optional[Any]						=				max_position_embeddings
							UpperCAmelCase_   :				List[Any]						=				eos_token_id
							UpperCAmelCase_   :				Any						=				pad_token_id
							UpperCAmelCase_   :				Union[str, Any]						=				bos_token_id
							UpperCAmelCase_   :				List[str]						=				initializer_range



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

							UpperCAmelCase_   :				Any						=				shift_tokens_right(_A    ,		1    ,		2       )

							UpperCAmelCase_   :				int						=				BlenderbotConfig(
							    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=_A    ,		)
							UpperCAmelCase_   :				List[str]						=				prepare_blenderbot_inputs_dict(_A    ,		_A    ,		_A       )
							return config, inputs_dict



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				Dict						=				self.prepare_config_and_inputs()
							return config, inputs_dict



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Optional[int]:
							UpperCAmelCase_   :				Optional[Any]						=				2_0
							UpperCAmelCase_   :				List[str]						=				model_class_name(_A       )

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

							UpperCAmelCase_   :				Any						=				(
							    inputs_dict['decoder_input_ids'],
							    inputs_dict['decoder_attention_mask'],
							)

							UpperCAmelCase_   :				List[Any]						=				model.init_cache(decoder_input_ids.shape[0]    ,		_A    ,		_A       )
							UpperCAmelCase_   :				List[Any]						=				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,		dtype='i4'       )

							UpperCAmelCase_   :				Optional[int]						=				jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]    ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,		)
							UpperCAmelCase_   :				Any						=				model.decode(
							    decoder_input_ids[:, :-1]    ,		_A    ,		decoder_attention_mask=_A    ,		past_key_values=_A    ,		decoder_position_ids=_A    ,		)

							UpperCAmelCase_   :				Dict						=				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,		dtype='i4'       )
							UpperCAmelCase_   :				List[Any]						=				model.decode(
							    decoder_input_ids[:, -1:]    ,		_A    ,		decoder_attention_mask=_A    ,		past_key_values=outputs_cache.past_key_values    ,		decoder_position_ids=_A    ,		)

							UpperCAmelCase_   :				Optional[Any]						=				model.decode(_A    ,		_A       )

							UpperCAmelCase_   :				Union[str, Any]						=				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       )     ->							Tuple:
							UpperCAmelCase_   :				Dict						=				2_0
							UpperCAmelCase_   :				Dict						=				model_class_name(_A       )

							UpperCAmelCase_   :				Union[str, Any]						=				model.encode(inputs_dict['input_ids']       )

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

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

							UpperCAmelCase_   :				Optional[Any]						=				model.init_cache(decoder_input_ids.shape[0]    ,		_A    ,		_A       )
							UpperCAmelCase_   :				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)    ,		)

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

							UpperCAmelCase_   :				Dict						=				model.decode(_A    ,		_A    ,		decoder_attention_mask=_A       )

							UpperCAmelCase_   :				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}"       )





@require_flax
class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   List[str]							=  9_9



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				List[str]						=				np.array(
							    [
							        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
							        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
							        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
							        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
							        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
							        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
							        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
							        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
							        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
							        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
							        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
							        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
							        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
							    ]    ,		dtype=np.intaa    ,		)

							UpperCAmelCase_   :				Tuple						=				input_ids.shape[0]
							UpperCAmelCase_   :				Any						=				BlenderbotConfig(
							    vocab_size=self.vocab_size    ,		d_model=2_4    ,		encoder_layers=2    ,		decoder_layers=2    ,		encoder_attention_heads=2    ,		decoder_attention_heads=2    ,		encoder_ffn_dim=3_2    ,		decoder_ffn_dim=3_2    ,		max_position_embeddings=4_8    ,		eos_token_id=2    ,		pad_token_id=1    ,		bos_token_id=0    ,		)
							return config, input_ids, batch_size



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				List[Any]						=				self._get_config_and_data()
							UpperCAmelCase_   :				str						=				FlaxBlenderbotForConditionalGeneration(_A       )
							UpperCAmelCase_   :				int						=				lm_model(input_ids=_A       )
							UpperCAmelCase_   :				Tuple						=				(batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs['logits'].shape    ,		_A       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				Optional[Any]						=				BlenderbotConfig(
							    vocab_size=self.vocab_size    ,		d_model=1_4    ,		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=4_8    ,		)
							UpperCAmelCase_   :				Optional[Any]						=				FlaxBlenderbotForConditionalGeneration(_A       )
							UpperCAmelCase_   :				Any						=				np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]    ,		dtype=np.intaa       )
							UpperCAmelCase_   :				Optional[int]						=				np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]    ,		dtype=np.intaa       )
							UpperCAmelCase_   :				Optional[int]						=				lm_model(input_ids=_A    ,		decoder_input_ids=_A       )
							UpperCAmelCase_   :				Optional[Any]						=				(*summary.shape, config.vocab_size)
							self.assertEqual(outputs['logits'].shape    ,		_A       )



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				Union[str, Any]						=				np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]    ,		dtype=np.intaa       )
							UpperCAmelCase_   :				Dict						=				shift_tokens_right(_A    ,		1    ,		2       )
							UpperCAmelCase_   :				Tuple						=				np.equal(_A    ,		1       ).astype(np.floataa       ).sum()
							UpperCAmelCase_   :				List[str]						=				np.equal(_A    ,		1       ).astype(np.floataa       ).sum()
							self.assertEqual(shifted.shape    ,		input_ids.shape       )
							self.assertEqual(_A    ,		n_pad_before - 1       )
							self.assertTrue(np.equal(shifted[:, 0]    ,		2       ).all()       )





@require_flax
class   lowerCamelCase				(snake_case_			,  unittest.TestCase			,  snake_case_      ):







		'''simple docstring'''



		_snake_case   :   List[str]							=  True
		_snake_case   :   int							=  (
		    (
		        FlaxBlenderbotModel,
		        FlaxBlenderbotForConditionalGeneration,
		    )
		    if is_flax_available()
		    else ()
		)
		_snake_case   :   Optional[int]							=  (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				Optional[Any]						=				FlaxBlenderbotModelTester(self       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				str						=				self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward(_A    ,		_A    ,		_A       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				Optional[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(_A    ,		_A    ,		_A       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				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__       ):
																	UpperCAmelCase_   :				Dict						=				self._prepare_for_class(_A    ,		_A       )
																	UpperCAmelCase_   :				Dict						=				model_class(_A       )

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

																	with self.subTest('JIT Enabled'       ):
																						UpperCAmelCase_   :				Dict						=				encode_jitted(**_A       ).to_tuple()

																	with self.subTest('JIT Disabled'       ):
																						with jax.disable_jit():
																											UpperCAmelCase_   :				int						=				encode_jitted(**_A       ).to_tuple()

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



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				Tuple						=				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__       ):
																	UpperCAmelCase_   :				Tuple						=				model_class(_A       )
																	UpperCAmelCase_   :				int						=				model.encode(inputs_dict['input_ids']    ,		inputs_dict['attention_mask']       )

																	UpperCAmelCase_   :				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    ,		_UpperCamelCase    ,		_UpperCamelCase       ):
																						return model.decode(
																						    decoder_input_ids=_A    ,		decoder_attention_mask=_A    ,		encoder_outputs=_A    ,		)

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

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

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



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:


							for model_class_name in self.all_model_classes:
												UpperCAmelCase_   :				List[Any]						=				model_class_name.from_pretrained('facebook/blenderbot-400M-distill'       )
												# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
												UpperCAmelCase_   :				int						=				np.ones((1, 1)       ) * model.config.eos_token_id
												UpperCAmelCase_   :				Any						=				model(_A       )
												self.assertIsNotNone(_A       )



		@unittest.skipUnless(jax_device != 'cpu'    ,		'3B test too slow on CPU.'       )
		@slow
		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				Optional[int]						=				{'num_beams': 1, 'early_stopping': True, 'min_length': 1_5, 'max_length': 2_5}
							UpperCAmelCase_   :				int						=				{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

							UpperCAmelCase_   :				Any						=				FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'    ,		from_pt=_A       )
							UpperCAmelCase_   :				Optional[Any]						=				BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'       )

							UpperCAmelCase_   :				Optional[int]						=				['Sam']
							UpperCAmelCase_   :				Optional[Any]						=				tokenizer(_A    ,		return_tensors='jax'       )

							UpperCAmelCase_   :				int						=				model.generate(**_A    ,		**_A       )
							UpperCAmelCase_   :				Tuple						=				'Sam is a great name. It means "sun" in Gaelic.'

							UpperCAmelCase_   :				Union[str, Any]						=				tokenizer.batch_decode(_A    ,		**_A       )
							assert generated_txt[0].strip() == tgt_text














from __future__ import annotations




def 					lowercase__				(					__snake_case   :							list[int]					,	__snake_case   :							int  ):


					'''simple docstring'''


					if len(__snake_case  ) < k or k < 0:
										raise ValueError('Invalid Input'  )
					UpperCAmelCase_   :				int						=						sum(array[:k]  )
					for i in range(len(__snake_case  ) - k  ):
										UpperCAmelCase_   :				List[Any]						=						current_sum - array[i] + array[i + k]
										UpperCAmelCase_   :				List[Any]						=						max(__snake_case					,	__snake_case  )
					return max_sum


if __name__ == "__main__":
		from doctest import testmod
		from random import randint

		testmod()
		__UpperCAmelCase     							=						[randint(-1000, 1000) for i in range(100)]
		__UpperCAmelCase     							=						randint(0, 110)
		print(F'The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}')






