






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   :							str					,	__snake_case   :							str					,	__snake_case   :							Tuple=None					,	__snake_case   :							Any=None					,	__snake_case   :							Dict=None					,	__snake_case   :							str=None					,	__snake_case   :							Optional[Any]=None					,	__snake_case   :							str=None					,	):


					'''simple docstring'''


					if attention_mask is None:
										UpperCAmelCase_   :				Optional[Any]						=						np.where(input_ids != config.pad_token_id					,	1					,	0  )
					if decoder_attention_mask is None:
										UpperCAmelCase_   :				List[Any]						=						np.where(decoder_input_ids != config.pad_token_id					,	1					,	0  )
					if head_mask is None:
										UpperCAmelCase_   :				Tuple						=						np.ones((config.encoder_layers, config.encoder_attention_heads)  )
					if decoder_head_mask is None:
										UpperCAmelCase_   :				int						=						np.ones((config.decoder_layers, config.decoder_attention_heads)  )
					if cross_attn_head_mask is None:
										UpperCAmelCase_   :				Union[str, 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    ,		)     ->							Dict:
							UpperCAmelCase_   :				Optional[Any]						=						parent
							UpperCAmelCase_   :				Union[str, Any]						=						batch_size
							UpperCAmelCase_   :				str						=						seq_length
							UpperCAmelCase_   :				Union[str, Any]						=						is_training
							UpperCAmelCase_   :				Optional[Any]						=						use_labels
							UpperCAmelCase_   :				List[Any]						=						vocab_size
							UpperCAmelCase_   :				str						=						hidden_size
							UpperCAmelCase_   :				str						=						num_hidden_layers
							UpperCAmelCase_   :				Optional[int]						=						num_attention_heads
							UpperCAmelCase_   :				Tuple						=						intermediate_size
							UpperCAmelCase_   :				Tuple						=						hidden_act
							UpperCAmelCase_   :				Dict						=						hidden_dropout_prob
							UpperCAmelCase_   :				Any						=						attention_probs_dropout_prob
							UpperCAmelCase_   :				Any						=						max_position_embeddings
							UpperCAmelCase_   :				Tuple						=						eos_token_id
							UpperCAmelCase_   :				Any						=						pad_token_id
							UpperCAmelCase_   :				Optional[Any]						=						bos_token_id
							UpperCAmelCase_   :				Any						=						initializer_range



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

							UpperCAmelCase_   :				Optional[Any]						=						shift_tokens_right(lowerCAmelCase__    ,		1    ,		2       )

							UpperCAmelCase_   :				Optional[Any]						=						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=lowerCAmelCase__    ,		)
							UpperCAmelCase_   :				Optional[Any]						=						prepare_blenderbot_inputs_dict(lowerCAmelCase__    ,		lowerCAmelCase__    ,		lowerCAmelCase__       )
							return config, inputs_dict



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						self.prepare_config_and_inputs()
							return config, inputs_dict



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

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

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

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

							UpperCAmelCase_   :				List[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_   :				Union[str, Any]						=						model.decode(
							    decoder_input_ids[:, :-1]    ,		lowerCAmelCase__    ,		decoder_attention_mask=lowerCAmelCase__    ,		past_key_values=lowerCAmelCase__    ,		decoder_position_ids=lowerCAmelCase__    ,		)

							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:]    ,		lowerCAmelCase__    ,		decoder_attention_mask=lowerCAmelCase__    ,		past_key_values=outputs_cache.past_key_values    ,		decoder_position_ids=lowerCAmelCase__    ,		)

							UpperCAmelCase_   :				Union[str, Any]						=						model.decode(lowerCAmelCase__    ,		lowerCAmelCase__       )

							UpperCAmelCase_   :				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}"       )



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

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

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

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

							UpperCAmelCase_   :				int						=						model.init_cache(decoder_input_ids.shape[0]    ,		lowerCAmelCase__    ,		lowerCAmelCase__       )
							UpperCAmelCase_   :				List[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_   :				Union[str, Any]						=						model.decode(
							    decoder_input_ids[:, :-1]    ,		lowerCAmelCase__    ,		decoder_attention_mask=lowerCAmelCase__    ,		past_key_values=lowerCAmelCase__    ,		decoder_position_ids=lowerCAmelCase__    ,		)
							UpperCAmelCase_   :				int						=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,		dtype='i4'       )
							UpperCAmelCase_   :				Optional[Any]						=						model.decode(
							    decoder_input_ids[:, -1:]    ,		lowerCAmelCase__    ,		past_key_values=outputs_cache.past_key_values    ,		decoder_attention_mask=lowerCAmelCase__    ,		decoder_position_ids=lowerCAmelCase__    ,		)

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

							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[Any]							=  9_9



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				Dict						=						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_   :				Dict						=						input_ids.shape[0]
							UpperCAmelCase_   :				Dict						=						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       )     ->							int:
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						self._get_config_and_data()
							UpperCAmelCase_   :				Optional[int]						=						FlaxBlenderbotForConditionalGeneration(lowerCAmelCase__       )
							UpperCAmelCase_   :				Dict						=						lm_model(input_ids=lowerCAmelCase__       )
							UpperCAmelCase_   :				List[str]						=						(batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs['logits'].shape    ,		lowerCAmelCase__       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				str						=						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_   :				str						=						FlaxBlenderbotForConditionalGeneration(lowerCAmelCase__       )
							UpperCAmelCase_   :				Optional[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_   :				List[Any]						=						np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]    ,		dtype=np.intaa       )
							UpperCAmelCase_   :				int						=						lm_model(input_ids=lowerCAmelCase__    ,		decoder_input_ids=lowerCAmelCase__       )
							UpperCAmelCase_   :				Optional[int]						=						(*summary.shape, config.vocab_size)
							self.assertEqual(outputs['logits'].shape    ,		lowerCAmelCase__       )



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				List[str]						=						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_   :				List[Any]						=						shift_tokens_right(lowerCAmelCase__    ,		1    ,		2       )
							UpperCAmelCase_   :				Union[str, Any]						=						np.equal(lowerCAmelCase__    ,		1       ).astype(np.floataa       ).sum()
							UpperCAmelCase_   :				Tuple						=						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   lowerCamelCase				(lowerCamelCase_			,  unittest.TestCase			,  lowerCamelCase_      ):







		'''simple docstring'''



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



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				Dict						=						FlaxBlenderbotModelTester(self       )



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_	,  UpperCAmelCase_   :				Union[str, Any]						=						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       )     ->							Any:
							UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						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       )     ->							List[Any]:
							UpperCAmelCase_	,  UpperCAmelCase_   :				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_   :				List[Any]						=						self._prepare_for_class(lowerCAmelCase__    ,		lowerCAmelCase__       )
																	UpperCAmelCase_   :				Any						=						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'       ):
																						UpperCAmelCase_   :				List[str]						=						encode_jitted(**lowerCAmelCase__       ).to_tuple()

																	with self.subTest('JIT Disabled'       ):
																						with jax.disable_jit():
																											UpperCAmelCase_   :				Optional[int]						=						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       )     ->							Tuple:
							UpperCAmelCase_	,  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_   :				Optional[int]						=						model_class(lowerCAmelCase__       )
																	UpperCAmelCase_   :				Optional[Any]						=						model.encode(inputs_dict['input_ids']    ,		inputs_dict['attention_mask']       )

																	UpperCAmelCase_   :				Dict						=						{
																	    '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'       ):
																						UpperCAmelCase_   :				List[str]						=						decode_jitted(**lowerCAmelCase__       ).to_tuple()

																	with self.subTest('JIT Disabled'       ):
																						with jax.disable_jit():
																											UpperCAmelCase_   :				str						=						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       )     ->							List[Any]:
							for model_class_name in self.all_model_classes:
												UpperCAmelCase_   :				Tuple						=						model_class_name.from_pretrained('facebook/blenderbot-400M-distill'       )
												# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
												UpperCAmelCase_   :				Optional[int]						=						np.ones((1, 1)       ) * model.config.eos_token_id
												UpperCAmelCase_   :				str						=						model(lowerCAmelCase__       )
												self.assertIsNotNone(lowerCAmelCase__       )



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

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

							UpperCAmelCase_   :				List[str]						=						['Sam']
							UpperCAmelCase_   :				Optional[Any]						=						tokenizer(lowerCAmelCase__    ,		return_tensors='jax'       )

							UpperCAmelCase_   :				Tuple						=						model.generate(**lowerCAmelCase__    ,		**lowerCAmelCase__       )
							UpperCAmelCase_   :				List[Any]						=						'Sam is a great name. It means "sun" in Gaelic.'

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











def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Tuple						=						[1]

					UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						0, 0, 0
					UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 2
					UpperCAmelCase_   :				Tuple						=						ugly_nums[ia] * 3
					UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 5

					for _ in range(1					,	__snake_case  ):
										UpperCAmelCase_   :				Tuple						=						min(__snake_case					,	__snake_case					,	__snake_case  )
										ugly_nums.append(__snake_case  )
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 2
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				Any						=						ugly_nums[ia] * 3
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				List[str]						=						ugly_nums[ia] * 5
					return ugly_nums[-1]


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

		testmod(verbose=True)
		print(F'{ugly_numbers(200) = }')






