
"""simple docstring"""



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
	_snake_case          =						'''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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					Optional[Any]=None     ,  SCREAMING_SNAKE_CASE:					str=None     ,  SCREAMING_SNAKE_CASE:					Optional[Any]=None     ,  SCREAMING_SNAKE_CASE:					Optional[Any]=None     ,  SCREAMING_SNAKE_CASE:					Tuple=None     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]=None     ,  ):






							"""simple docstring"""



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






				'''simple docstring'''



				def __init__(					self	:			int				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			List[Any]=13				,     UpperCAmelCase_	:			Tuple=7				,     UpperCAmelCase_	:			int=True				,     UpperCAmelCase_	:			List[str]=False				,     UpperCAmelCase_	:			Dict=99				,     UpperCAmelCase_	:			Optional[int]=16				,     UpperCAmelCase_	:			Optional[int]=2				,     UpperCAmelCase_	:			Union[str, Any]=4				,     UpperCAmelCase_	:			List[Any]=4				,     UpperCAmelCase_	:			Any="gelu"				,     UpperCAmelCase_	:			Dict=0.1				,     UpperCAmelCase_	:			List[Any]=0.1				,     UpperCAmelCase_	:			List[Any]=32				,     UpperCAmelCase_	:			Tuple=2				,     UpperCAmelCase_	:			Any=1				,     UpperCAmelCase_	:			Any=0				,     UpperCAmelCase_	:			Dict=0.02				,     )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			parent
											_lowerCAmelCase  	=			batch_size
											_lowerCAmelCase  	=			seq_length
											_lowerCAmelCase  	=			is_training
											_lowerCAmelCase  	=			use_labels
											_lowerCAmelCase  	=			vocab_size
											_lowerCAmelCase  	=			hidden_size
											_lowerCAmelCase  	=			num_hidden_layers
											_lowerCAmelCase  	=			num_attention_heads
											_lowerCAmelCase  	=			intermediate_size
											_lowerCAmelCase  	=			hidden_act
											_lowerCAmelCase  	=			hidden_dropout_prob
											_lowerCAmelCase  	=			attention_probs_dropout_prob
											_lowerCAmelCase  	=			max_position_embeddings
											_lowerCAmelCase  	=			eos_token_id
											_lowerCAmelCase  	=			pad_token_id
											_lowerCAmelCase  	=			bos_token_id
											_lowerCAmelCase  	=			initializer_range


				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,     self.vocab_size  )				,     3				,     self.vocab_size  )
											_lowerCAmelCase  	=			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,     dtype=np.intaa  ))				,     -1  )

											_lowerCAmelCase  	=			shift_tokens_right(UpperCAmelCase_				,     1				,     2  )

											_lowerCAmelCase  	=			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=UpperCAmelCase_				,     )
											_lowerCAmelCase  	=			prepare_blenderbot_inputs_dict(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_  )
											return config, inputs_dict


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase  	=			self.prepare_config_and_inputs()
											return config, inputs_dict


				def 				__lowerCamelCase	(					self	:			int				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			Tuple  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			20
											_lowerCAmelCase  	=			model_class_name(UpperCAmelCase_  )

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

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

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

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

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

											_lowerCAmelCase  	=			model.decode(UpperCAmelCase_				,     UpperCAmelCase_  )

											_lowerCAmelCase  	=			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 				__lowerCamelCase	(					self	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			List[str]  )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			20
											_lowerCAmelCase  	=			model_class_name(UpperCAmelCase_  )

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

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

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

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

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

											_lowerCAmelCase  	=			model.decode(UpperCAmelCase_				,     UpperCAmelCase_				,     decoder_attention_mask=UpperCAmelCase_  )

											_lowerCAmelCase  	=			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        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							int							=					9_9


				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			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				,     )

											_lowerCAmelCase  	=			input_ids.shape[0]
											_lowerCAmelCase  	=			BlenderbotConfig(
											    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 				__lowerCamelCase	(					self	:			str  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			self._get_config_and_data()
											_lowerCAmelCase  	=			FlaxBlenderbotForConditionalGeneration(UpperCAmelCase_  )
											_lowerCAmelCase  	=			lm_model(input_ids=UpperCAmelCase_  )
											_lowerCAmelCase  	=			(batch_size, input_ids.shape[1], config.vocab_size)
											self.assertEqual(outputs['logits'].shape				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			BlenderbotConfig(
											    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				,     )
											_lowerCAmelCase  	=			FlaxBlenderbotForConditionalGeneration(UpperCAmelCase_  )
											_lowerCAmelCase  	=			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				,     dtype=np.intaa  )
											_lowerCAmelCase  	=			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				,     dtype=np.intaa  )
											_lowerCAmelCase  	=			lm_model(input_ids=UpperCAmelCase_				,     decoder_input_ids=UpperCAmelCase_  )
											_lowerCAmelCase  	=			(*summary.shape, config.vocab_size)
											self.assertEqual(outputs['logits'].shape				,     UpperCAmelCase_  )







				def 				__lowerCamelCase	(					self	:			int  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				,     dtype=np.intaa  )
											_lowerCAmelCase  	=			shift_tokens_right(UpperCAmelCase_				,     1				,     2  )
											_lowerCAmelCase  	=			np.equal(UpperCAmelCase_				,     1  ).astype(np.floataa  ).sum()
											_lowerCAmelCase  	=			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        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase      ,				UpperCAmelCase							):






				'''simple docstring'''



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


				def 				__lowerCamelCase	(					self	:			Dict  )		->  Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			FlaxBlenderbotModelTester(self  )


				def 				__lowerCamelCase	(					self	:			int  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase  	=			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 				__lowerCamelCase	(					self	:			int  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase  	=			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 				__lowerCamelCase	(					self	:			Optional[int]  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__  ):
																									_lowerCAmelCase  	=			self._prepare_for_class(UpperCAmelCase_				,     UpperCAmelCase_  )
																									_lowerCAmelCase  	=			model_class(UpperCAmelCase_  )

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

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

																									with self.subTest('JIT Disabled'  ):
																																with jax.disable_jit():
																																							_lowerCAmelCase  	=			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 				__lowerCamelCase	(					self	:			Tuple  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs_for_common()

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

																									_lowerCAmelCase  	=			{
																									    '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_	:			Any				,     UpperCAmelCase_	:			int  ):
																																return model.decode(
																																    decoder_input_ids=UpperCAmelCase_				,     decoder_attention_mask=UpperCAmelCase_				,     encoder_outputs=UpperCAmelCase_				,     )

																									with self.subTest('JIT Enabled'  ):
																																_lowerCAmelCase  	=			decode_jitted(**UpperCAmelCase_  ).to_tuple()

																									with self.subTest('JIT Disabled'  ):
																																with jax.disable_jit():
																																							_lowerCAmelCase  	=			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 				__lowerCamelCase	(					self	:			Any  )		->  str:







											"""simple docstring"""




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







				@unittest.skipUnless(jax_device != 'cpu'				,     '3B test too slow on CPU.'  )
				@slow
				def 				__lowerCamelCase	(					self	:			int  )		->  Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			{'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
											_lowerCAmelCase  	=			{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

											_lowerCAmelCase  	=			FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'				,     from_pt=UpperCAmelCase_  )
											_lowerCAmelCase  	=			BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'  )

											_lowerCAmelCase  	=			['Sam']
											_lowerCAmelCase  	=			tokenizer(UpperCAmelCase_				,     return_tensors='jax'  )

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

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








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[0 for i in range(len(SCREAMING_SNAKE_CASE						)						)]

							# initialize interval's left pointer and right pointer
							_lowerCAmelCase	,		_lowerCAmelCase  	=			0, 0

							for i in range(1     ,  len(SCREAMING_SNAKE_CASE						)						):
														# case when current index is inside the interval
														if i <= right_pointer:
																					_lowerCAmelCase  	=			min(right_pointer - i + 1     ,  z_result[i - left_pointer]						)
																					_lowerCAmelCase  	=			min_edge

														while go_next(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
																					z_result[i] += 1

														# if new index's result gives us more right interval,
														# we've to update left_pointer and right_pointer
														if i + z_result[i] - 1 > right_pointer:
																					_lowerCAmelCase	,		_lowerCAmelCase  	=			i, i + z_result[i] - 1

							return z_result






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					list[int]     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							return i + z_result[i] < len(SCREAMING_SNAKE_CASE						) and s[z_result[i]] == s[i + z_result[i]]






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			0
							# concatenate 'pattern' and 'input_str' and call z_function
							# with concatenated string
							_lowerCAmelCase  	=			z_function(pattern + input_str						)

							for val in z_result:
														# if value is greater then length of the pattern string
														# that means this index is starting position of substring
														# which is equal to pattern string
														if val >= len(SCREAMING_SNAKE_CASE						):
																					answer += 1

							return answer


if __name__ == "__main__":
	import doctest

	doctest.testmod()






