



"""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
			__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      '''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_(   lowercase_     :							str						,   lowercase_     :							int						,   lowercase_     :							int=None						,   lowercase_     :							List[Any]=None						,   lowercase_     :							Dict=None						,   lowercase_     :							Union[str, Any]=None						,   lowercase_     :							Any=None						,   lowercase_     :							List[str]=None						,   )	->       Optional[int]:
				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 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=1_3				,       lowerCamelCase__=7				,       lowerCamelCase__=True				,       lowerCamelCase__=False				,       lowerCamelCase__=9_9				,       lowerCamelCase__=1_6				,       lowerCamelCase__=2				,       lowerCamelCase__=4				,       lowerCamelCase__=4				,       lowerCamelCase__="gelu"				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.1				,       lowerCamelCase__=3_2				,       lowerCamelCase__=2				,       lowerCamelCase__=1				,       lowerCamelCase__=0				,       lowerCamelCase__=0.0_2				,       ):
									_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 			snake_case__      (    self   ):
									_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(lowerCamelCase__				,       1				,       2   )

									_lowerCamelCase        =				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__				,       )
									_lowerCamelCase        =				prepare_blenderbot_inputs_dict(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
									return config, inputs_dict

					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.prepare_config_and_inputs()
									return config, inputs_dict

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				2_0
									_lowerCamelCase        =				model_class_name(lowerCamelCase__   )

									_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]				,       lowerCamelCase__				,       lowerCamelCase__   )
									_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]				,       lowerCamelCase__				,       decoder_attention_mask=lowerCamelCase__				,       past_key_values=lowerCamelCase__				,       decoder_position_ids=lowerCamelCase__				,       )

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

									_lowerCamelCase        =				model.decode(lowerCamelCase__				,       lowerCamelCase__   )

									_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 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				2_0
									_lowerCamelCase        =				model_class_name(lowerCamelCase__   )

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

									_lowerCamelCase        =				model.decode(lowerCamelCase__				,       lowerCamelCase__				,       decoder_attention_mask=lowerCamelCase__   )

									_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 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Tuple										= 99

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				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				,       )

									_lowerCamelCase        =				input_ids.shape[0]
									_lowerCamelCase        =				BlenderbotSmallConfig(
									    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 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				self._get_config_and_data()
									_lowerCamelCase        =				FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase__   )
									_lowerCamelCase        =				lm_model(input_ids=lowerCamelCase__   )
									_lowerCamelCase        =				(batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs['''logits'''].shape				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				BlenderbotSmallConfig(
									    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				,       )
									_lowerCamelCase        =				FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase__   )
									_lowerCamelCase        =				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   )
									_lowerCamelCase        =				np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]				,       dtype=np.intaa   )
									_lowerCamelCase        =				lm_model(input_ids=lowerCamelCase__				,       decoder_input_ids=lowerCamelCase__   )
									_lowerCamelCase        =				(*summary.shape, config.vocab_size)
									self.assertEqual(outputs['''logits'''].shape				,       lowerCamelCase__   )





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				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   )
									_lowerCamelCase        =				shift_tokens_right(lowerCamelCase__				,       1				,       2   )
									_lowerCamelCase        =				np.equal(lowerCamelCase__				,       1   ).astype(np.floataa   ).sum()
									_lowerCamelCase        =				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_(   A__,				unittest.TestCase,				A__    ):





					'''simple docstring'''



					lowercase__   :       Optional[int]										= True
					lowercase__   :       Optional[Any]										= (
					    (
					        FlaxBlenderbotSmallModel,
					        FlaxBlenderbotSmallForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					lowercase__   :       str										= (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				FlaxBlenderbotSmallModelTester(self   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				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 			snake_case__      (    self   ):
									_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(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_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(lowerCamelCase__				,       lowerCamelCase__   )
																	_lowerCamelCase        =				model_class(lowerCamelCase__   )

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

																	with self.subTest('''JIT Enabled'''   ):
																					_lowerCamelCase        =				encode_jitted(**lowerCamelCase__   ).to_tuple()

																	with self.subTest('''JIT Disabled'''   ):
																					with jax.disable_jit():
																									_lowerCamelCase        =				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 			snake_case__      (    self   ):
									_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(lowerCamelCase__   )
																	_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(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
																					return model.decode(
																					    decoder_input_ids=lowerCamelCase__				,       decoder_attention_mask=lowerCamelCase__				,       encoder_outputs=lowerCamelCase__				,       )

																	with self.subTest('''JIT Enabled'''   ):
																					_lowerCamelCase        =				decode_jitted(**lowerCamelCase__   ).to_tuple()

																	with self.subTest('''JIT Disabled'''   ):
																					with jax.disable_jit():
																									_lowerCamelCase        =				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 			snake_case__      (    self   ):
									for model_class_name in self.all_model_classes:
													_lowerCamelCase        =				model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''   )
													# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
													_lowerCamelCase        =				np.ones((1, 1)   ) * model.config.eos_token_id
													_lowerCamelCase        =				model(lowerCamelCase__   )
													self.assertIsNotNone(lowerCamelCase__   )





"""simple docstring"""
def        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							str							)	->       bool:
				_lowerCamelCase        =				len(lowercase_							)
				_lowerCamelCase        =				len(lowercase_							)
				_lowerCamelCase        =				[[False for _ in range(m + 1							)] for _ in range(n + 1							)]
				_lowerCamelCase        =				True
				for i in range(lowercase_							):
								for j in range(m + 1							):
												if dp[i][j]:
																if j < m and a[i].upper() == b[j]:
																				_lowerCamelCase        =				True
																if a[i].islower():
																				_lowerCamelCase        =				True
				return dp[n][m]


if __name__ == "__main__":
			import doctest

			doctest.testmod()
