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







							def __init__(				self     :    Optional[Any]	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    List[Any]=13	,					__snake_case     :    Any=7	,					__snake_case     :    List[str]=True	,					__snake_case     :    Any=False	,					__snake_case     :    Optional[int]=99	,					__snake_case     :    List[Any]=16	,					__snake_case     :    str=2	,					__snake_case     :    List[Any]=4	,					__snake_case     :    List[Any]=4	,					__snake_case     :    Tuple="gelu"	,					__snake_case     :    str=0.1	,					__snake_case     :    str=0.1	,					__snake_case     :    Optional[Any]=32	,					__snake_case     :    int=2	,					__snake_case     :    Optional[int]=1	,					__snake_case     :    str=0	,					__snake_case     :    str=0.0_2	,					):
										lowerCamelCase						:Optional[Any]						  =     parent
										lowerCamelCase						:Any						  =     batch_size
										lowerCamelCase						:str						  =     seq_length
										lowerCamelCase						:Optional[Any]						  =     is_training
										lowerCamelCase						:Tuple						  =     use_labels
										lowerCamelCase						:List[str]						  =     vocab_size
										lowerCamelCase						:Any						  =     hidden_size
										lowerCamelCase						:str						  =     num_hidden_layers
										lowerCamelCase						:Any						  =     num_attention_heads
										lowerCamelCase						:Tuple						  =     intermediate_size
										lowerCamelCase						:List[Any]						  =     hidden_act
										lowerCamelCase						:Tuple						  =     hidden_dropout_prob
										lowerCamelCase						:Dict						  =     attention_probs_dropout_prob
										lowerCamelCase						:List[Any]						  =     max_position_embeddings
										lowerCamelCase						:List[str]						  =     eos_token_id
										lowerCamelCase						:Tuple						  =     pad_token_id
										lowerCamelCase						:Tuple						  =     bos_token_id
										lowerCamelCase						:int						  =     initializer_range







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

										lowerCamelCase						:List[Any]						  =     shift_tokens_right(__snake_case	,					1	,					2		)

										lowerCamelCase						:Optional[int]						  =     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=__snake_case	,					)
										lowerCamelCase						:Dict						  =     prepare_blenderbot_inputs_dict(__snake_case	,					__snake_case	,					__snake_case		)
										return config, inputs_dict







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Optional[int]						  =     self.prepare_config_and_inputs()
										return config, inputs_dict







							def 				snake_case  (				self     :    List[str]	,					__snake_case     :    Tuple	,					__snake_case     :    Optional[int]	,					__snake_case     :    Union[str, Any]		):
										lowerCamelCase						:Tuple						  =     20
										lowerCamelCase						:Union[str, Any]						  =     model_class_name(__snake_case		)

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

										lowerCamelCase						:Dict						  =     (
										    inputs_dict['''decoder_input_ids'''],
										    inputs_dict['''decoder_attention_mask'''],
										)

										lowerCamelCase						:Optional[Any]						  =     model.init_cache(decoder_input_ids.shape[0]	,					__snake_case	,					__snake_case		)
										lowerCamelCase						:Union[str, Any]						  =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,					dtype='''i4'''		)

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

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

										lowerCamelCase						:Optional[int]						  =     model.decode(__snake_case	,					__snake_case		)

										lowerCamelCase						:Tuple						  =     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     :    Optional[int]	,					__snake_case     :    List[str]	,					__snake_case     :    List[Any]	,					__snake_case     :    Tuple		):
										lowerCamelCase						:List[str]						  =     20
										lowerCamelCase						:Union[str, Any]						  =     model_class_name(__snake_case		)

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

										lowerCamelCase						:Optional[int]						  =     (
										    inputs_dict['''decoder_input_ids'''],
										    inputs_dict['''decoder_attention_mask'''],
										)

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

										lowerCamelCase						:Optional[int]						  =     model.init_cache(decoder_input_ids.shape[0]	,					__snake_case	,					__snake_case		)
										lowerCamelCase						:Any						  =     jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]	,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,					)

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

										lowerCamelCase						:str						  =     model.decode(__snake_case	,					__snake_case	,					decoder_attention_mask=__snake_case		)

										lowerCamelCase						: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}"		)


@require_flax
class   _lowerCAmelCase		(     unittest.TestCase				):
							_UpperCAmelCase						    =     9_9







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Dict						  =     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						:Union[str, Any]						  =     input_ids.shape[0]
										lowerCamelCase						:Any						  =     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 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:List[Any]						  =     self._get_config_and_data()
										lowerCamelCase						:str						  =     FlaxBlenderbotSmallForConditionalGeneration(__snake_case		)
										lowerCamelCase						:Any						  =     lm_model(input_ids=__snake_case		)
										lowerCamelCase						:Union[str, Any]						  =     (batch_size, input_ids.shape[1], config.vocab_size)
										self.assertEqual(outputs['''logits'''].shape	,					__snake_case		)







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







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


@require_flax
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   unittest.TestCase      ,   __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     True
							_UpperCAmelCase						    =     (
							    (
							        FlaxBlenderbotSmallModel,
							        FlaxBlenderbotSmallForConditionalGeneration,
							    )
							    if is_flax_available()
							    else ()
							)
							_UpperCAmelCase						    =     (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:Optional[int]						  =     FlaxBlenderbotSmallModelTester(self		)







							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Any						  =     self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(__snake_case	,					__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Any						  =     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(__snake_case	,					__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:List[Any]						  =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__		):
																lowerCamelCase						:Tuple						  =     self._prepare_for_class(__snake_case	,					__snake_case		)
																lowerCamelCase						:Optional[int]						  =     model_class(__snake_case		)

																@jax.jit
																def encode_jitted(__snake_case     :    Tuple	,					__snake_case     :    Optional[int]=None	,					**__snake_case     :    Optional[Any]		):
																			return model.encode(input_ids=__snake_case	,					attention_mask=__snake_case		)

																with self.subTest('''JIT Enabled'''		):
																			lowerCamelCase						:Optional[int]						  =     encode_jitted(**__snake_case		).to_tuple()

																with self.subTest('''JIT Disabled'''		):
																			with jax.disable_jit():
																						lowerCamelCase						:Any						  =     encode_jitted(**__snake_case		).to_tuple()

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







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						: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__		):
																lowerCamelCase						:Tuple						  =     model_class(__snake_case		)
																lowerCamelCase						:Dict						  =     model.encode(inputs_dict['''input_ids''']	,					inputs_dict['''attention_mask''']		)

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

																with self.subTest('''JIT Enabled'''		):
																			lowerCamelCase						:Tuple						  =     decode_jitted(**__snake_case		).to_tuple()

																with self.subTest('''JIT Disabled'''		):
																			with jax.disable_jit():
																						lowerCamelCase						:Optional[Any]						  =     decode_jitted(**__snake_case		).to_tuple()

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







							@slow
							def 				snake_case  (				self     :    List[str]		):
										for model_class_name in self.all_model_classes:
													lowerCamelCase						:Optional[Any]						  =     model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''		)
													# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
													lowerCamelCase						:Optional[int]						  =     np.ones((1, 1)		) * model.config.eos_token_id
													lowerCamelCase						:Union[str, Any]						  =     model(__snake_case		)
													self.assertIsNotNone(__snake_case		)




def  _lowerCamelCase					(   a_				: str						,			a_				: str):
			lowerCamelCase						:List[str]						  =     len(a_)
			lowerCamelCase						:List[str]						  =     len(a_)
			lowerCamelCase						:int						  =     [[False for _ in range(m + 1)] for _ in range(n + 1)]
			lowerCamelCase						:Optional[Any]						  =     True
			for i in range(a_):
						for j in range(m + 1):
									if dp[i][j]:
												if j < m and a[i].upper() == b[j]:
															lowerCamelCase						:Any						  =     True
												if a[i].islower():
															lowerCamelCase						:List[str]						  =     True
			return dp[n][m]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



