
'''simple docstring'''


def lowercase_	(   _lowercase				)     ->							bool:
					'''simple docstring'''

					lowerCamelCase_					:      str     =		0
					for ch in input_str:
										lowerCamelCase_					:      Dict     =		ord(_lowercase				)
										lowerCamelCase_					:      Any     =		pow(2			,       _lowercase				)

										# If we already turned on bit for current character's unicode
										if bitmap >> ch_unicode & 1 == 1:
															return False
										bitmap |= ch_bit_index_on
					return True


if __name__ == "__main__":
					import doctest

					doctest.testmod()




'''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
					__lowercase							:   Union[str, 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 lowercase_	(   _lowercase			,       _lowercase			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None			,       )     ->							Tuple:
					'''simple docstring'''

					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_					:      List[Any]     =		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_					:      Tuple     =		np.ones((config.decoder_layers, config.decoder_attention_heads)				)
					if cross_attn_head_mask is None:
										lowerCamelCase_					:      Tuple     =		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 	__lowercase				:

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

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[int]     =		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,   self.vocab_size      )			,   3			,   self.vocab_size      )
								lowerCamelCase_					:      List[Any]     =		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,   dtype=np.intaa      ))			,   -1      )

								lowerCamelCase_					:      Any     =		shift_tokens_right(A			,   1			,   2      )

								lowerCamelCase_					:      Any     =		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=A			,   )
								lowerCamelCase_					:      List[str]     =		prepare_blenderbot_inputs_dict(A			,   A			,   A      )
								return config, inputs_dict

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_,      lowerCamelCase_					:      Dict     =		self.prepare_config_and_inputs()
								return config, inputs_dict

			def        UpperCAmelCase__ (self			,   A			,   A			,   A      ):
								lowerCamelCase_					:      Any     =		2_0
								lowerCamelCase_					:      Dict     =		model_class_name(A      )

								lowerCamelCase_					:      List[str]     =		model.encode(inputs_dict['''input_ids''']      )

								lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		(
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

								lowerCamelCase_					:      Any     =		model.init_cache(decoder_input_ids.shape[0]			,   A			,   A      )
								lowerCamelCase_					:      Optional[Any]     =		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,   dtype='''i4'''      )

								lowerCamelCase_					:      Tuple     =		jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]			,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,   )
								lowerCamelCase_					:      List[Any]     =		model.decode(
								    decoder_input_ids[:, :-1]			,   A			,   decoder_attention_mask=A			,   past_key_values=A			,   decoder_position_ids=A			,   )

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

								lowerCamelCase_					:      str     =		model.decode(A			,   A      )

								lowerCamelCase_					:      List[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			,   A			,   A			,   A      ):
								lowerCamelCase_					:      Tuple     =		2_0
								lowerCamelCase_					:      Dict     =		model_class_name(A      )

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

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

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

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

								lowerCamelCase_					:      Union[str, Any]     =		model.decode(A			,   A			,   decoder_attention_mask=A      )

								lowerCamelCase_					:      Optional[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 	__lowercase				(  unittest.TestCase    ):
			lowerCamelCase			:		Optional[int]	      =			99

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Union[str, 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, 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_					:      Optional[int]     =		input_ids.shape[0]
								lowerCamelCase_					:      int     =		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        UpperCAmelCase__ (self      ):
								lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      Any     =		self._get_config_and_data()
								lowerCamelCase_					:      List[str]     =		FlaxBlenderbotSmallForConditionalGeneration(A      )
								lowerCamelCase_					:      Optional[Any]     =		lm_model(input_ids=A      )
								lowerCamelCase_					:      List[Any]     =		(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape			,   A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		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_					:      Union[str, Any]     =		FlaxBlenderbotSmallForConditionalGeneration(A      )
								lowerCamelCase_					:      List[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      )
								lowerCamelCase_					:      str     =		np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]			,   dtype=np.intaa      )
								lowerCamelCase_					:      Optional[int]     =		lm_model(input_ids=A			,   decoder_input_ids=A      )
								lowerCamelCase_					:      List[str]     =		(*summary.shape, config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape			,   A      )






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		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_					:      Dict     =		shift_tokens_right(A			,   1			,   2      )
								lowerCamelCase_					:      Union[str, Any]     =		np.equal(A			,   1      ).astype(np.floataa      ).sum()
								lowerCamelCase_					:      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 	__lowercase				(  _lowercase      ,     unittest.TestCase      ,     _lowercase    ):
			lowerCamelCase			:		Dict	      =			True
			lowerCamelCase			:		str	      =			(
			    (
			        FlaxBlenderbotSmallModel,
			        FlaxBlenderbotSmallForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			lowerCamelCase			:		List[Any]	      =			(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[Any]     =		FlaxBlenderbotSmallModelTester(self      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		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      ):
								lowerCamelCase_,      lowerCamelCase_					:      Optional[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(A			,   A			,   A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__      ):
																		lowerCamelCase_					:      Optional[int]     =		self._prepare_for_class(A			,   A      )
																		lowerCamelCase_					:      Any     =		model_class(A      )

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

																		with self.subTest('''JIT Enabled'''      ):
																							lowerCamelCase_					:      int     =		encode_jitted(**A      ).to_tuple()

																		with self.subTest('''JIT Disabled'''      ):
																							with jax.disable_jit():
																												lowerCamelCase_					:      Union[str, Any]     =		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      ):
								lowerCamelCase_,      lowerCamelCase_					:      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_					:      Any     =		model_class(A      )
																		lowerCamelCase_					:      Union[str, Any]     =		model.encode(inputs_dict['''input_ids''']			,   inputs_dict['''attention_mask''']      )

																		lowerCamelCase_					:      Union[str, 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(A			,   A			,   A      ):
																							return model.decode(
																							    decoder_input_ids=A			,   decoder_attention_mask=A			,   encoder_outputs=A			,   )

																		with self.subTest('''JIT Enabled'''      ):
																							lowerCamelCase_					:      Tuple     =		decode_jitted(**A      ).to_tuple()

																		with self.subTest('''JIT Disabled'''      ):
																							with jax.disable_jit():
																												lowerCamelCase_					:      Optional[Any]     =		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      ):
								for model_class_name in self.all_model_classes:
													lowerCamelCase_					:      Union[str, Any]     =		model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''      )
													# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
													lowerCamelCase_					:      Optional[Any]     =		np.ones((1, 1)      ) * model.config.eos_token_id
													lowerCamelCase_					:      Optional[int]     =		model(A      )
													self.assertIsNotNone(A      )


