






'''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
			UpperCAmelCase__						:List[str]          =			"""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,						)   ->     Any:


					"""simple docstring"""
					if attention_mask is None:
										__lowerCamelCase						:       Tuple				   =				np.where(input_ids != config.pad_token_id,						1,						0     )
					if decoder_attention_mask is None:
										__lowerCamelCase						:       Union[str, Any]				   =				np.where(decoder_input_ids != config.pad_token_id,						1,						0     )
					if head_mask is None:
										__lowerCamelCase						:       Tuple				   =				np.ones((config.encoder_layers, config.encoder_attention_heads)     )
					if decoder_head_mask is None:
										__lowerCamelCase						:       Dict				   =				np.ones((config.decoder_layers, config.decoder_attention_heads)     )
					if cross_attn_head_mask is None:
										__lowerCamelCase						:       int				   =				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	:




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


												"""simple docstring"""







												__lowerCamelCase						:       Optional[Any]				   =				parent
												__lowerCamelCase						:       Tuple				   =				batch_size
												__lowerCamelCase						:       List[str]				   =				seq_length
												__lowerCamelCase						:       Optional[Any]				   =				is_training
												__lowerCamelCase						:       str				   =				use_labels
												__lowerCamelCase						:       Dict				   =				vocab_size
												__lowerCamelCase						:       List[Any]				   =				hidden_size
												__lowerCamelCase						:       str				   =				num_hidden_layers
												__lowerCamelCase						:       Dict				   =				num_attention_heads
												__lowerCamelCase						:       int				   =				intermediate_size
												__lowerCamelCase						:       Tuple				   =				hidden_act
												__lowerCamelCase						:       int				   =				hidden_dropout_prob
												__lowerCamelCase						:       List[str]				   =				attention_probs_dropout_prob
												__lowerCamelCase						:       Union[str, Any]				   =				max_position_embeddings
												__lowerCamelCase						:       List[Any]				   =				eos_token_id
												__lowerCamelCase						:       Any				   =				pad_token_id
												__lowerCamelCase						:       Optional[int]				   =				bos_token_id
												__lowerCamelCase						:       Any				   =				initializer_range




							def 					a_					(					self      : str					):


												"""simple docstring"""







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

												__lowerCamelCase						:       List[str]				   =				shift_tokens_right(_snake_case	,    1	,    2					)

												__lowerCamelCase						:       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						:       List[str]				   =				prepare_blenderbot_inputs_dict(_snake_case	,    _snake_case	,    _snake_case					)
												return config, inputs_dict




							def 					a_					(					self      : Any					):


												"""simple docstring"""







												__lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				self.prepare_config_and_inputs()
												return config, inputs_dict




							def 					a_					(					self      : Any	,    A__      : Union[str, Any]	,    A__      : int	,    A__      : str					):


												"""simple docstring"""







												__lowerCamelCase						:       Optional[int]				   =				20
												__lowerCamelCase						:       int				   =				model_class_name(_snake_case					)

												__lowerCamelCase						:       Dict				   =				model.encode(inputs_dict["""input_ids"""]					)

												__lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				(
												    inputs_dict["""decoder_input_ids"""],
												    inputs_dict["""decoder_attention_mask"""],
												)

												__lowerCamelCase						:       List[str]				   =				model.init_cache(decoder_input_ids.shape[0]	,    _snake_case	,    _snake_case					)
												__lowerCamelCase						:       Optional[int]				   =				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,    dtype="""i4"""					)

												__lowerCamelCase						:       Optional[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[str]				   =				model.decode(
												    decoder_input_ids[:, :-1]	,    _snake_case	,    decoder_attention_mask=_snake_case	,    past_key_values=_snake_case	,    decoder_position_ids=_snake_case	,    )

												__lowerCamelCase						:       Tuple				   =				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						:       Dict				   =				model.decode(_snake_case	,    _snake_case					)

												__lowerCamelCase						:       Union[str, 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 					a_					(					self      : Any	,    A__      : Optional[int]	,    A__      : List[Any]	,    A__      : List[Any]					):


												"""simple docstring"""







												__lowerCamelCase						:       Optional[int]				   =				20
												__lowerCamelCase						:       Union[str, Any]				   =				model_class_name(_snake_case					)

												__lowerCamelCase						:       Dict				   =				model.encode(inputs_dict["""input_ids"""]					)

												__lowerCamelCase   ,      __lowerCamelCase						:       Union[str, Any]				   =				(
												    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						:       int				   =				model.init_cache(decoder_input_ids.shape[0]	,    _snake_case	,    _snake_case					)
												__lowerCamelCase						:       Optional[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						:       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						:       List[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:]	,    _snake_case	,    past_key_values=outputs_cache.past_key_values	,    decoder_attention_mask=_snake_case	,    decoder_position_ids=_snake_case	,    )

												__lowerCamelCase						:       Tuple				   =				model.decode(_snake_case	,    _snake_case	,    decoder_attention_mask=_snake_case					)

												__lowerCamelCase						:       Optional[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}"					)







@require_flax
class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):
							snake_case__    :     Any   		=			9_9




							def 					a_					(					self      : int					):


												"""simple docstring"""







												__lowerCamelCase						:       int				   =				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						:       int				   =				input_ids.shape[0]
												__lowerCamelCase						:       Tuple				   =				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 					a_					(					self      : Tuple					):


												"""simple docstring"""







												__lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       Dict				   =				self._get_config_and_data()
												__lowerCamelCase						:       List[str]				   =				FlaxBlenderbotSmallForConditionalGeneration(_snake_case					)
												__lowerCamelCase						:       Optional[int]				   =				lm_model(input_ids=_snake_case					)
												__lowerCamelCase						:       Optional[Any]				   =				(batch_size, input_ids.shape[1], config.vocab_size)
												self.assertEqual(outputs["""logits"""].shape	,    _snake_case					)




							def 					a_					(					self      : Optional[Any]					):


												"""simple docstring"""







												__lowerCamelCase						:       str				   =				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						:       Union[str, Any]				   =				FlaxBlenderbotSmallForConditionalGeneration(_snake_case					)
												__lowerCamelCase						:       Union[str, Any]				   =				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]	,    dtype=np.intaa					)
												__lowerCamelCase						:       Tuple				   =				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]	,    dtype=np.intaa					)
												__lowerCamelCase						:       str				   =				lm_model(input_ids=_snake_case	,    decoder_input_ids=_snake_case					)
												__lowerCamelCase						:       Optional[Any]				   =				(*summary.shape, config.vocab_size)
												self.assertEqual(outputs["""logits"""].shape	,    _snake_case					)




							def 					a_					(					self      : Optional[Any]					):


												"""simple docstring"""







												__lowerCamelCase						:       List[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						:       List[Any]				   =				np.equal(_snake_case	,    1					).astype(np.floataa					).sum()
												__lowerCamelCase						:       Union[str, Any]				   =				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 	SCREAMING_SNAKE_CASE	(      UpperCAmelCase_    ,   unittest.TestCase    ,   UpperCAmelCase_  ):
							snake_case__    :     List[str]   		=			True
							snake_case__    :     Union[str, Any]   		=			(
							    (
							        FlaxBlenderbotSmallModel,
							        FlaxBlenderbotSmallForConditionalGeneration,
							    )
							    if is_flax_available()
							    else ()
							)
							snake_case__    :     int   		=			(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




							def 					a_					(					self      : Any					):


												"""simple docstring"""







												__lowerCamelCase						:       Dict				   =				FlaxBlenderbotSmallModelTester(self					)




							def 					a_					(					self      : Union[str, Any]					):


												"""simple docstring"""







												__lowerCamelCase   ,      __lowerCamelCase						:       Dict				   =				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 					a_					(					self      : Optional[int]					):


												"""simple docstring"""







												__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(_snake_case	,    _snake_case	,    _snake_case					)




							def 					a_					(					self      : List[Any]					):


												"""simple docstring"""







												__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						:       int				   =				self._prepare_for_class(_snake_case	,    _snake_case					)
																						__lowerCamelCase						:       Tuple				   =				model_class(_snake_case					)

																						@jax.jit
																						def encode_jitted(A__      : str	,    A__      : int=None	,    **A__      : Union[str, Any]					):
																											return model.encode(input_ids=_snake_case	,    attention_mask=_snake_case					)

																						with self.subTest("""JIT Enabled"""					):
																											__lowerCamelCase						:       str				   =				encode_jitted(**_snake_case					).to_tuple()

																						with self.subTest("""JIT Disabled"""					):
																											with jax.disable_jit():
																																__lowerCamelCase						:       str				   =				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 					a_					(					self      : Tuple					):


												"""simple docstring"""







												__lowerCamelCase   ,      __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						:       Dict				   =				model_class(_snake_case					)
																						__lowerCamelCase						:       Tuple				   =				model.encode(inputs_dict["""input_ids"""]	,    inputs_dict["""attention_mask"""]					)

																						__lowerCamelCase						:       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(A__      : Optional[Any]	,    A__      : Tuple	,    A__      : Tuple					):
																											return model.decode(
																											    decoder_input_ids=_snake_case	,    decoder_attention_mask=_snake_case	,    encoder_outputs=_snake_case	,    )

																						with self.subTest("""JIT Enabled"""					):
																											__lowerCamelCase						:       int				   =				decode_jitted(**_snake_case					).to_tuple()

																						with self.subTest("""JIT Disabled"""					):
																											with jax.disable_jit():
																																__lowerCamelCase						:       int				   =				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 					a_					(					self      : List[str]					):


												"""simple docstring"""







												for model_class_name in self.all_model_classes:
																	__lowerCamelCase						:       Tuple				   =				model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""					)
																	# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																	__lowerCamelCase						:       Dict				   =				np.ones((1, 1)					) * model.config.eos_token_id
																	__lowerCamelCase						:       Optional[Any]				   =				model(_snake_case					)
																	self.assertIsNotNone(_snake_case					)











'''simple docstring'''

def     __lowercase  (_lowercase     )   ->     list:


     """simple docstring"""

     # bit count represents no. of bits in the gray code
     if bit_count < 0:
          raise ValueError("""The given input must be positive"""     )

     # get the generated string sequence
     __lowerCamelCase						:       Optional[Any]				   =				gray_code_sequence_string(_lowercase     )
     #
     # convert them to integers
     for i in range(len(_lowercase     )     ):
          __lowerCamelCase						:       List[str]				   =				int(sequence[i],						2     )

     return sequence
def     __lowercase  (_lowercase     )   ->     list:


     """simple docstring"""

     # The approach is a recursive one
     # Base case achieved when either n = 0 or n=1
     if bit_count == 0:
          return ["0"]

     if bit_count == 1:
          return ["0", "1"]

     __lowerCamelCase						:       str				   =				1 << bit_count  # defines the length of the sequence
     # 1<< n is equivalent to 2^n

     # recursive answer will generate answer for n-1 bits
     __lowerCamelCase						:       Union[str, Any]				   =				gray_code_sequence_string(bit_count - 1     )

     __lowerCamelCase						:       Optional[int]				   =				[]

     # append 0 to first half of the smaller sequence generated
     for i in range(seq_len // 2     ):
          __lowerCamelCase						:       Union[str, Any]				   =				"""0""" + smaller_sequence[i]
          sequence.append(_lowercase     )

     # append 1 to second half ... start from the end of the list
     for i in reversed(range(seq_len // 2     )     ):
          __lowerCamelCase						:       Optional[Any]				   =				"""1""" + smaller_sequence[i]
          sequence.append(_lowercase     )

     return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()



