



'''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__:     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 				snake_case_  (      _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		Dict=None     ,   _lowerCAmelCase						:		Optional[int]=None     ,   _lowerCAmelCase						:		Union[str, Any]=None     ,   _lowerCAmelCase						:		List[str]=None     ,   _lowerCAmelCase						:		Union[str, Any]=None     ,   _lowerCAmelCase						:		Dict=None     ,   )   ->     Optional[Any]:
							if attention_mask is None:
														UpperCAmelCase				: Optional[int]											=	np.where(input_ids != config.pad_token_id     ,   1     ,   0						)
							if decoder_attention_mask is None:
														UpperCAmelCase				: Any											=	np.where(decoder_input_ids != config.pad_token_id     ,   1     ,   0						)
							if head_mask is None:
														UpperCAmelCase				: Any											=	np.ones((config.encoder_layers, config.encoder_attention_heads)						)
							if decoder_head_mask is None:
														UpperCAmelCase				: Any											=	np.ones((config.decoder_layers, config.decoder_attention_heads)						)
							if cross_attn_head_mask is None:
														UpperCAmelCase				: 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:







	"""simple docstring"""






	def __init__(		self    :  str	,    __snake_case    :  Optional[int]	,    __snake_case    :  Any=13	,    __snake_case    :  str=7	,    __snake_case    :  str=True	,    __snake_case    :  Any=False	,    __snake_case    :  List[Any]=99	,    __snake_case    :  int=16	,    __snake_case    :  Optional[int]=2	,    __snake_case    :  List[Any]=4	,    __snake_case    :  List[Any]=4	,    __snake_case    :  int="gelu"	,    __snake_case    :  Tuple=0.1	,    __snake_case    :  List[str]=0.1	,    __snake_case    :  List[Any]=32	,    __snake_case    :  List[Any]=2	,    __snake_case    :  int=1	,    __snake_case    :  int=0	,    __snake_case    :  Any=0.02	,    )    ->	str:
								UpperCAmelCase				: List[str]											=	parent
								UpperCAmelCase				: Optional[int]											=	batch_size
								UpperCAmelCase				: int											=	seq_length
								UpperCAmelCase				: Optional[Any]											=	is_training
								UpperCAmelCase				: Dict											=	use_labels
								UpperCAmelCase				: Dict											=	vocab_size
								UpperCAmelCase				: Optional[int]											=	hidden_size
								UpperCAmelCase				: List[str]											=	num_hidden_layers
								UpperCAmelCase				: Optional[int]											=	num_attention_heads
								UpperCAmelCase				: List[str]											=	intermediate_size
								UpperCAmelCase				: str											=	hidden_act
								UpperCAmelCase				: Tuple											=	hidden_dropout_prob
								UpperCAmelCase				: Tuple											=	attention_probs_dropout_prob
								UpperCAmelCase				: str											=	max_position_embeddings
								UpperCAmelCase				: Optional[int]											=	eos_token_id
								UpperCAmelCase				: List[str]											=	pad_token_id
								UpperCAmelCase				: str											=	bos_token_id
								UpperCAmelCase				: int											=	initializer_range







	def   A			(		self    :  Dict	)    ->	Union[str, Any]:
								UpperCAmelCase				: Tuple											=	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,    self.vocab_size	)	,    3	,    self.vocab_size	)
								UpperCAmelCase				: int											=	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,    dtype=np.intaa	))	,    -1	)

								UpperCAmelCase				: int											=	shift_tokens_right(__snake_case	,    1	,    2	)

								UpperCAmelCase				: List[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=__snake_case	,    )
								UpperCAmelCase				: int											=	prepare_blenderbot_inputs_dict(__snake_case	,    __snake_case	,    __snake_case	)
								return config, inputs_dict







	def   A			(		self    :  str	)    ->	int:
								UpperCAmelCase							,	UpperCAmelCase				: Tuple											=	self.prepare_config_and_inputs()
								return config, inputs_dict







	def   A			(		self    :  Any	,    __snake_case    :  Optional[Any]	,    __snake_case    :  str	,    __snake_case    :  List[Any]	)    ->	Tuple:
								UpperCAmelCase				: List[str]											=	20
								UpperCAmelCase				: Optional[Any]											=	model_class_name(__snake_case	)

								UpperCAmelCase				: int											=	model.encode(inputs_dict['''input_ids''']	)

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

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

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

								UpperCAmelCase				: Tuple											=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,    dtype='''i4'''	)
								UpperCAmelCase				: Optional[Any]											=	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	,    )

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

								UpperCAmelCase				: 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}"""	)







	def   A			(		self    :  str	,    __snake_case    :  int	,    __snake_case    :  Optional[int]	,    __snake_case    :  Dict	)    ->	Union[str, Any]:
								UpperCAmelCase				: Union[str, Any]											=	20
								UpperCAmelCase				: List[str]											=	model_class_name(__snake_case	)

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

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

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

								UpperCAmelCase				: Optional[int]											=	model.init_cache(decoder_input_ids.shape[0]	,    __snake_case	,    __snake_case	)
								UpperCAmelCase				: 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)	,    )

								UpperCAmelCase				: 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	,    )
								UpperCAmelCase				: Tuple											=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,    dtype='''i4'''	)
								UpperCAmelCase				: List[str]											=	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	,    )

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

								UpperCAmelCase				: 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        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= 99







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

								UpperCAmelCase				: int											=	input_ids.shape[0]
								UpperCAmelCase				: List[str]											=	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    :  List[Any]	)    ->	int:
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Dict											=	self._get_config_and_data()
								UpperCAmelCase				: List[str]											=	FlaxBlenderbotSmallForConditionalGeneration(__snake_case	)
								UpperCAmelCase				: Optional[Any]											=	lm_model(input_ids=__snake_case	)
								UpperCAmelCase				: List[str]											=	(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape	,    __snake_case	)







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







	def   A			(		self    :  str	)    ->	List[Any]:
								UpperCAmelCase				: Union[str, Any]											=	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]	,    dtype=np.intaa	)
								UpperCAmelCase				: Dict											=	shift_tokens_right(__snake_case	,    1	,    2	)
								UpperCAmelCase				: str											=	np.equal(__snake_case	,    1	).astype(np.floataa	).sum()
								UpperCAmelCase				: int											=	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(							A__							,	unittest.TestCase							,	A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= True
	lowerCamelCase__    				= (
	    (
	        FlaxBlenderbotSmallModel,
	        FlaxBlenderbotSmallForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	lowerCamelCase__    				= (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()







	def   A			(		self    :  int	)    ->	Union[str, Any]:
								UpperCAmelCase				: List[Any]											=	FlaxBlenderbotSmallModelTester(self	)







	def   A			(		self    :  str	)    ->	Union[str, Any]:
								UpperCAmelCase							,	UpperCAmelCase				: 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]	)    ->	List[Any]:
								UpperCAmelCase							,	UpperCAmelCase				: 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    :  Optional[int]	)    ->	Dict:
								UpperCAmelCase							,	UpperCAmelCase				: int											=	self.model_tester.prepare_config_and_inputs_for_common()

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

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

																						with self.subTest('''JIT Enabled'''	):
																													UpperCAmelCase				: List[str]											=	encode_jitted(**__snake_case	).to_tuple()

																						with self.subTest('''JIT Disabled'''	):
																													with jax.disable_jit():
																																				UpperCAmelCase				: List[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    :  int	)    ->	Any:
								UpperCAmelCase							,	UpperCAmelCase				: 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__	):
																						UpperCAmelCase				: Union[str, Any]											=	model_class(__snake_case	)
																						UpperCAmelCase				: Optional[int]											=	model.encode(inputs_dict['''input_ids''']	,    inputs_dict['''attention_mask''']	)

																						UpperCAmelCase				: 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(__snake_case    :  Tuple	,    __snake_case    :  List[str]	,    __snake_case    :  int	):
																													return model.decode(
																													    decoder_input_ids=__snake_case	,    decoder_attention_mask=__snake_case	,    encoder_outputs=__snake_case	,    )

																						with self.subTest('''JIT Enabled'''	):
																													UpperCAmelCase				: Union[str, Any]											=	decode_jitted(**__snake_case	).to_tuple()

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










'''simple docstring'''






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict


@pytest.mark.parametrize(
    '''split_dict'''     ,   [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''     ,   num_bytes=1337     ,   num_examples=42     ,   dataset_name='''my_dataset'''						)}						),
        SplitDict({'''train''': SplitInfo(name='''train'''     ,   num_bytes=1337     ,   num_examples=42						)}						),
        SplitDict({'''train''': SplitInfo()}						),
    ]     ,   )
def 				snake_case_  (      _lowerCAmelCase						:		SplitDict						)   ->     List[Any]:
							UpperCAmelCase				: str											=	split_dict._to_yaml_list()
							assert len(_lowerCAmelCase						) == len(_lowerCAmelCase						)
							UpperCAmelCase				: Optional[int]											=	SplitDict._from_yaml_list(_lowerCAmelCase						)
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														UpperCAmelCase				: Optional[Any]											=	None
														# the split name of split_dict takes over the name of the split info object
														UpperCAmelCase				: Dict											=	split_name
							assert split_dict == reloaded




@pytest.mark.parametrize(
    '''split_info'''     ,   [SplitInfo(), SplitInfo(dataset_name=_lowerCAmelCase						), SplitInfo(dataset_name='''my_dataset'''						)]						)
def 				snake_case_  (      _lowerCAmelCase						:		List[Any]						)   ->     Optional[Any]:
							# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
							# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
							UpperCAmelCase				: Any											=	asdict(SplitDict({'''train''': split_info}						)						)
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name





