






'''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
		__lowerCAmelCase									=   'platform'

		import jax
		import jax.numpy as jnp

		from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
		    FlaxBlenderbotSmallForConditionalGeneration,
		    FlaxBlenderbotSmallModel,
		    shift_tokens_right,
		)





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






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






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

							a_		:	Any									=   shift_tokens_right(__SCREAMING_SNAKE_CASE	,    1	,    2       )

							a_		:	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=__SCREAMING_SNAKE_CASE	,    )
							a_		:	Optional[int]									=   prepare_blenderbot_inputs_dict(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							return config, inputs_dict






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      int:
							a_					,      a_		:	int									=   self.prepare_config_and_inputs()
							return config, inputs_dict






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      Dict       )      ->      List[Any]:
							a_		:	int									=   20
							a_		:	Union[str, Any]									=   model_class_name(__SCREAMING_SNAKE_CASE       )

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

							a_					,      a_		:	str									=   (
							    inputs_dict['''decoder_input_ids'''],
							    inputs_dict['''decoder_attention_mask'''],
							)

							a_		:	int									=   model.init_cache(decoder_input_ids.shape[0]	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							a_		:	Dict									=   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,    dtype='''i4'''       )

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

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

							a_		:	Tuple									=   model.decode(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )

							a_		:	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       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      Dict       )      ->      Dict:
							a_		:	Any									=   20
							a_		:	Dict									=   model_class_name(__SCREAMING_SNAKE_CASE       )

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

							a_					,      a_		:	str									=   (
							    inputs_dict['''decoder_input_ids'''],
							    inputs_dict['''decoder_attention_mask'''],
							)

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

							a_		:	str									=   model.init_cache(decoder_input_ids.shape[0]	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							a_		:	str									=   jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]	,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,    )

							a_		:	List[Any]									=   model.decode(
							    decoder_input_ids[:, :-1]	,    __SCREAMING_SNAKE_CASE	,    decoder_attention_mask=__SCREAMING_SNAKE_CASE	,    past_key_values=__SCREAMING_SNAKE_CASE	,    decoder_position_ids=__SCREAMING_SNAKE_CASE	,    )
							a_		:	Tuple									=   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,    dtype='''i4'''       )
							a_		:	Any									=   model.decode(
							    decoder_input_ids[:, -1:]	,    __SCREAMING_SNAKE_CASE	,    past_key_values=outputs_cache.past_key_values	,    decoder_attention_mask=__SCREAMING_SNAKE_CASE	,    decoder_position_ids=__SCREAMING_SNAKE_CASE	,    )

							a_		:	Optional[Any]									=   model.decode(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    decoder_attention_mask=__SCREAMING_SNAKE_CASE       )

							a_		:	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							):
			snake_case__	=					99






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Tuple:
							a_		:	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	,    )

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






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







			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      Union[str, Any]:
							a_		:	Tuple									=   np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]	,    dtype=np.intaa       )
							a_		:	int									=   shift_tokens_right(__SCREAMING_SNAKE_CASE	,    1	,    2       )
							a_		:	str									=   np.equal(__SCREAMING_SNAKE_CASE	,    1       ).astype(np.floataa       ).sum()
							a_		:	Optional[Any]									=   np.equal(__SCREAMING_SNAKE_CASE	,    1       ).astype(np.floataa       ).sum()
							self.assertEqual(shifted.shape	,    input_ids.shape       )
							self.assertEqual(__SCREAMING_SNAKE_CASE	,    n_pad_before - 1       )
							self.assertTrue(np.equal(shifted[:, 0]	,    2       ).all()       )





@require_flax
class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_						, unittest.TestCase						, SCREAMING_SNAKE_CASE_							):
			snake_case__	=					True
			snake_case__	=					(
			    (
			        FlaxBlenderbotSmallModel,
			        FlaxBlenderbotSmallForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			snake_case__	=					(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	List[Any]									=   FlaxBlenderbotSmallModelTester(self       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      str:
							a_					,      a_		:	List[Any]									=   self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Tuple:
							a_					,      a_		:	List[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(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      Dict:
							a_					,      a_		:	Dict									=   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__       ):
															a_		:	str									=   self._prepare_for_class(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
															a_		:	Any									=   model_class(__SCREAMING_SNAKE_CASE       )

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

															with self.subTest('''JIT Enabled'''       ):
																			a_		:	Dict									=   encode_jitted(**__SCREAMING_SNAKE_CASE       ).to_tuple()

															with self.subTest('''JIT Disabled'''       ):
																			with jax.disable_jit():
																							a_		:	Tuple									=   encode_jitted(**__SCREAMING_SNAKE_CASE       ).to_tuple()

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






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      int:
							a_					,      a_		:	int									=   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__       ):
															a_		:	int									=   model_class(__SCREAMING_SNAKE_CASE       )
															a_		:	Union[str, Any]									=   model.encode(inputs_dict['''input_ids''']	,    inputs_dict['''attention_mask''']       )

															a_		:	List[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(__SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Optional[int]       ):
																			return model.decode(
																			    decoder_input_ids=__SCREAMING_SNAKE_CASE	,    decoder_attention_mask=__SCREAMING_SNAKE_CASE	,    encoder_outputs=__SCREAMING_SNAKE_CASE	,    )

															with self.subTest('''JIT Enabled'''       ):
																			a_		:	int									=   decode_jitted(**__SCREAMING_SNAKE_CASE       ).to_tuple()

															with self.subTest('''JIT Disabled'''       ):
																			with jax.disable_jit():
																							a_		:	int									=   decode_jitted(**__SCREAMING_SNAKE_CASE       ).to_tuple()

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







			@slow
			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      int:
							for model_class_name in self.all_model_classes:
											a_		:	Tuple									=   model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''       )
											# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
											a_		:	Optional[int]									=   np.ones((1, 1)       ) * model.config.eos_token_id
											a_		:	Optional[int]									=   model(__SCREAMING_SNAKE_CASE       )
											self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )












'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase									=   {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'SEWForCTC',
		    'SEWForSequenceClassification',
		    'SEWModel',
		    'SEWPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew import (
				    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWForCTC,
				    SEWForSequenceClassification,
				    SEWModel,
				    SEWPreTrainedModel,
				)


else:
		import sys

		__lowerCAmelCase									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




