



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, 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     :    str      						=					"""platform"""

							import jax
							import jax.numpy as jnp

							from transformers import BlenderbotTokenizer
							from transformers.models.blenderbot.modeling_flax_blenderbot import (
							    FlaxBlenderbotForConditionalGeneration,
							    FlaxBlenderbotModel,
							    shift_tokens_right,
							)



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None			,       )   ->    str:
							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  :	Optional[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  :	List[str]							  =	np.ones((config.decoder_layers, config.decoder_attention_heads)	)
							if cross_attn_head_mask is None:
														UpperCAmelCase  :	Optional[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     UpperCamelCase_				:





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





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

								UpperCAmelCase  :	List[Any]							  =	shift_tokens_right(A	,	1	,	2					)

								UpperCAmelCase  :	Dict							  =	BlenderbotConfig(
								    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	,	)
								UpperCAmelCase  :	int							  =	prepare_blenderbot_inputs_dict(A	,	A	,	A					)
								return config, inputs_dict





	def 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	Optional[Any]							  =	self.prepare_config_and_inputs()
								return config, inputs_dict





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    List[str]:
								UpperCAmelCase  :	str							  =	20
								UpperCAmelCase  :	List[str]							  =	model_class_name(A					)

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

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

								UpperCAmelCase  :	Dict							  =	model.init_cache(decoder_input_ids.shape[0]	,	A	,	A					)
								UpperCAmelCase  :	List[str]							  =	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,	dtype="""i4"""					)

								UpperCAmelCase  :	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  :	int							  =	model.decode(
								    decoder_input_ids[:, :-1]	,	A	,	decoder_attention_mask=A	,	past_key_values=A	,	decoder_position_ids=A	,	)

								UpperCAmelCase  :	Any							  =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,	dtype="""i4"""					)
								UpperCAmelCase  :	Tuple							  =	model.decode(
								    decoder_input_ids[:, -1:]	,	A	,	decoder_attention_mask=A	,	past_key_values=outputs_cache.past_key_values	,	decoder_position_ids=A	,	)

								UpperCAmelCase  :	Optional[Any]							  =	model.decode(A	,	A					)

								UpperCAmelCase  :	List[str]							  =	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 				_lowercase(						self	,	A	,	A	,	A					)    ->    int:
								UpperCAmelCase  :	Optional[int]							  =	20
								UpperCAmelCase  :	Any							  =	model_class_name(A					)

								UpperCAmelCase  :	Tuple							  =	model.encode(inputs_dict["""input_ids"""]					)

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

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

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

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

								UpperCAmelCase  :	List[str]							  =	model.decode(A	,	A	,	decoder_attention_mask=A					)

								UpperCAmelCase  :	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}'''					)




@require_flax
class     UpperCamelCase_				(							unittest.TestCase    ):
	lowercase    =				99





	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	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	,	)

								UpperCAmelCase  :	Tuple							  =	input_ids.shape[0]
								UpperCAmelCase  :	Any							  =	BlenderbotConfig(
								    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 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	int							  =	self._get_config_and_data()
								UpperCAmelCase  :	List[str]							  =	FlaxBlenderbotForConditionalGeneration(A					)
								UpperCAmelCase  :	Optional[Any]							  =	lm_model(input_ids=A					)
								UpperCAmelCase  :	List[Any]							  =	(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs["""logits"""].shape	,	A					)





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






	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	List[str]							  =	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]	,	dtype=np.intaa					)
								UpperCAmelCase  :	Any							  =	shift_tokens_right(A	,	1	,	2					)
								UpperCAmelCase  :	str							  =	np.equal(A	,	1					).astype(np.floataa					).sum()
								UpperCAmelCase  :	Any							  =	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     UpperCamelCase_				(							__magic_name__   ,  unittest.TestCase   ,  __magic_name__    ):
	lowercase    =				True
	lowercase    =				(
	    (
	        FlaxBlenderbotModel,
	        FlaxBlenderbotForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	lowercase    =				(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()





	def 				_lowercase(						self					)    ->    str:
								UpperCAmelCase  :	List[str]							  =	FlaxBlenderbotModelTester(self					)





	def 				_lowercase(						self					)    ->    Optional[int]:
								UpperCAmelCase  :	Union[str, Any]							  =	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 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Union[str, 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 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Optional[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  :	Any							  =	self._prepare_for_class(A	,	A					)
																						UpperCAmelCase  :	List[str]							  =	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"""					):
																													UpperCAmelCase  :	Optional[int]							  =	encode_jitted(**A					).to_tuple()

																						with self.subTest("""JIT Disabled"""					):
																													with jax.disable_jit():
																																				UpperCAmelCase  :	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 				_lowercase(						self					)    ->    Optional[int]:
								UpperCAmelCase  :	Optional[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  :	Dict							  =	model_class(A					)
																						UpperCAmelCase  :	Optional[int]							  =	model.encode(inputs_dict["""input_ids"""]	,	inputs_dict["""attention_mask"""]					)

																						UpperCAmelCase  :	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(A	,	A	,	A					):
																													return model.decode(
																													    decoder_input_ids=A	,	decoder_attention_mask=A	,	encoder_outputs=A	,	)

																						with self.subTest("""JIT Enabled"""					):
																													UpperCAmelCase  :	str							  =	decode_jitted(**A					).to_tuple()

																						with self.subTest("""JIT Disabled"""					):
																													with jax.disable_jit():
																																				UpperCAmelCase  :	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 				_lowercase(						self					)    ->    Optional[Any]:
								for model_class_name in self.all_model_classes:
															UpperCAmelCase  :	List[str]							  =	model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""					)
															# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
															UpperCAmelCase  :	Union[str, Any]							  =	np.ones((1, 1)					) * model.config.eos_token_id
															UpperCAmelCase  :	Tuple							  =	model(A					)
															self.assertIsNotNone(A					)






	@unittest.skipUnless(jax_device != """cpu"""	,	"""3B test too slow on CPU."""					)
	@slow
	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	Optional[Any]							  =	{"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
								UpperCAmelCase  :	Tuple							  =	{"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

								UpperCAmelCase  :	int							  =	FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""	,	from_pt=A					)
								UpperCAmelCase  :	Any							  =	BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""					)

								UpperCAmelCase  :	Union[str, Any]							  =	["""Sam"""]
								UpperCAmelCase  :	Any							  =	tokenizer(A	,	return_tensors="""jax"""					)

								UpperCAmelCase  :	Optional[Any]							  =	model.generate(**A	,	**A					)
								UpperCAmelCase  :	Tuple							  =	"""Sam is a great name. It means \"sun\" in Gaelic."""

								UpperCAmelCase  :	List[Any]							  =	tokenizer.batch_decode(A	,	**A					)
								assert generated_txt[0].strip() == tgt_text






'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


a     :    str      						=					logging.get_logger(__name__)

a     :    Any      						=					{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'fnet'





	def __init__(						self	,	A=32000	,	A=768	,	A=12	,	A=3072	,	A="gelu_new"	,	A=0.1	,	A=512	,	A=4	,	A=0.0_2	,	A=1e-12	,	A=False	,	A=512	,	A=3	,	A=1	,	A=2	,	**A	,	)    ->    int:
								super().__init__(pad_token_id=A	,	bos_token_id=A	,	eos_token_id=A	,	**A					)

								UpperCAmelCase  :	Optional[Any]							  =	vocab_size
								UpperCAmelCase  :	Dict							  =	max_position_embeddings
								UpperCAmelCase  :	List[str]							  =	hidden_size
								UpperCAmelCase  :	int							  =	num_hidden_layers
								UpperCAmelCase  :	Any							  =	intermediate_size
								UpperCAmelCase  :	List[str]							  =	hidden_act
								UpperCAmelCase  :	Tuple							  =	hidden_dropout_prob
								UpperCAmelCase  :	List[str]							  =	initializer_range
								UpperCAmelCase  :	List[Any]							  =	type_vocab_size
								UpperCAmelCase  :	int							  =	layer_norm_eps
								UpperCAmelCase  :	Optional[Any]							  =	use_tpu_fourier_optimizations
								UpperCAmelCase  :	List[Any]							  =	tpu_short_seq_length

