



"""simple docstring"""







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    =	'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__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      ) ->							int:



							if attention_mask is None:
														A         =					np.where(input_ids != config.pad_token_id				,      1				,      0			)
							if decoder_attention_mask is None:
														A         =					np.where(decoder_input_ids != config.pad_token_id				,      1				,      0			)
							if head_mask is None:
														A         =					np.ones((config.encoder_layers, config.encoder_attention_heads)			)
							if decoder_head_mask is None:
														A         =					np.ones((config.decoder_layers, config.decoder_attention_heads)			)
							if cross_attn_head_mask is None:
														A         =					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  :    Optional[Any]      ,							snake_case  :    str      ,							snake_case  :    Optional[Any]=13      ,							snake_case  :    Dict=7      ,							snake_case  :    Any=True      ,							snake_case  :    Dict=False      ,							snake_case  :    Tuple=99      ,							snake_case  :    str=16      ,							snake_case  :    Union[str, Any]=2      ,							snake_case  :    List[Any]=4      ,							snake_case  :    Dict=4      ,							snake_case  :    Union[str, Any]="gelu"      ,							snake_case  :    Tuple=0.1      ,							snake_case  :    List[str]=0.1      ,							snake_case  :    List[str]=32      ,							snake_case  :    Dict=2      ,							snake_case  :    Optional[int]=1      ,							snake_case  :    Tuple=0      ,							snake_case  :    List[Any]=0.02      ,							)			->						Union[str, Any]:






									'''simple docstring'''

									A         =					parent
									A         =					batch_size
									A         =					seq_length
									A         =					is_training
									A         =					use_labels
									A         =					vocab_size
									A         =					hidden_size
									A         =					num_hidden_layers
									A         =					num_attention_heads
									A         =					intermediate_size
									A         =					hidden_act
									A         =					hidden_dropout_prob
									A         =					attention_probs_dropout_prob
									A         =					max_position_embeddings
									A         =					eos_token_id
									A         =					pad_token_id
									A         =					bos_token_id
									A         =					initializer_range



		def 					A_		( self  :    str				)			->						List[Any]:






									'''simple docstring'''

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

									A         =					shift_tokens_right(lowercase__      ,							1      ,							2				)

									A         =					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=lowercase__      ,							)
									A         =					prepare_blenderbot_inputs_dict(lowercase__      ,							lowercase__      ,							lowercase__				)
									return config, inputs_dict



		def 					A_		( self  :    Optional[Any]				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					self.prepare_config_and_inputs()
									return config, inputs_dict



		def 					A_		( self  :    str      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    str      ,							snake_case  :    Dict				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					20
									A         =					model_class_name(lowercase__				)

									A         =					model.encode(inputs_dict['input_ids']				)

									A         =					(
									    inputs_dict['''decoder_input_ids'''],
									    inputs_dict['''decoder_attention_mask'''],
									)

									A         =					model.init_cache(decoder_input_ids.shape[0]      ,							lowercase__      ,							lowercase__				)
									A         =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,							dtype='i4'				)

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

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

									A         =					model.decode(lowercase__      ,							lowercase__				)

									A         =					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  :    Union[str, Any]      ,							snake_case  :    int      ,							snake_case  :    Optional[int]      ,							snake_case  :    Optional[int]				)			->						Tuple:






									'''simple docstring'''

									A         =					20
									A         =					model_class_name(lowercase__				)

									A         =					model.encode(inputs_dict['input_ids']				)

									A         =					(
									    inputs_dict['''decoder_input_ids'''],
									    inputs_dict['''decoder_attention_mask'''],
									)

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

									A         =					model.init_cache(decoder_input_ids.shape[0]      ,							lowercase__      ,							lowercase__				)
									A         =					jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]      ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,							)

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

									A         =					model.decode(lowercase__      ,							lowercase__      ,							decoder_attention_mask=lowercase__				)

									A         =					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     ):
		lowerCAmelCase_       :	Optional[Any] 							=     99



		def 					A_		( self  :    int				)			->						List[Any]:






									'''simple docstring'''

									A         =					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         =					input_ids.shape[0]
									A         =					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 					A_		( self  :    int				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					self._get_config_and_data()
									A         =					FlaxBlenderbotForConditionalGeneration(lowercase__				)
									A         =					lm_model(input_ids=lowercase__				)
									A         =					(batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs['logits'].shape      ,							lowercase__				)



		def 					A_		( self  :    List[str]				)			->						int:






									'''simple docstring'''

									A         =					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      ,							)
									A         =					FlaxBlenderbotForConditionalGeneration(lowercase__				)
									A         =					np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]      ,							dtype=np.intaa				)
									A         =					np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]      ,							dtype=np.intaa				)
									A         =					lm_model(input_ids=lowercase__      ,							decoder_input_ids=lowercase__				)
									A         =					(*summary.shape, config.vocab_size)
									self.assertEqual(outputs['logits'].shape      ,							lowercase__				)



		def 					A_		( self  :    Any				)			->						Any:






									'''simple docstring'''

									A         =					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]      ,							dtype=np.intaa				)
									A         =					shift_tokens_right(lowercase__      ,							1      ,							2				)
									A         =					np.equal(lowercase__      ,							1				).astype(np.floataa				).sum()
									A         =					np.equal(lowercase__      ,							1				).astype(np.floataa				).sum()
									self.assertEqual(shifted.shape      ,							input_ids.shape				)
									self.assertEqual(lowercase__      ,							n_pad_before - 1				)
									self.assertTrue(np.equal(shifted[:, 0]      ,							2				).all()				)






@require_flax
class      UpperCAmelCase__		(      a__   ,unittest.TestCase   ,a__     ):
		lowerCAmelCase_       :	Optional[Any] 							=     True
		lowerCAmelCase_       :	List[str] 							=     (
		    (
		        FlaxBlenderbotModel,
		        FlaxBlenderbotForConditionalGeneration,
		    )
		    if is_flax_available()
		    else ()
		)
		lowerCAmelCase_       :	Tuple 							=     (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



		def 					A_		( self  :    Union[str, Any]				)			->						Tuple:






									'''simple docstring'''

									A         =					FlaxBlenderbotModelTester(self				)



		def 					A_		( self  :    List[Any]				)			->						List[str]:






									'''simple docstring'''

									A         =					self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(lowercase__      ,							lowercase__      ,							lowercase__				)



		def 					A_		( self  :    List[str]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					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(lowercase__      ,							lowercase__      ,							lowercase__				)



		def 					A_		( self  :    Optional[int]				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__				):
																							A         =					self._prepare_for_class(lowercase__      ,							lowercase__				)
																							A         =					model_class(lowercase__				)

																							@jax.jit
																							def encode_jitted(snake_case  :    Dict      ,							snake_case  :    Dict=None      ,							**snake_case  :    Optional[int]				):
																														return model.encode(input_ids=lowercase__      ,							attention_mask=lowercase__				)

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

																							with self.subTest('JIT Disabled'				):
																														with jax.disable_jit():
																																					A         =					encode_jitted(**lowercase__				).to_tuple()

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



		def 					A_		( self  :    List[str]				)			->						List[Any]:






									'''simple docstring'''

									A         =					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__				):
																							A         =					model_class(lowercase__				)
																							A         =					model.encode(inputs_dict['input_ids']      ,							inputs_dict['attention_mask']				)

																							A         =					{
																							    '''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  :    Optional[Any]      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Optional[int]				):
																														return model.decode(
																														    decoder_input_ids=lowercase__      ,							decoder_attention_mask=lowercase__      ,							encoder_outputs=lowercase__      ,							)

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

																							with self.subTest('JIT Disabled'				):
																														with jax.disable_jit():
																																					A         =					decode_jitted(**lowercase__				).to_tuple()

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



		@slow
		def 					A_		( self  :    int				)			->						Tuple:






									'''simple docstring'''

									for model_class_name in self.all_model_classes:
																A         =					model_class_name.from_pretrained('facebook/blenderbot-400M-distill'				)
																# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																A         =					np.ones((1, 1)				) * model.config.eos_token_id
																A         =					model(lowercase__				)
																self.assertIsNotNone(lowercase__				)



		@unittest.skipUnless(jax_device != 'cpu'      ,							'3B test too slow on CPU.'				)
		@slow
		def 					A_		( self  :    Tuple				)			->						List[str]:






									'''simple docstring'''

									A         =					{'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
									A         =					{'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

									A         =					FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'      ,							from_pt=lowercase__				)
									A         =					BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'				)

									A         =					['''Sam''']
									A         =					tokenizer(lowercase__      ,							return_tensors='jax'				)

									A         =					model.generate(**lowercase__      ,							**lowercase__				)
									A         =					'''Sam is a great name. It means "sun" in Gaelic.'''

									A         =					tokenizer.batch_decode(lowercase__      ,							**lowercase__				)
									assert generated_txt[0].strip() == tgt_text








"""simple docstring"""







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


A    =			logging.get_logger(__name__)

A    =			{
    '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__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	List[Any] 							=     """fnet"""



		def __init__( self  :    Tuple      ,							snake_case  :    Optional[int]=32_000      ,							snake_case  :    Optional[Any]=768      ,							snake_case  :    Union[str, Any]=12      ,							snake_case  :    List[str]=3_072      ,							snake_case  :    str="gelu_new"      ,							snake_case  :    Any=0.1      ,							snake_case  :    List[str]=512      ,							snake_case  :    Any=4      ,							snake_case  :    str=0.02      ,							snake_case  :    Union[str, Any]=1E-1_2      ,							snake_case  :    Optional[Any]=False      ,							snake_case  :    List[Any]=512      ,							snake_case  :    int=3      ,							snake_case  :    Any=1      ,							snake_case  :    Union[str, Any]=2      ,							**snake_case  :    Union[str, Any]      ,							)			->						Dict:






									'''simple docstring'''

									super().__init__(pad_token_id=snake_case      ,							bos_token_id=snake_case      ,							eos_token_id=snake_case      ,							**snake_case				)

									A         =					vocab_size
									A         =					max_position_embeddings
									A         =					hidden_size
									A         =					num_hidden_layers
									A         =					intermediate_size
									A         =					hidden_act
									A         =					hidden_dropout_prob
									A         =					initializer_range
									A         =					type_vocab_size
									A         =					layer_norm_eps
									A         =					use_tpu_fourier_optimizations
									A         =					tpu_short_seq_length



