



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
				lowerCAmelCase__					  =  '''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					,			):

				"""simple docstring"""


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

					"""simple docstring"""






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







					def 			snake_case					(							self :     List[str]					):
									lowercase__					:   Any													=					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,       self.vocab_size					)				,       3				,       self.vocab_size					)
									lowercase__					:   Dict													=					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,       dtype=np.intaa					))				,       -1					)

									lowercase__					:   Optional[Any]													=					shift_tokens_right(SCREAMING_SNAKE_CASE				,       1				,       2					)

									lowercase__					:   str													=					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=SCREAMING_SNAKE_CASE				,       )
									lowercase__					:   Optional[int]													=					prepare_blenderbot_inputs_dict(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
									return config, inputs_dict







					def 			snake_case					(							self :     int					):
									lowercase__							,							lowercase__					:   int													=					self.prepare_config_and_inputs()
									return config, inputs_dict







					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Union[str, Any]					):
									lowercase__					:   str													=					20
									lowercase__					:   Union[str, Any]													=					model_class_name(SCREAMING_SNAKE_CASE					)

									lowercase__					:   Optional[Any]													=					model.encode(inputs_dict["input_ids"]					)

									lowercase__							,							lowercase__					:   Dict													=					(
									    inputs_dict["decoder_input_ids"],
									    inputs_dict["decoder_attention_mask"],
									)

									lowercase__					:   Any													=					model.init_cache(decoder_input_ids.shape[0]				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
									lowercase__					:   int													=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,       dtype="i4"					)

									lowercase__					:   List[str]													=					jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]				,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,       )
									lowercase__					:   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				,       )

									lowercase__					:   Dict													=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,       dtype="i4"					)
									lowercase__					:   Optional[Any]													=					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				,       )

									lowercase__					:   int													=					model.decode(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   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 			snake_case					(							self :     int				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     List[Any]					):
									lowercase__					:   Union[str, Any]													=					20
									lowercase__					:   int													=					model_class_name(SCREAMING_SNAKE_CASE					)

									lowercase__					:   int													=					model.encode(inputs_dict["input_ids"]					)

									lowercase__							,							lowercase__					:   str													=					(
									    inputs_dict["decoder_input_ids"],
									    inputs_dict["decoder_attention_mask"],
									)

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

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

									lowercase__					:   Optional[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				,       )
									lowercase__					:   List[Any]													=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,       dtype="i4"					)
									lowercase__					:   Optional[int]													=					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				,       )

									lowercase__					:   Optional[Any]													=					model.decode(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       decoder_attention_mask=SCREAMING_SNAKE_CASE					)

									lowercase__					:   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}"""					)






@require_flax
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		9_9







					def 			snake_case					(							self :     str					):
									lowercase__					:   Optional[Any]													=					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				,       )

									lowercase__					:   str													=					input_ids.shape[0]
									lowercase__					:   int													=					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 			snake_case					(							self :     Dict					):
									lowercase__							,							lowercase__							,							lowercase__					:   Optional[int]													=					self._get_config_and_data()
									lowercase__					:   str													=					FlaxBlenderbotForConditionalGeneration(SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[Any]													=					lm_model(input_ids=SCREAMING_SNAKE_CASE					)
									lowercase__					:   str													=					(batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs["logits"].shape				,       SCREAMING_SNAKE_CASE					)







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







					def 			snake_case					(							self :     int					):
									lowercase__					:   Dict													=					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				,       dtype=np.intaa					)
									lowercase__					:   Optional[Any]													=					shift_tokens_right(SCREAMING_SNAKE_CASE				,       1				,       2					)
									lowercase__					:   Dict													=					np.equal(SCREAMING_SNAKE_CASE				,       1					).astype(np.floataa					).sum()
									lowercase__					:   Union[str, 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 				snake_case__(_UpperCamelCase    ,      unittest.TestCase    ,      _UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		True
					lowercase_								=		(
					    (
					        FlaxBlenderbotModel,
					        FlaxBlenderbotForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					lowercase_								=		(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







					def 			snake_case					(							self :     List[Any]					):
									lowercase__					:   List[str]													=					FlaxBlenderbotModelTester(self					)







					def 			snake_case					(							self :     Dict					):
									lowercase__							,							lowercase__					:   Dict													=					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 			snake_case					(							self :     int					):
									lowercase__							,							lowercase__					:   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(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     int					):
									lowercase__							,							lowercase__					:   int													=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__					):
																	lowercase__					:   Tuple													=					self._prepare_for_class(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
																	lowercase__					:   Optional[Any]													=					model_class(SCREAMING_SNAKE_CASE					)

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

																	with self.subTest("JIT Enabled"					):
																					lowercase__					:   List[str]													=					encode_jitted(**SCREAMING_SNAKE_CASE					).to_tuple()

																	with self.subTest("JIT Disabled"					):
																					with jax.disable_jit():
																									lowercase__					:   Union[str, Any]													=					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 			snake_case					(							self :     Union[str, Any]					):
									lowercase__							,							lowercase__					:   Tuple													=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__					):
																	lowercase__					:   Tuple													=					model_class(SCREAMING_SNAKE_CASE					)
																	lowercase__					:   Dict													=					model.encode(inputs_dict["input_ids"]				,       inputs_dict["attention_mask"]					)

																	lowercase__					:   int													=					{
																	    "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 :     List[Any]				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       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"					):
																					lowercase__					:   Dict													=					decode_jitted(**SCREAMING_SNAKE_CASE					).to_tuple()

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







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

									lowercase__					:   List[Any]													=					FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"				,       from_pt=SCREAMING_SNAKE_CASE					)
									lowercase__					:   List[str]													=					BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"					)

									lowercase__					:   List[str]													=					["Sam"]
									lowercase__					:   Dict													=					tokenizer(SCREAMING_SNAKE_CASE				,       return_tensors="jax"					)

									lowercase__					:   Dict													=					model.generate(**SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[int]													=					"Sam is a great name. It means \"sun\" in Gaelic."

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









def  __lowerCamelCase (       lowerCamelCase__ = 1_000						):

				"""simple docstring"""


				lowercase__							,							lowercase__					:   int													=					1, 1
				lowercase__					:   List[Any]													=					[]
				for i in range(1					,			n + 1						):
								lowercase__					:   Dict													=					prev_numerator + 2 * prev_denominator
								lowercase__					:   Tuple													=					prev_numerator + prev_denominator
								if len(str(lowerCamelCase__						)						) > len(str(lowerCamelCase__						)						):
												result.append(lowerCamelCase__						)
								lowercase__					:   int													=					numerator
								lowercase__					:   int													=					denominator

				return len(lowerCamelCase__						)


if __name__ == "__main__":
				print(f'''{solution() = }''')




