






"""simple docstring"""

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				(DPMSolverSinglestepScheduler,)
				UpperCAmelCase__        =				(('''num_inference_steps''', 2_5),)







				def     _snake_case					(							self							:						Optional[int]						,    **__lowerCamelCase							:						Union[str, Any]	)				->					List[str]:
										__magic_name__            =     {
										    """num_train_timesteps""": 1_0_0_0,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										    """solver_order""": 2,
										    """prediction_type""": """epsilon""",
										    """thresholding""": False,
										    """sample_max_value""": 1.0,
										    """algorithm_type""": """dpmsolver++""",
										    """solver_type""": """midpoint""",
										    """lambda_min_clipped""": -float("inf"	),
										    """variance_type""": None,
										}

										config.update(**A__	)
										return config







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Optional[Any]=0						,    **__lowerCamelCase							:						Any	)				->					Any:
										__magic_name__            =     dict(self.forward_default_kwargs	)
										__magic_name__            =     kwargs.pop("num_inference_steps"						,    A__	)
										__magic_name__            =     self.dummy_sample
										__magic_name__            =     0.1 * sample
										__magic_name__            =     [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
																__magic_name__            =     self.get_scheduler_config(**A__	)
																__magic_name__            =     scheduler_class(**A__	)
																scheduler.set_timesteps(A__	)
																# copy over dummy past residuals
																__magic_name__            =     dummy_past_residuals[: scheduler.config.solver_order]

																with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(A__	)
																						__magic_name__            =     scheduler_class.from_pretrained(A__	)
																						new_scheduler.set_timesteps(A__	)
																						# copy over dummy past residuals
																						__magic_name__            =     dummy_past_residuals[: new_scheduler.config.solver_order]

																__magic_name__            =     sample, sample
																for t in range(A__						,    time_step + scheduler.config.solver_order + 1	):
																						__magic_name__            =     scheduler.step(A__						,    A__						,    A__						,    **A__	).prev_sample
																						__magic_name__            =     new_scheduler.step(A__						,    A__						,    A__						,    **A__	).prev_sample

																						assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"







				def     _snake_case					(							self							:						Tuple	)				->					List[str]:
										pass







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						int=0						,    **__lowerCamelCase							:						Any	)				->					List[str]:
										__magic_name__            =     dict(self.forward_default_kwargs	)
										__magic_name__            =     kwargs.pop("num_inference_steps"						,    A__	)
										__magic_name__            =     self.dummy_sample
										__magic_name__            =     0.1 * sample
										__magic_name__            =     [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
																__magic_name__            =     self.get_scheduler_config()
																__magic_name__            =     scheduler_class(**A__	)
																scheduler.set_timesteps(A__	)

																# copy over dummy past residuals (must be after setting timesteps)
																__magic_name__            =     dummy_past_residuals[: scheduler.config.solver_order]

																with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(A__	)
																						__magic_name__            =     scheduler_class.from_pretrained(A__	)
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(A__	)

																						# copy over dummy past residual (must be after setting timesteps)
																						__magic_name__            =     dummy_past_residuals[: new_scheduler.config.solver_order]

																__magic_name__            =     scheduler.step(A__						,    A__						,    A__						,    **A__	).prev_sample
																__magic_name__            =     new_scheduler.step(A__						,    A__						,    A__						,    **A__	).prev_sample

																assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						Any=None						,    **__lowerCamelCase							:						Tuple	)				->					Any:
										if scheduler is None:
																__magic_name__            =     self.scheduler_classes[0]
																__magic_name__            =     self.get_scheduler_config(**A__	)
																__magic_name__            =     scheduler_class(**A__	)

										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config(**A__	)
										__magic_name__            =     scheduler_class(**A__	)

										__magic_name__            =     1_0
										__magic_name__            =     self.dummy_model()
										__magic_name__            =     self.dummy_sample_deter
										scheduler.set_timesteps(A__	)

										for i, t in enumerate(scheduler.timesteps	):
																__magic_name__            =     model(A__						,    A__	)
																__magic_name__            =     scheduler.step(A__						,    A__						,    A__	).prev_sample

										return sample







				def     _snake_case					(							self							:						Optional[int]	)				->					List[Any]:
										__magic_name__            =     DPMSolverSinglestepScheduler(**self.get_scheduler_config()	)
										__magic_name__            =     5_0
										__magic_name__            =     self.dummy_model()
										__magic_name__            =     self.dummy_sample_deter
										scheduler.set_timesteps(A__	)

										# make sure that the first t is uneven
										for i, t in enumerate(scheduler.timesteps[3:]	):
																__magic_name__            =     model(A__						,    A__	)
																__magic_name__            =     scheduler.step(A__						,    A__						,    A__	).prev_sample

										__magic_name__            =     torch.mean(torch.abs(A__	)	)

										assert abs(result_mean.item() - 0.2574	) < 1e-3







				def     _snake_case					(							self							:						str	)				->					Dict:
										for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																self.check_over_configs(num_train_timesteps=A__	)







				def     _snake_case					(							self							:						List[str]	)				->					List[str]:
										# make sure that iterating over schedulers with same config names gives same results
										# for defaults
										__magic_name__            =     DPMSolverSinglestepScheduler(**self.get_scheduler_config()	)
										__magic_name__            =     self.full_loop(scheduler=A__	)
										__magic_name__            =     torch.mean(torch.abs(A__	)	)

										assert abs(result_mean.item() - 0.2791	) < 1e-3

										__magic_name__            =     DEISMultistepScheduler.from_config(scheduler.config	)
										__magic_name__            =     DPMSolverMultistepScheduler.from_config(scheduler.config	)
										__magic_name__            =     UniPCMultistepScheduler.from_config(scheduler.config	)
										__magic_name__            =     DPMSolverSinglestepScheduler.from_config(scheduler.config	)

										__magic_name__            =     self.full_loop(scheduler=A__	)
										__magic_name__            =     torch.mean(torch.abs(A__	)	)

										assert abs(result_mean.item() - 0.2791	) < 1e-3







				def     _snake_case					(							self							:						List[str]	)				->					List[Any]:
										self.check_over_configs(thresholding=A__	)
										for order in [1, 2, 3]:
																for solver_type in ["midpoint", "heun"]:
																						for threshold in [0.5, 1.0, 2.0]:
																												for prediction_type in ["epsilon", "sample"]:
																																		self.check_over_configs(
																																		    thresholding=A__						,    prediction_type=A__						,    sample_max_value=A__						,    algorithm_type="dpmsolver++"						,    solver_order=A__						,    solver_type=A__						,    )







				def     _snake_case					(							self							:						int	)				->					Optional[Any]:
										for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=A__	)







				def     _snake_case					(							self							:						int	)				->					Tuple:
										for algorithm_type in ["dpmsolver", "dpmsolver++"]:
																for solver_type in ["midpoint", "heun"]:
																						for order in [1, 2, 3]:
																												for prediction_type in ["epsilon", "sample"]:
																																		self.check_over_configs(
																																		    solver_order=A__						,    solver_type=A__						,    prediction_type=A__						,    algorithm_type=A__						,    )
																																		__magic_name__            =     self.full_loop(
																																		    solver_order=A__						,    solver_type=A__						,    prediction_type=A__						,    algorithm_type=A__						,    )
																																		assert not torch.isnan(A__	).any(), "Samples have nan numbers"







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Optional[Any]:
										self.check_over_configs(lower_order_final=A__	)
										self.check_over_configs(lower_order_final=A__	)







				def     _snake_case					(							self							:						Tuple	)				->					str:
										self.check_over_configs(lambda_min_clipped=-float("inf"	)	)
										self.check_over_configs(lambda_min_clipped=-5.1	)







				def     _snake_case					(							self							:						List[Any]	)				->					List[str]:
										self.check_over_configs(variance_type=A__	)
										self.check_over_configs(variance_type="learned_range"	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					str:
										for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																self.check_over_forward(num_inference_steps=A__						,    time_step=0	)







				def     _snake_case					(							self							:						str	)				->					List[str]:
										__magic_name__            =     self.full_loop()
										__magic_name__            =     torch.mean(torch.abs(A__	)	)

										assert abs(result_mean.item() - 0.2791	) < 1e-3







				def     _snake_case					(							self							:						Optional[Any]	)				->					List[str]:
										__magic_name__            =     self.full_loop(use_karras_sigmas=A__	)
										__magic_name__            =     torch.mean(torch.abs(A__	)	)

										assert abs(result_mean.item() - 0.2248	) < 1e-3







				def     _snake_case					(							self							:						Tuple	)				->					Union[str, Any]:
										__magic_name__            =     self.full_loop(prediction_type="v_prediction"	)
										__magic_name__            =     torch.mean(torch.abs(A__	)	)

										assert abs(result_mean.item() - 0.1453	) < 1e-3







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[int]:
										__magic_name__            =     self.full_loop(prediction_type="v_prediction"						,    use_karras_sigmas=A__	)
										__magic_name__            =     torch.mean(torch.abs(A__	)	)

										assert abs(result_mean.item() - 0.0649	) < 1e-3







				def     _snake_case					(							self							:						Tuple	)				->					List[Any]:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config(thresholding=A__						,    dynamic_thresholding_ratio=0	)
										__magic_name__            =     scheduler_class(**A__	)

										__magic_name__            =     1_0
										__magic_name__            =     self.dummy_model()
										__magic_name__            =     self.dummy_sample_deter.half()
										scheduler.set_timesteps(A__	)

										for i, t in enumerate(scheduler.timesteps	):
																__magic_name__            =     model(A__						,    A__	)
																__magic_name__            =     scheduler.step(A__						,    A__						,    A__	).prev_sample

										assert sample.dtype == torch.floataa











"""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
			lowercase    			=       '''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:List[Any] ,						__lowerCamelCase:int ,						__lowerCamelCase:List[Any]=None ,						__lowerCamelCase:Any=None ,						__lowerCamelCase:Any=None ,						__lowerCamelCase:List[str]=None ,						__lowerCamelCase:Optional[int]=None ,						__lowerCamelCase:Optional[int]=None ,						):

						'''simple docstring'''






						if attention_mask is None:
												__magic_name__            =     np.where(input_ids != config.pad_token_id ,						1 ,						0   )
						if decoder_attention_mask is None:
												__magic_name__            =     np.where(decoder_input_ids != config.pad_token_id ,						1 ,						0   )
						if head_mask is None:
												__magic_name__            =     np.ones((config.encoder_layers, config.encoder_attention_heads)   )
						if decoder_head_mask is None:
												__magic_name__            =     np.ones((config.decoder_layers, config.decoder_attention_heads)   )
						if cross_attn_head_mask is None:
												__magic_name__            =     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 						A_							:







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						Optional[Any]=1_3						,    __lowerCamelCase							:						str=7						,    __lowerCamelCase							:						Optional[Any]=True						,    __lowerCamelCase							:						Dict=False						,    __lowerCamelCase							:						Optional[int]=9_9						,    __lowerCamelCase							:						Tuple=1_6						,    __lowerCamelCase							:						Any=2						,    __lowerCamelCase							:						int=4						,    __lowerCamelCase							:						List[Any]=4						,    __lowerCamelCase							:						List[str]="gelu"						,    __lowerCamelCase							:						Union[str, Any]=0.1						,    __lowerCamelCase							:						Optional[int]=0.1						,    __lowerCamelCase							:						List[str]=3_2						,    __lowerCamelCase							:						int=2						,    __lowerCamelCase							:						Any=1						,    __lowerCamelCase							:						int=0						,    __lowerCamelCase							:						Tuple=0.02						,    )				->					List[Any]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     seq_length
										__magic_name__            =     is_training
										__magic_name__            =     use_labels
										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size
										__magic_name__            =     hidden_act
										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     eos_token_id
										__magic_name__            =     pad_token_id
										__magic_name__            =     bos_token_id
										__magic_name__            =     initializer_range







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

										__magic_name__            =     shift_tokens_right(__lowerCamelCase						,    1						,    2	)

										__magic_name__            =     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=__lowerCamelCase						,    )
										__magic_name__            =     prepare_blenderbot_inputs_dict(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)
										return config, inputs_dict







				def     _snake_case					(							self							:						Optional[Any]	)				->					Any:
										__magic_name__      ,    __magic_name__            =     self.prepare_config_and_inputs()
										return config, inputs_dict







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						List[Any]	)				->					Union[str, Any]:
										__magic_name__            =     2_0
										__magic_name__            =     model_class_name(__lowerCamelCase	)

										__magic_name__            =     model.encode(inputs_dict["input_ids"]	)

										__magic_name__      ,    __magic_name__            =     (
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

										__magic_name__            =     model.init_cache(decoder_input_ids.shape[0]						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,    dtype="i4"	)

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

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

										__magic_name__            =     model.decode(__lowerCamelCase						,    __lowerCamelCase	)

										__magic_name__            =     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							:						List[Any]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Tuple	)				->					int:
										__magic_name__            =     2_0
										__magic_name__            =     model_class_name(__lowerCamelCase	)

										__magic_name__            =     model.encode(inputs_dict["input_ids"]	)

										__magic_name__      ,    __magic_name__            =     (
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

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

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

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

										__magic_name__            =     model.decode(__lowerCamelCase						,    __lowerCamelCase						,    decoder_attention_mask=__lowerCamelCase	)

										__magic_name__            =     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 						A_							(      unittest.TestCase  ):
				UpperCAmelCase__        =				9_9







				def     _snake_case					(							self							:						Dict	)				->					Dict:
										__magic_name__            =     np.array(
										    [
										        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
										        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
										        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
										        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
										        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
										        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
										        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
										        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
										        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
										        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
										        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
										        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
										        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
										    ]						,    dtype=np.intaa						,    )

										__magic_name__            =     input_ids.shape[0]
										__magic_name__            =     BlenderbotConfig(
										    vocab_size=self.vocab_size						,    d_model=2_4						,    encoder_layers=2						,    decoder_layers=2						,    encoder_attention_heads=2						,    decoder_attention_heads=2						,    encoder_ffn_dim=3_2						,    decoder_ffn_dim=3_2						,    max_position_embeddings=4_8						,    eos_token_id=2						,    pad_token_id=1						,    bos_token_id=0						,    )
										return config, input_ids, batch_size







				def     _snake_case					(							self							:						Optional[Any]	)				->					Optional[int]:
										__magic_name__      ,    __magic_name__      ,    __magic_name__            =     self._get_config_and_data()
										__magic_name__            =     FlaxBlenderbotForConditionalGeneration(__lowerCamelCase	)
										__magic_name__            =     lm_model(input_ids=__lowerCamelCase	)
										__magic_name__            =     (batch_size, input_ids.shape[1], config.vocab_size)
										self.assertEqual(outputs["logits"].shape						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[Any]:
										__magic_name__            =     BlenderbotConfig(
										    vocab_size=self.vocab_size						,    d_model=1_4						,    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=4_8						,    )
										__magic_name__            =     FlaxBlenderbotForConditionalGeneration(__lowerCamelCase	)
										__magic_name__            =     np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]						,    dtype=np.intaa	)
										__magic_name__            =     np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]						,    dtype=np.intaa	)
										__magic_name__            =     lm_model(input_ids=__lowerCamelCase						,    decoder_input_ids=__lowerCamelCase	)
										__magic_name__            =     (*summary.shape, config.vocab_size)
										self.assertEqual(outputs["logits"].shape						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[Any]:
										__magic_name__            =     np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]						,    dtype=np.intaa	)
										__magic_name__            =     shift_tokens_right(__lowerCamelCase						,    1						,    2	)
										__magic_name__            =     np.equal(__lowerCamelCase						,    1	).astype(np.floataa	).sum()
										__magic_name__            =     np.equal(__lowerCamelCase						,    1	).astype(np.floataa	).sum()
										self.assertEqual(shifted.shape						,    input_ids.shape	)
										self.assertEqual(__lowerCamelCase						,    n_pad_before - 1	)
										self.assertTrue(np.equal(shifted[:, 0]						,    2	).all()	)





@require_flax
class 						A_							(      snake_case_					,							unittest.TestCase					,							snake_case_  ):
				UpperCAmelCase__        =				True
				UpperCAmelCase__        =				(
				    (
				        FlaxBlenderbotModel,
				        FlaxBlenderbotForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				UpperCAmelCase__        =				(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







				def     _snake_case					(							self							:						List[Any]	)				->					Any:
										__magic_name__            =     FlaxBlenderbotModelTester(self	)







				def     _snake_case					(							self							:						int	)				->					Optional[Any]:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[int]	)				->					Union[str, Any]:
										__magic_name__      ,    __magic_name__            =     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(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Tuple	)				->					Dict:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__	):
																						__magic_name__            =     self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)
																						__magic_name__            =     model_class(__lowerCamelCase	)

																						@jax.jit
																						def encode_jitted(__lowerCamelCase							:						Dict						,    __lowerCamelCase							:						str=None						,    **__lowerCamelCase							:						List[str]	):
																												return model.encode(input_ids=__lowerCamelCase						,    attention_mask=__lowerCamelCase	)

																						with self.subTest("JIT Enabled"	):
																												__magic_name__            =     encode_jitted(**__lowerCamelCase	).to_tuple()

																						with self.subTest("JIT Disabled"	):
																												with jax.disable_jit():
																																		__magic_name__            =     encode_jitted(**__lowerCamelCase	).to_tuple()

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







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Union[str, Any]:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__	):
																						__magic_name__            =     model_class(__lowerCamelCase	)
																						__magic_name__            =     model.encode(inputs_dict["input_ids"]						,    inputs_dict["attention_mask"]	)

																						__magic_name__            =     {
																						    "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(__lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Dict	):
																												return model.decode(
																												    decoder_input_ids=__lowerCamelCase						,    decoder_attention_mask=__lowerCamelCase						,    encoder_outputs=__lowerCamelCase						,    )

																						with self.subTest("JIT Enabled"	):
																												__magic_name__            =     decode_jitted(**__lowerCamelCase	).to_tuple()

																						with self.subTest("JIT Disabled"	):
																												with jax.disable_jit():
																																		__magic_name__            =     decode_jitted(**__lowerCamelCase	).to_tuple()

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







				@slow
				def     _snake_case					(							self							:						int	)				->					int:
										for model_class_name in self.all_model_classes:
																__magic_name__            =     model_class_name.from_pretrained("facebook/blenderbot-400M-distill"	)
																# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																__magic_name__            =     np.ones((1, 1)	) * model.config.eos_token_id
																__magic_name__            =     model(__lowerCamelCase	)
																self.assertIsNotNone(__lowerCamelCase	)







				@unittest.skipUnless(jax_device != "cpu"						,    "3B test too slow on CPU."	)
				@slow
				def     _snake_case					(							self							:						int	)				->					List[Any]:
										__magic_name__            =     {"num_beams": 1, "early_stopping": True, "min_length": 1_5, "max_length": 2_5}
										__magic_name__            =     {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

										__magic_name__            =     FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"						,    from_pt=__lowerCamelCase	)
										__magic_name__            =     BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"	)

										__magic_name__            =     ["Sam"]
										__magic_name__            =     tokenizer(__lowerCamelCase						,    return_tensors="jax"	)

										__magic_name__            =     model.generate(**__lowerCamelCase						,    **__lowerCamelCase	)
										__magic_name__            =     "Sam is a great name. It means \"sun\" in Gaelic."

										__magic_name__            =     tokenizer.batch_decode(__lowerCamelCase						,    **__lowerCamelCase	)
										assert generated_txt[0].strip() == tgt_text



