


"""simple docstring"""





# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()



class 		snake_case_(       a__							,					a__							,					a__							,					unittest.TestCase			):
     __UpperCamelCase  =					StableDiffusionControlNetImgaImgPipeline
     __UpperCamelCase  =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
     __UpperCamelCase  =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     __UpperCamelCase  =					IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''}			)
     __UpperCamelCase  =					IMAGE_TO_IMAGE_IMAGE_PARAMS






     def      lowerCamelCase__     (	self  :					Tuple				):
           torch.manual_seed(0				)
           lowerCAmelCase					:	Optional[int]       =   UNetaDConditionModel(
               block_out_channels=(3_2, 6_4)					,				layers_per_block=2					,				sample_size=3_2					,				in_channels=4					,				out_channels=4					,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,				cross_attention_dim=3_2					,				)
           torch.manual_seed(0				)
           lowerCAmelCase					:	int       =   ControlNetModel(
               block_out_channels=(3_2, 6_4)					,				layers_per_block=2					,				in_channels=4					,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,				cross_attention_dim=3_2					,				conditioning_embedding_out_channels=(1_6, 3_2)					,				)
           torch.manual_seed(0				)
           lowerCAmelCase					:	List[Any]       =   DDIMScheduler(
               beta_start=0.00_085					,				beta_end=0.012					,				beta_schedule='''scaled_linear'''					,				clip_sample=UpperCamelCase_					,				set_alpha_to_one=UpperCamelCase_					,				)
           torch.manual_seed(0				)
           lowerCAmelCase					:	Optional[int]       =   AutoencoderKL(
               block_out_channels=[3_2, 6_4]					,				in_channels=3					,				out_channels=3					,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,				latent_channels=4					,				)
           torch.manual_seed(0				)
           lowerCAmelCase					:	Dict       =   CLIPTextConfig(
               bos_token_id=0					,				eos_token_id=2					,				hidden_size=3_2					,				intermediate_size=3_7					,				layer_norm_eps=1E-05					,				num_attention_heads=4					,				num_hidden_layers=5					,				pad_token_id=1					,				vocab_size=1_0_0_0					,				)
           lowerCAmelCase					:	Optional[int]       =   CLIPTextModel(UpperCamelCase_				)
           lowerCAmelCase					:	List[Any]       =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

           lowerCAmelCase					:	List[str]       =   {
               '''unet''': unet,
               '''controlnet''': controlnet,
               '''scheduler''': scheduler,
               '''vae''': vae,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''safety_checker''': None,
               '''feature_extractor''': None,
           }
           return components






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Tuple=0				):
           if str(UpperCamelCase_				).startswith('''mps'''				):
                 lowerCAmelCase					:	Any       =   torch.manual_seed(UpperCamelCase_				)
           else:
                 lowerCAmelCase					:	Optional[int]       =   torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)

           lowerCAmelCase					:	Tuple       =   2
           lowerCAmelCase					:	Union[str, Any]       =   randn_tensor(
               (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)					,				generator=UpperCamelCase_					,				device=torch.device(UpperCamelCase_				)					,				)
           lowerCAmelCase					:	Optional[Any]       =   floats_tensor(control_image.shape					,				rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   image.cpu().permute(0					,				2					,				3					,				1				)[0]
           lowerCAmelCase					:	Tuple       =   Image.fromarray(np.uinta(UpperCamelCase_				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
           lowerCAmelCase					:	Optional[int]       =   {
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''generator''': generator,
               '''num_inference_steps''': 2,
               '''guidance_scale''': 6.0,
               '''output_type''': '''numpy''',
               '''image''': image,
               '''control_image''': control_image,
           }

           return inputs






     def      lowerCamelCase__     (	self  :					List[str]				):
           return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3				)






     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()					,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,				)
     def      lowerCamelCase__     (	self  :					List[str]				):
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)






     def      lowerCamelCase__     (	self  :					List[str]				):
           self._test_inference_batch_single_identical(expected_max_diff=2E-3				)



class 		snake_case_(       a__							,					a__							,					unittest.TestCase			):
     __UpperCamelCase  =					StableDiffusionControlNetImgaImgPipeline
     __UpperCamelCase  =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
     __UpperCamelCase  =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     __UpperCamelCase  =					frozenset([]			)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess






     def      lowerCamelCase__     (	self  :					List[str]				):
           torch.manual_seed(0				)
           lowerCAmelCase					:	Tuple       =   UNetaDConditionModel(
               block_out_channels=(3_2, 6_4)					,				layers_per_block=2					,				sample_size=3_2					,				in_channels=4					,				out_channels=4					,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,				cross_attention_dim=3_2					,				)
           torch.manual_seed(0				)

           def init_weights(UpperCamelCase_  :					Optional[int]				):
                 if isinstance(UpperCamelCase_					,				torch.nn.Convad				):
                       torch.nn.init.normal(m.weight				)
                       m.bias.data.fill_(1.0				)

           lowerCAmelCase					:	Optional[int]       =   ControlNetModel(
               block_out_channels=(3_2, 6_4)					,				layers_per_block=2					,				in_channels=4					,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,				cross_attention_dim=3_2					,				conditioning_embedding_out_channels=(1_6, 3_2)					,				)
           controlneta.controlnet_down_blocks.apply(UpperCamelCase_				)

           torch.manual_seed(0				)
           lowerCAmelCase					:	Optional[int]       =   ControlNetModel(
               block_out_channels=(3_2, 6_4)					,				layers_per_block=2					,				in_channels=4					,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,				cross_attention_dim=3_2					,				conditioning_embedding_out_channels=(1_6, 3_2)					,				)
           controlneta.controlnet_down_blocks.apply(UpperCamelCase_				)

           torch.manual_seed(0				)
           lowerCAmelCase					:	int       =   DDIMScheduler(
               beta_start=0.00_085					,				beta_end=0.012					,				beta_schedule='''scaled_linear'''					,				clip_sample=UpperCamelCase_					,				set_alpha_to_one=UpperCamelCase_					,				)
           torch.manual_seed(0				)
           lowerCAmelCase					:	str       =   AutoencoderKL(
               block_out_channels=[3_2, 6_4]					,				in_channels=3					,				out_channels=3					,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,				latent_channels=4					,				)
           torch.manual_seed(0				)
           lowerCAmelCase					:	str       =   CLIPTextConfig(
               bos_token_id=0					,				eos_token_id=2					,				hidden_size=3_2					,				intermediate_size=3_7					,				layer_norm_eps=1E-05					,				num_attention_heads=4					,				num_hidden_layers=5					,				pad_token_id=1					,				vocab_size=1_0_0_0					,				)
           lowerCAmelCase					:	Optional[Any]       =   CLIPTextModel(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

           lowerCAmelCase					:	Tuple       =   MultiControlNetModel([controlneta, controlneta]				)

           lowerCAmelCase					:	Dict       =   {
               '''unet''': unet,
               '''controlnet''': controlnet,
               '''scheduler''': scheduler,
               '''vae''': vae,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''safety_checker''': None,
               '''feature_extractor''': None,
           }
           return components






     def      lowerCamelCase__     (	self  :					str					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Union[str, Any]=0				):
           if str(UpperCamelCase_				).startswith('''mps'''				):
                 lowerCAmelCase					:	int       =   torch.manual_seed(UpperCamelCase_				)
           else:
                 lowerCAmelCase					:	Tuple       =   torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)

           lowerCAmelCase					:	List[Any]       =   2

           lowerCAmelCase					:	List[Any]       =   [
               randn_tensor(
                   (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)					,				generator=UpperCamelCase_					,				device=torch.device(UpperCamelCase_				)					,				),
               randn_tensor(
                   (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)					,				generator=UpperCamelCase_					,				device=torch.device(UpperCamelCase_				)					,				),
           ]

           lowerCAmelCase					:	Optional[int]       =   floats_tensor(control_image[0].shape					,				rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)
           lowerCAmelCase					:	Dict       =   image.cpu().permute(0					,				2					,				3					,				1				)[0]
           lowerCAmelCase					:	List[Any]       =   Image.fromarray(np.uinta(UpperCamelCase_				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
           lowerCAmelCase					:	int       =   {
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''generator''': generator,
               '''num_inference_steps''': 2,
               '''guidance_scale''': 6.0,
               '''output_type''': '''numpy''',
               '''image''': image,
               '''control_image''': control_image,
           }

           return inputs






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase					:	Any       =   self.get_dummy_components()
           lowerCAmelCase					:	Any       =   self.pipeline_class(**UpperCamelCase_				)
           pipe.to(UpperCamelCase_				)

           lowerCAmelCase					:	Tuple       =   10.0
           lowerCAmelCase					:	Any       =   4

           lowerCAmelCase					:	Tuple       =   self.get_dummy_inputs(UpperCamelCase_				)
           lowerCAmelCase					:	List[str]       =   steps
           lowerCAmelCase					:	Union[str, Any]       =   scale
           lowerCAmelCase					:	Tuple       =   pipe(**UpperCamelCase_				)[0]

           lowerCAmelCase					:	List[Any]       =   self.get_dummy_inputs(UpperCamelCase_				)
           lowerCAmelCase					:	Any       =   steps
           lowerCAmelCase					:	Tuple       =   scale
           lowerCAmelCase					:	Tuple       =   pipe(**UpperCamelCase_					,				control_guidance_start=0.1					,				control_guidance_end=0.2				)[0]

           lowerCAmelCase					:	Any       =   self.get_dummy_inputs(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   steps
           lowerCAmelCase					:	int       =   scale
           lowerCAmelCase					:	List[str]       =   pipe(**UpperCamelCase_					,				control_guidance_start=[0.1, 0.3]					,				control_guidance_end=[0.2, 0.7]				)[0]

           lowerCAmelCase					:	Dict       =   self.get_dummy_inputs(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   steps
           lowerCAmelCase					:	List[Any]       =   scale
           lowerCAmelCase					:	Optional[int]       =   pipe(**UpperCamelCase_					,				control_guidance_start=0.4					,				control_guidance_end=[0.5, 0.8]				)[0]

           # make sure that all outputs are different
           assert np.sum(np.abs(output_a - output_a				)				) > 1E-3
           assert np.sum(np.abs(output_a - output_a				)				) > 1E-3
           assert np.sum(np.abs(output_a - output_a				)				) > 1E-3






     def      lowerCamelCase__     (	self  :					str				):
           return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3				)






     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()					,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,				)
     def      lowerCamelCase__     (	self  :					List[str]				):
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           self._test_inference_batch_single_identical(expected_max_diff=2E-3				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase					:	Any       =   self.get_dummy_components()
           lowerCAmelCase					:	str       =   self.pipeline_class(**UpperCamelCase_				)
           pipe.to(UpperCamelCase_				)
           pipe.set_progress_bar_config(disable=UpperCamelCase_				)
           with tempfile.TemporaryDirectory() as tmpdir:
                 try:
                       # save_pretrained is not implemented for Multi-ControlNet
                       pipe.save_pretrained(UpperCamelCase_				)
                 except NotImplementedError:
                       pass



@slow
@require_torch_gpu
class 		snake_case_(       unittest.TestCase			):






     def      lowerCamelCase__     (	self  :					Any				):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	Optional[int]       =   ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''				)

           lowerCAmelCase					:	str       =   StableDiffusionControlNetImgaImgPipeline.from_pretrained(
               '''runwayml/stable-diffusion-v1-5'''					,				safety_checker=UpperCamelCase_					,				controlnet=UpperCamelCase_				)
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=UpperCamelCase_				)

           lowerCAmelCase					:	Any       =   torch.Generator(device='''cpu'''				).manual_seed(0				)
           lowerCAmelCase					:	int       =   '''evil space-punk bird'''
           lowerCAmelCase					:	Dict       =   load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''				).resize((5_1_2, 5_1_2)				)
           lowerCAmelCase					:	List[Any]       =   load_image(
               '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''				).resize((5_1_2, 5_1_2)				)

           lowerCAmelCase					:	List[Any]       =   pipe(
               UpperCamelCase_					,				UpperCamelCase_					,				control_image=UpperCamelCase_					,				generator=UpperCamelCase_					,				output_type='''np'''					,				num_inference_steps=5_0					,				strength=0.6					,				)

           lowerCAmelCase					:	Optional[int]       =   output.images[0]

           assert image.shape == (5_1_2, 5_1_2, 3)

           lowerCAmelCase					:	List[str]       =   load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'''				)

           assert np.abs(expected_image - image				).max() < 9E-2






"""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
 snake_case__		:     int    				=				'''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      _snake_case						(						_snake_case				:			str ,   _snake_case				:			Any ,   _snake_case				:			str=None ,   _snake_case				:			str=None ,   _snake_case				:			Dict=None ,   _snake_case				:			Tuple=None ,   _snake_case				:			str=None ,   _snake_case				:			Any=None ,   ):
      if attention_mask is None:
            lowerCAmelCase					:	List[str]       =   np.where(input_ids != config.pad_token_id ,   1 ,   0			)
      if decoder_attention_mask is None:
            lowerCAmelCase					:	Optional[int]       =   np.where(decoder_input_ids != config.pad_token_id ,   1 ,   0			)
      if head_mask is None:
            lowerCAmelCase					:	Any       =   np.ones((config.encoder_layers, config.encoder_attention_heads)			)
      if decoder_head_mask is None:
            lowerCAmelCase					:	int       =   np.ones((config.decoder_layers, config.decoder_attention_heads)			)
      if cross_attn_head_mask is None:
            lowerCAmelCase					:	List[str]       =   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_:






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






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

           lowerCAmelCase					:	Tuple       =   shift_tokens_right(UpperCamelCase_					,				1					,				2				)

           lowerCAmelCase					:	Union[str, Any]       =   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=UpperCamelCase_					,				)
           lowerCAmelCase					:	Union[str, Any]       =   prepare_blenderbot_inputs_dict(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)
           return config, inputs_dict






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   self.prepare_config_and_inputs()
           return config, inputs_dict






     def      lowerCamelCase__     (	self  :					List[str]					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	int       =   2_0
           lowerCAmelCase					:	Tuple       =   model_class_name(UpperCamelCase_				)

           lowerCAmelCase					:	Optional[Any]       =   model.encode(inputs_dict['''input_ids''']				)

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

           lowerCAmelCase					:	str       =   model.init_cache(decoder_input_ids.shape[0]					,				UpperCamelCase_					,				UpperCamelCase_				)
           lowerCAmelCase					:	Union[str, Any]       =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,				dtype='''i4'''				)

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

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

           lowerCAmelCase					:	Union[str, Any]       =   model.decode(UpperCamelCase_					,				UpperCamelCase_				)

           lowerCAmelCase					:	int       =   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      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					List[str]				):
           lowerCAmelCase					:	Optional[int]       =   2_0
           lowerCAmelCase					:	List[Any]       =   model_class_name(UpperCamelCase_				)

           lowerCAmelCase					:	Union[str, Any]       =   model.encode(inputs_dict['''input_ids''']				)

           lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   (
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

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

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

           lowerCAmelCase					:	Dict       =   model.decode(
               decoder_input_ids[:, :-1]					,				UpperCamelCase_					,				decoder_attention_mask=UpperCamelCase_					,				past_key_values=UpperCamelCase_					,				decoder_position_ids=UpperCamelCase_					,				)
           lowerCAmelCase					:	Any       =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,				dtype='''i4'''				)
           lowerCAmelCase					:	Union[str, Any]       =   model.decode(
               decoder_input_ids[:, -1:]					,				UpperCamelCase_					,				past_key_values=outputs_cache.past_key_values					,				decoder_attention_mask=UpperCamelCase_					,				decoder_position_ids=UpperCamelCase_					,				)

           lowerCAmelCase					:	Dict       =   model.decode(UpperCamelCase_					,				UpperCamelCase_					,				decoder_attention_mask=UpperCamelCase_				)

           lowerCAmelCase					:	Any       =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
           self.parent.assertTrue(diff < 1E-3					,				msg=F'''Max diff is {diff}'''				)



@require_flax
class 		snake_case_(       unittest.TestCase			):
     __UpperCamelCase  =					99






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase					:	List[Any]       =   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					,				)

           lowerCAmelCase					:	List[Any]       =   input_ids.shape[0]
           lowerCAmelCase					:	Optional[Any]       =   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      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	Any       =   self._get_config_and_data()
           lowerCAmelCase					:	Any       =   FlaxBlenderbotForConditionalGeneration(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[int]       =   lm_model(input_ids=UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   (batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs['''logits'''].shape					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	Any       =   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					,				)
           lowerCAmelCase					:	int       =   FlaxBlenderbotForConditionalGeneration(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   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				)
           lowerCAmelCase					:	List[str]       =   np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]					,				dtype=np.intaa				)
           lowerCAmelCase					:	List[Any]       =   lm_model(input_ids=UpperCamelCase_					,				decoder_input_ids=UpperCamelCase_				)
           lowerCAmelCase					:	str       =   (*summary.shape, config.vocab_size)
           self.assertEqual(outputs['''logits'''].shape					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Any       =   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				)
           lowerCAmelCase					:	Tuple       =   shift_tokens_right(UpperCamelCase_					,				1					,				2				)
           lowerCAmelCase					:	Optional[int]       =   np.equal(UpperCamelCase_					,				1				).astype(np.floataa				).sum()
           lowerCAmelCase					:	str       =   np.equal(UpperCamelCase_					,				1				).astype(np.floataa				).sum()
           self.assertEqual(shifted.shape					,				input_ids.shape				)
           self.assertEqual(UpperCamelCase_					,				n_pad_before - 1				)
           self.assertTrue(np.equal(shifted[:, 0]					,				2				).all()				)



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






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	Any       =   FlaxBlenderbotModelTester(self				)






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase,      lowerCAmelCase					:	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(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase,      lowerCAmelCase					:	str       =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__				):
                       lowerCAmelCase					:	Optional[int]       =   self._prepare_for_class(UpperCamelCase_					,				UpperCamelCase_				)
                       lowerCAmelCase					:	Optional[Any]       =   model_class(UpperCamelCase_				)

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

                       with self.subTest('''JIT Enabled'''				):
                             lowerCAmelCase					:	List[str]       =   encode_jitted(**UpperCamelCase_				).to_tuple()

                       with self.subTest('''JIT Disabled'''				):
                             with jax.disable_jit():
                                   lowerCAmelCase					:	int       =   encode_jitted(**UpperCamelCase_				).to_tuple()

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






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase,      lowerCAmelCase					:	Any       =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__				):
                       lowerCAmelCase					:	Tuple       =   model_class(UpperCamelCase_				)
                       lowerCAmelCase					:	int       =   model.encode(inputs_dict['''input_ids''']					,				inputs_dict['''attention_mask''']				)

                       lowerCAmelCase					:	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(UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					int				):
                             return model.decode(
                                 decoder_input_ids=UpperCamelCase_					,				decoder_attention_mask=UpperCamelCase_					,				encoder_outputs=UpperCamelCase_					,				)

                       with self.subTest('''JIT Enabled'''				):
                             lowerCAmelCase					:	str       =   decode_jitted(**UpperCamelCase_				).to_tuple()

                       with self.subTest('''JIT Disabled'''				):
                             with jax.disable_jit():
                                   lowerCAmelCase					:	Union[str, Any]       =   decode_jitted(**UpperCamelCase_				).to_tuple()

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






     @slow
     def      lowerCamelCase__     (	self  :					Optional[int]				):
           for model_class_name in self.all_model_classes:
                 lowerCAmelCase					:	Optional[int]       =   model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''				)
                 # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                 lowerCAmelCase					:	int       =   np.ones((1, 1)				) * model.config.eos_token_id
                 lowerCAmelCase					:	List[str]       =   model(UpperCamelCase_				)
                 self.assertIsNotNone(UpperCamelCase_				)






     @unittest.skipUnless(jax_device != '''cpu'''					,				'''3B test too slow on CPU.'''				)
     @slow
     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Dict       =   {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 1_5, '''max_length''': 2_5}
           lowerCAmelCase					:	List[str]       =   {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

           lowerCAmelCase					:	Tuple       =   FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''					,				from_pt=UpperCamelCase_				)
           lowerCAmelCase					:	Union[str, Any]       =   BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''				)

           lowerCAmelCase					:	List[Any]       =   ['''Sam''']
           lowerCAmelCase					:	str       =   tokenizer(UpperCamelCase_					,				return_tensors='''jax'''				)

           lowerCAmelCase					:	Union[str, Any]       =   model.generate(**UpperCamelCase_					,				**UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   '''Sam is a great name. It means "sun" in Gaelic.'''

           lowerCAmelCase					:	Union[str, Any]       =   tokenizer.batch_decode(UpperCamelCase_					,				**UpperCamelCase_				)
           assert generated_txt[0].strip() == tgt_text


