




from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


UpperCAmelCase__     :   Union[str, Any]				     = logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase__     :   List[Any]				     = "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Any=8				)    -> List[Any]:
							'''simple docstring'''


							__lowercase      =    height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							__lowercase      =    width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def __init__( self :     List[Any]				,							lowerCamelCase__ :     UNetaDConditionModel				,							lowerCamelCase__ :     DDPMScheduler				,							lowerCamelCase__ :     VQModel				,							)    ->						Any:
													"""simple docstring"""


													super().__init__()

													self.register_modules(
													    unet=lowerCamelCase__				,							scheduler=lowerCamelCase__				,							movq=lowerCamelCase__				,							)
													__lowercase      =    2 ** (len(self.movq.config.block_out_channels				) - 1)






						def     UpperCAmelCase_      ( self :     List[Any]				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     Dict				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Any				)    ->						List[str]:
													"""simple docstring"""


													if latents is None:
																				__lowercase      =    randn_tensor(lowerCamelCase__				,							generator=lowerCamelCase__				,							device=lowerCamelCase__				,							dtype=lowerCamelCase__				)
													else:
																				if latents.shape != shape:
																											raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'				)
																				__lowercase      =    latents.to(lowerCamelCase__				)

													__lowercase      =    latents * scheduler.init_noise_sigma
													return latents






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     int=0				)    ->						Optional[Any]:
													"""simple docstring"""


													if is_accelerate_available():
																				from accelerate import cpu_offload
													else:
																				raise ImportError('''Please install accelerate via `pip install accelerate`'''				)

													__lowercase      =    torch.device(f'cuda:{gpu_id}'				)

													__lowercase      =    [
													    self.unet,
													    self.movq,
													]
													for cpu_offloaded_model in models:
																				if cpu_offloaded_model is not None:
																											cpu_offload(lowerCamelCase__				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     List[Any]=0				)    ->						Union[str, Any]:
													"""simple docstring"""


													if is_accelerate_available() and is_accelerate_version('''>='''				,							'''0.17.0.dev0'''				):
																				from accelerate import cpu_offload_with_hook
													else:
																				raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''				)

													__lowercase      =    torch.device(f'cuda:{gpu_id}'				)

													if self.device.type != "cpu":
																				self.to('''cpu'''				,							silence_dtype_warnings=lowerCamelCase__				)
																				torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

													__lowercase      =    None
													for cpu_offloaded_model in [self.unet, self.movq]:
																				__lowercase     ,     __lowercase      =    cpu_offload_with_hook(lowerCamelCase__				,							lowerCamelCase__				,							prev_module_hook=lowerCamelCase__				)

													# We'll offload the last model manually.
													__lowercase      =    hook






						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Optional[int]:
													"""simple docstring"""


													if not hasattr(self.unet				,							'''_hf_hook'''				):
																				return self.device
													for module in self.unet.modules():
																				if (
																				    hasattr(lowerCamelCase__				,							'''_hf_hook'''				)
																				    and hasattr(module._hf_hook				,							'''execution_device'''				)
																				    and module._hf_hook.execution_device is not None
																				):
																											return torch.device(module._hf_hook.execution_device				)
													return self.device






						@torch.no_grad()
						@replace_example_docstring(lowerCamelCase__				)
						def __call__( self :     Union[str, Any]				,							lowerCamelCase__ :     Union[torch.FloatTensor, List[torch.FloatTensor]]				,							lowerCamelCase__ :     Union[torch.FloatTensor, List[torch.FloatTensor]]				,							lowerCamelCase__ :     torch.FloatTensor				,							lowerCamelCase__ :     int = 512				,							lowerCamelCase__ :     int = 512				,							lowerCamelCase__ :     int = 100				,							lowerCamelCase__ :     float = 4.0				,							lowerCamelCase__ :     int = 1				,							lowerCamelCase__ :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							lowerCamelCase__ :     Optional[torch.FloatTensor] = None				,							lowerCamelCase__ :     Optional[str] = "pil"				,							lowerCamelCase__ :     bool = True				,							)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    self._execution_device

													__lowercase      =    guidance_scale > 1.0

													if isinstance(lowerCamelCase__				,							lowerCamelCase__				):
																				__lowercase      =    torch.cat(lowerCamelCase__				,							dim=0				)
													if isinstance(lowerCamelCase__				,							lowerCamelCase__				):
																				__lowercase      =    torch.cat(lowerCamelCase__				,							dim=0				)
													if isinstance(lowerCamelCase__				,							lowerCamelCase__				):
																				__lowercase      =    torch.cat(lowerCamelCase__				,							dim=0				)

													__lowercase      =    image_embeds.shape[0] * num_images_per_prompt

													if do_classifier_free_guidance:
																				__lowercase      =    image_embeds.repeat_interleave(lowerCamelCase__				,							dim=0				)
																				__lowercase      =    negative_image_embeds.repeat_interleave(lowerCamelCase__				,							dim=0				)
																				__lowercase      =    hint.repeat_interleave(lowerCamelCase__				,							dim=0				)

													__lowercase      =    torch.cat([negative_image_embeds, image_embeds]				,							dim=0				).to(dtype=self.unet.dtype				,							device=lowerCamelCase__				)
													__lowercase      =    torch.cat([hint, hint]				,							dim=0				).to(dtype=self.unet.dtype				,							device=lowerCamelCase__				)
													self.scheduler.set_timesteps(lowerCamelCase__				,							device=lowerCamelCase__				)
													__lowercase      =    self.scheduler.timesteps

													__lowercase      =    self.movq.config.latent_channels

													__lowercase     ,     __lowercase      =    downscale_height_and_width(lowerCamelCase__				,							lowerCamelCase__				,							self.movq_scale_factor				)

													# create initial latent
													__lowercase      =    self.prepare_latents(
													    (batch_size, num_channels_latents, height, width)				,							image_embeds.dtype				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							self.scheduler				,							)

													for i, t in enumerate(self.progress_bar(lowerCamelCase__				)				):
																				# expand the latents if we are doing classifier free guidance
																				__lowercase      =    torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

																				__lowercase      =    {'''image_embeds''': image_embeds, '''hint''': hint}
																				__lowercase      =    self.unet(
																				    sample=lowerCamelCase__				,							timestep=lowerCamelCase__				,							encoder_hidden_states=lowerCamelCase__				,							added_cond_kwargs=lowerCamelCase__				,							return_dict=lowerCamelCase__				,							)[0]

																				if do_classifier_free_guidance:
																											__lowercase     ,     __lowercase      =    noise_pred.split(latents.shape[1]				,							dim=1				)
																											__lowercase     ,     __lowercase      =    noise_pred.chunk(2				)
																											__lowercase     ,     __lowercase      =    variance_pred.chunk(2				)
																											__lowercase      =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																											__lowercase      =    torch.cat([noise_pred, variance_pred_text]				,							dim=1				)

																				if not (
																				    hasattr(self.scheduler.config				,							'''variance_type'''				)
																				    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																				):
																											__lowercase     ,     __lowercase      =    noise_pred.split(latents.shape[1]				,							dim=1				)

																				# compute the previous noisy sample x_t -> x_t-1
																				__lowercase      =    self.scheduler.step(
																				    lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							generator=lowerCamelCase__				,							)[0]
													# post-processing
													__lowercase      =    self.movq.decode(lowerCamelCase__				,							force_not_quantize=lowerCamelCase__				)['''sample''']

													if output_type not in ["pt", "np", "pil"]:
																				raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'				)

													if output_type in ["np", "pil"]:
																				__lowercase      =    image * 0.5 + 0.5
																				__lowercase      =    image.clamp(0				,							1				)
																				__lowercase      =    image.cpu().permute(0				,							2				,							3				,							1				).float().numpy()

													if output_type == "pil":
																				__lowercase      =    self.numpy_to_pil(lowerCamelCase__				)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=lowerCamelCase__				)












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
			UpperCAmelCase__				     = "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 		_A(       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Dict=None				,       UpperCamelCase__							:			Optional[int]=None				,       UpperCamelCase__							:			int=None				,       UpperCamelCase__							:			Union[str, Any]=None				,       UpperCamelCase__							:			Optional[Any]=None				,       UpperCamelCase__							:			List[str]=None				,       )    -> int:
							'''simple docstring'''


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

						def __init__( self :     Dict				,							lowerCamelCase__ :     Dict				,							lowerCamelCase__ :     Union[str, Any]=13				,							lowerCamelCase__ :     Union[str, Any]=7				,							lowerCamelCase__ :     Dict=True				,							lowerCamelCase__ :     int=False				,							lowerCamelCase__ :     Any=99				,							lowerCamelCase__ :     str=16				,							lowerCamelCase__ :     Union[str, Any]=2				,							lowerCamelCase__ :     Union[str, Any]=4				,							lowerCamelCase__ :     Tuple=4				,							lowerCamelCase__ :     Optional[Any]="gelu"				,							lowerCamelCase__ :     int=0.1				,							lowerCamelCase__ :     Tuple=0.1				,							lowerCamelCase__ :     Any=32				,							lowerCamelCase__ :     List[str]=2				,							lowerCamelCase__ :     Optional[Any]=1				,							lowerCamelCase__ :     List[Any]=0				,							lowerCamelCase__ :     int=0.0_2				,							)    ->						Optional[int]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     int				)    ->						Any:
													"""simple docstring"""


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

													__lowercase      =    shift_tokens_right(lowerCamelCase__				,							1				,							2				)

													__lowercase      =    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__				,							)
													__lowercase      =    prepare_blenderbot_inputs_dict(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)
													return config, inputs_dict






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    self.prepare_config_and_inputs()
													return config, inputs_dict






						def     UpperCAmelCase_      ( self :     Optional[Any]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     Tuple				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    20
													__lowercase      =    model_class_name(lowerCamelCase__				)

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

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

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

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

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

													__lowercase      =    model.decode(lowerCamelCase__				,							lowerCamelCase__				)

													__lowercase      =    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     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     List[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    20
													__lowercase      =    model_class_name(lowerCamelCase__				)

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

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

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

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

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

													__lowercase      =    model.decode(lowerCamelCase__				,							lowerCamelCase__				,							decoder_attention_mask=lowerCamelCase__				)

													__lowercase      =    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  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[Any]       	=		99






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    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      =    input_ids.shape[0]
													__lowercase      =    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     UpperCAmelCase_      ( self :     Tuple				)    ->						Optional[int]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						List[Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				,							dtype=np.intaa				)
													__lowercase      =    shift_tokens_right(lowerCamelCase__				,							1				,							2				)
													__lowercase      =    np.equal(lowerCamelCase__				,							1				).astype(np.floataa				).sum()
													__lowercase      =    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 (     __SCREAMING_SNAKE_CASE       ,						unittest.TestCase       ,						__SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       Optional[Any]       	=		True
						UpperCamelCase_	:       List[str]       	=		(
						    (
						        FlaxBlenderbotModel,
						        FlaxBlenderbotForConditionalGeneration,
						    )
						    if is_flax_available()
						    else ()
						)
						UpperCamelCase_	:       Union[str, Any]       	=		(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    FlaxBlenderbotModelTester(self				)






						def     UpperCAmelCase_      ( self :     Any				)    ->						List[str]:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    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     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    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     UpperCAmelCase_      ( self :     List[str]				)    ->						int:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				with self.subTest(model_class.__name__				):
																											__lowercase      =    self._prepare_for_class(lowerCamelCase__				,							lowerCamelCase__				)
																											__lowercase      =    model_class(lowerCamelCase__				)

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

																											with self.subTest('''JIT Enabled'''				):
																																		__lowercase      =    encode_jitted(**lowerCamelCase__				).to_tuple()

																											with self.subTest('''JIT Disabled'''				):
																																		with jax.disable_jit():
																																									__lowercase      =    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     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    self.model_tester.prepare_config_and_inputs_for_common()

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

																											__lowercase      =    {
																											    '''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__ :     List[str]				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     Tuple				):
																																		return model.decode(
																																		    decoder_input_ids=lowerCamelCase__				,							decoder_attention_mask=lowerCamelCase__				,							encoder_outputs=lowerCamelCase__				,							)

																											with self.subTest('''JIT Enabled'''				):
																																		__lowercase      =    decode_jitted(**lowerCamelCase__				).to_tuple()

																											with self.subTest('''JIT Disabled'''				):
																																		with jax.disable_jit():
																																									__lowercase      =    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     UpperCAmelCase_      ( self :     int				)    ->						Union[str, Any]:
													"""simple docstring"""


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






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


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

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

													__lowercase      =    ['''Sam''']
													__lowercase      =    tokenizer(lowerCamelCase__				,							return_tensors='''jax'''				)

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

													__lowercase      =    tokenizer.batch_decode(lowerCamelCase__				,							**lowerCamelCase__				)
													assert generated_txt[0].strip() == tgt_text






