


'''simple docstring'''



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,
)


_lowercase      :      Optional[int]            =					logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowercase      :      str            =					"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
"""


def 				lowerCamelCase__				(	A       :      Any				,       A       :      Optional[Any]				,       A       :      Any=8					):




						'''simple docstring'''






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






class 	UpperCamelCase__(       lowerCAmelCase	):




				def __init__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			UNetaDConditionModel				,   lowerCAmelCase      :			DDPMScheduler				,   lowerCAmelCase      :			VQModel				,   )->					List[str]:

										"""simple docstring"""




										super().__init__()

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




				def 					a__(		self      :			int				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			List[Any]				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			str				)->					str:

										"""simple docstring"""




										if latents is None:
																UpperCAmelCase					    =     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}"""				)
																UpperCAmelCase					    =     latents.to(lowerCAmelCase				)

										UpperCAmelCase					    =     latents * scheduler.init_noise_sigma
										return latents




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			Union[str, Any]=0				)->					Optional[Any]:

										"""simple docstring"""




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

										UpperCAmelCase					    =     torch.device(F"""cuda:{gpu_id}"""				)

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




				def 					a__(		self      :			str				,   lowerCAmelCase      :			Union[str, Any]=0				)->					List[str]:

										"""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.'''				)

										UpperCAmelCase					    =     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)

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

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




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 					a__(		self      :			int				)->					Union[str, Any]:

										"""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      :			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				,   )->					int:

										"""simple docstring"""




										UpperCAmelCase					    =     self._execution_device

										UpperCAmelCase					    =     guidance_scale > 1.0

										if isinstance(lowerCAmelCase				,   lowerCAmelCase				):
																UpperCAmelCase					    =     torch.cat(lowerCAmelCase				,   dim=0				)
										UpperCAmelCase					    =     image_embeds.shape[0] * num_images_per_prompt
										if isinstance(lowerCAmelCase				,   lowerCAmelCase				):
																UpperCAmelCase					    =     torch.cat(lowerCAmelCase				,   dim=0				)

										if do_classifier_free_guidance:
																UpperCAmelCase					    =     image_embeds.repeat_interleave(lowerCAmelCase				,   dim=0				)
																UpperCAmelCase					    =     negative_image_embeds.repeat_interleave(lowerCAmelCase				,   dim=0				)

										UpperCAmelCase					    =     torch.cat([negative_image_embeds, image_embeds]				,   dim=0				).to(dtype=self.unet.dtype				,   device=lowerCAmelCase				)

										self.scheduler.set_timesteps(lowerCAmelCase				,   device=lowerCAmelCase				)
										UpperCAmelCase					    =     self.scheduler.timesteps

										UpperCAmelCase					    =     self.unet.config.in_channels

										UpperCAmelCase    ,    UpperCAmelCase					    =     downscale_height_and_width(lowerCAmelCase				,   lowerCAmelCase				,   self.movq_scale_factor				)

										# create initial latent
										UpperCAmelCase					    =     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
																UpperCAmelCase					    =     torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

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

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

																# compute the previous noisy sample x_t -> x_t-1
																UpperCAmelCase					    =     self.scheduler.step(
																    lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   generator=lowerCAmelCase				,   )[0]
										# post-processing
										UpperCAmelCase					    =     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"]:
																UpperCAmelCase					    =     image * 0.5 + 0.5
																UpperCAmelCase					    =     image.clamp(0				,   1				)
																UpperCAmelCase					    =     image.cpu().permute(0				,   2				,   3				,   1				).float().numpy()

										if output_type == "pil":
																UpperCAmelCase					    =     self.numpy_to_pil(lowerCAmelCase				)

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase				)









'''simple docstring'''



import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
	import os

	import jax.numpy as jnp
	from jax import jit

	from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
	from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

	_lowercase      :      List[str]            =					"""0.12"""  # assumed parallelism: 8


if is_torch_available():
	import torch


def 				lowerCamelCase__				(	A       :      str				,       A       :      str				,       A       :      List[Any]=None					):




						'''simple docstring'''






						if rng is None:
												UpperCAmelCase					    =     random.Random()

						UpperCAmelCase					    =     1
						for dim in shape:
												total_dims *= dim

						UpperCAmelCase					    =     []
						for _ in range(A					):
												values.append(rng.randint(0				,       vocab_size - 1					)					)

						UpperCAmelCase					    =     np.array(A				,       dtype=jnp.intaa					).reshape(A					)

						return output


def 				lowerCamelCase__				(	A       :      int				,       A       :      Optional[int]=None					):




						'''simple docstring'''






						UpperCAmelCase					    =     ids_tensor(A				,       vocab_size=2				,       rng=A					)
						# make sure that at least one token is attended to for each batch
						UpperCAmelCase					    =     1
						return attn_mask






@require_flax
class 	UpperCamelCase__:
				__magic_name__    :				Optional[int]  		=	None
				__magic_name__    :				Optional[Any]  		=	()




				def 					a__(		self      :			str				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

										# cut to half length & take max batch_size 3
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     inputs['''input_ids'''].shape[-1] // 2
										UpperCAmelCase					    =     inputs['''input_ids'''][:max_batch_size, :sequence_length]

										UpperCAmelCase					    =     jnp.ones_like(lowerCAmelCase				)
										UpperCAmelCase					    =     attention_mask[:max_batch_size, :sequence_length]

										# generate max 5 tokens
										UpperCAmelCase					    =     input_ids.shape[-1] + 5
										if config.eos_token_id is not None and config.pad_token_id is None:
																# hack to allow generate for models such as GPT2 as is done in `generate()`
																UpperCAmelCase					    =     config.eos_token_id
										return config, input_ids, attention_mask, max_length




				@is_pt_flax_cross_test
				def 					a__(		self      :			Dict				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     0

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
																UpperCAmelCase					    =     getattr(lowerCAmelCase				,   lowerCAmelCase				)
																UpperCAmelCase					    =     pt_model_class(lowerCAmelCase				).eval()
																UpperCAmelCase					    =     load_flax_weights_in_pytorch_model(lowerCAmelCase				,   flax_model.params				)

																UpperCAmelCase					    =     flax_model.generate(lowerCAmelCase				).sequences
																UpperCAmelCase					    =     pt_model.generate(torch.tensor(lowerCAmelCase				,   dtype=torch.long				)				)

																if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																						UpperCAmelCase					    =     flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																self.assertListEqual(pt_generation_outputs.numpy().tolist()				,   flax_generation_outputs.tolist()				)




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			str				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     2

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[0]				,   input_ids.shape[0] * config.num_return_sequences				)




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     0.8
										UpperCAmelCase					    =     10
										UpperCAmelCase					    =     0.3
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Tuple				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

										# pad attention mask on the left
										UpperCAmelCase					    =     attention_mask.at[(0, 0)].set(0				)

										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

										# pad attention mask on the left
										UpperCAmelCase					    =     attention_mask.at[(0, 0)].set(0				)

										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)





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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

										# pad attention mask on the left
										UpperCAmelCase					    =     attention_mask.at[(0, 0)].set(0				)

										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)







@require_flax
class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			Union[str, Any]				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''				)
										UpperCAmelCase					    =     FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)

										UpperCAmelCase					    =     '''Hello world'''
										UpperCAmelCase					    =     tokenizer(lowerCAmelCase				,   return_tensors='''np'''				).input_ids

										# typos are quickly detected (the correct argument is `do_sample`)
										with self.assertRaisesRegex(lowerCAmelCase				,   '''do_samples'''				):
																model.generate(lowerCAmelCase				,   do_samples=lowerCAmelCase				)

										# arbitrary arguments that will not be used anywhere are also not accepted
										with self.assertRaisesRegex(lowerCAmelCase				,   '''foo'''				):
																UpperCAmelCase					    =     {'''foo''': '''bar'''}
																model.generate(lowerCAmelCase				,   **lowerCAmelCase				)





