

from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						import torch

						from transformers.modeling_outputs import BaseModelOutput

						from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowerCAmelCase							:  Optional[Any]   	=						logging.get_logger(__name__)



@add_end_docstrings(UpperCAmelCase_	)
class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def __init__( self  :     int   ,						**lowerCAmelCase__  :     Tuple):
									super().__init__(**lowerCAmelCase__)

									if self.framework == "tf":
															raise ValueError(F"The {self.__class__} is only available in PyTorch.")

									requires_backends(self   ,						"vision")
									self.check_model_type(lowerCAmelCase__)



			def __call__( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Union[str, "Image.Image", List[Dict[str, Any]]]   ,						lowerCAmelCase__  :     Union[str, List[str]] = None   ,						**lowerCAmelCase__  :     Optional[int]   ,						):
									if "text_queries" in kwargs:
															SCREAMING_SNAKE_CASE_: Tuple  		=       kwargs.pop("text_queries")

									if isinstance(lowerCAmelCase__   ,						(str, Image.Image)):
															SCREAMING_SNAKE_CASE_: str  		=       {"image": image, "candidate_labels": candidate_labels}
									else:
															SCREAMING_SNAKE_CASE_: Tuple  		=       image
									SCREAMING_SNAKE_CASE_: List[Any]  		=       super().__call__(lowerCAmelCase__   ,						**lowerCAmelCase__)
									return results



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						**lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       {}
									if "threshold" in kwargs:
															SCREAMING_SNAKE_CASE_: str  		=       kwargs["threshold"]
									if "top_k" in kwargs:
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       kwargs["top_k"]
									return {}, {}, postprocess_params



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       load_image(inputs["image"])
									SCREAMING_SNAKE_CASE_: Tuple  		=       inputs["candidate_labels"]
									if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: int  		=       candidate_labels.split(",")

									SCREAMING_SNAKE_CASE_: str  		=       torch.tensor([[image.height, image.width]]   ,						dtype=torch.intaa)
									for i, candidate_label in enumerate(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: List[Any]  		=       self.tokenizer(lowerCAmelCase__   ,						return_tensors=self.framework)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.image_processor(lowerCAmelCase__   ,						return_tensors=self.framework)
															yield {
															    "is_last": i == len(lowerCAmelCase__) - 1,
															    "target_size": target_size,
															    "candidate_label": candidate_label,
															    **text_inputs,
															    **image_features,
															}



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: int  		=       model_inputs.pop("target_size")
									SCREAMING_SNAKE_CASE_: List[str]  		=       model_inputs.pop("candidate_label")
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       model_inputs.pop("is_last")

									SCREAMING_SNAKE_CASE_: List[str]  		=       self.model(**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: str  		=       {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
									return model_outputs



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     List[Any]=0.1   ,						lowerCAmelCase__  :     Dict=None):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       []
									for model_output in model_outputs:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model_output["candidate_label"]
															SCREAMING_SNAKE_CASE_: Any  		=       BaseModelOutput(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.image_processor.post_process_object_detection(
															    outputs=lowerCAmelCase__   ,						threshold=lowerCAmelCase__   ,						target_sizes=model_output["target_size"])[0]

															for index in outputs["scores"].nonzero():
																					SCREAMING_SNAKE_CASE_: int  		=       outputs["scores"][index].item()
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self._get_bounding_box(outputs["boxes"][index][0])

																					SCREAMING_SNAKE_CASE_: Tuple  		=       {"score": score, "label": label, "box": box}
																					results.append(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       sorted(lowerCAmelCase__   ,						key=lambda lowerCAmelCase__: x["score"]   ,						reverse=lowerCAmelCase__)
									if top_k:
															SCREAMING_SNAKE_CASE_: int  		=       results[:top_k]

									return results




			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     "torch.Tensor"):
									if self.framework != "pt":
															raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch.")
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       box.int().tolist()
									SCREAMING_SNAKE_CASE_: Dict  		=       {
									    "xmin": xmin,
									    "ymin": ymin,
									    "xmax": xmax,
									    "ymax": ymax,
									}
									return bbox









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

						lowerCAmelCase							:  Any   	=						"""0.12"""  # assumed parallelism: 8


if is_torch_available():
						import torch







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=None    ):
						if rng is None:
												SCREAMING_SNAKE_CASE_: List[Any]  		=       random.Random()

						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       1
						for dim in shape:
												total_dims *= dim

						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       []
						for _ in range(_UpperCAmelCase    ):
												values.append(rng.randint(0				,	vocab_size - 1    )    )

						SCREAMING_SNAKE_CASE_: List[Any]  		=       np.array(_UpperCAmelCase				,	dtype=jnp.intaa    ).reshape(_UpperCAmelCase    )

						return output







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=None    ):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       ids_tensor(_UpperCAmelCase				,	vocab_size=2				,	rng=_UpperCAmelCase    )
						# make sure that at least one token is attended to for each batch
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       1
						return attn_mask



@require_flax
class   __lowercase				:




			"""simple docstring"""







			_UpperCAmelCase   :							Any										=     None
			_UpperCAmelCase   :							List[Any]										=     ()



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self.model_tester.prepare_config_and_inputs_for_common()

									# cut to half length & take max batch_size 3
									SCREAMING_SNAKE_CASE_: str  		=       2
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       inputs["input_ids"].shape[-1] // 2
									SCREAMING_SNAKE_CASE_: List[str]  		=       inputs["input_ids"][:max_batch_size, :sequence_length]

									SCREAMING_SNAKE_CASE_: Any  		=       jnp.ones_like(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       attention_mask[:max_batch_size, :sequence_length]

									# generate max 5 tokens
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       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()`
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       config.eos_token_id
									return config, input_ids, attention_mask, max_length



			@is_pt_flax_cross_test
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       False
									SCREAMING_SNAKE_CASE_: Dict  		=       max_length
									SCREAMING_SNAKE_CASE_: List[Any]  		=       0

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: int  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Tuple  		=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
															SCREAMING_SNAKE_CASE_: List[Any]  		=       getattr(lowerCAmelCase__   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       pt_model_class(lowerCAmelCase__).eval()
															SCREAMING_SNAKE_CASE_: str  		=       load_flax_weights_in_pytorch_model(lowerCAmelCase__   ,						flax_model.params)

															SCREAMING_SNAKE_CASE_: List[Any]  		=       flax_model.generate(lowerCAmelCase__).sequences
															SCREAMING_SNAKE_CASE_: str  		=       pt_model.generate(torch.tensor(lowerCAmelCase__   ,						dtype=torch.long))

															if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																					SCREAMING_SNAKE_CASE_: List[Any]  		=       flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       False
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: List[Any]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       jit_generate(lowerCAmelCase__).sequences

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



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       True
									SCREAMING_SNAKE_CASE_: Dict  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Tuple  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: int  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: List[str]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Dict  		=       jit_generate(lowerCAmelCase__).sequences

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: int  		=       False
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       max_length
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       2

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: List[str]  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Any  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       jit_generate(lowerCAmelCase__).sequences

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: str  		=       False
									SCREAMING_SNAKE_CASE_: int  		=       max_length
									SCREAMING_SNAKE_CASE_: str  		=       2
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       2

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: str  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[0]   ,						input_ids.shape[0] * config.num_return_sequences)



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Tuple  		=       True
									SCREAMING_SNAKE_CASE_: List[str]  		=       max_length
									SCREAMING_SNAKE_CASE_: Any  		=       0.8
									SCREAMING_SNAKE_CASE_: Any  		=       10
									SCREAMING_SNAKE_CASE_: List[str]  		=       0.3
									SCREAMING_SNAKE_CASE_: Tuple  		=       1
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       8
									SCREAMING_SNAKE_CASE_: int  		=       9

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: List[str]  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Dict  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[Any]  		=       jit_generate(lowerCAmelCase__).sequences

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



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Any  		=       max_length
									SCREAMING_SNAKE_CASE_: int  		=       1
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       8
									SCREAMING_SNAKE_CASE_: List[Any]  		=       9

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: int  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[str]  		=       jit_generate(lowerCAmelCase__).sequences

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



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Any  		=       max_length
									SCREAMING_SNAKE_CASE_: List[str]  		=       2
									SCREAMING_SNAKE_CASE_: str  		=       1
									SCREAMING_SNAKE_CASE_: Tuple  		=       8
									SCREAMING_SNAKE_CASE_: List[Any]  		=       9

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: int  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[str]  		=       jit_generate(lowerCAmelCase__).sequences

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



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()

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

									SCREAMING_SNAKE_CASE_: Dict  		=       False
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Any  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: List[Any]  		=       model.generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[Any]  		=       jit_generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self._get_input_ids_and_config()

									# pad attention mask on the left
									SCREAMING_SNAKE_CASE_: List[Any]  		=       attention_mask.at[(0, 0)].set(0)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       True
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: str  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Dict  		=       model.generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       jit_generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences

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




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()

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

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       2
									SCREAMING_SNAKE_CASE_: Any  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Tuple  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model.generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       jit_generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences

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







@require_flax
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert")
									SCREAMING_SNAKE_CASE_: List[Any]  		=       FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only")

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       "Hello world"
									SCREAMING_SNAKE_CASE_: List[Any]  		=       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"):
															SCREAMING_SNAKE_CASE_: str  		=       {"foo": "bar"}
															model.generate(lowerCAmelCase__   ,						**lowerCAmelCase__)






