

'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
							import torch


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






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     ["""pixel_values"""]



				def __init__(    self,     _lowercase = True,     _lowercase = None,     _lowercase = PILImageResampling.BILINEAR,     _lowercase = True,     _lowercase = None,     _lowercase = True,     _lowercase = 1 / 255,     _lowercase = True,     _lowercase = None,     _lowercase = None,     **_lowercase,     )     ->							None:
											super().__init__(**_lowercase       )
											SCREAMING_SNAKE_CASE_				=				size if size is not None else {'shortest_edge': 256}
											SCREAMING_SNAKE_CASE_				=				get_size_dict(_lowercase,     default_to_square=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				crop_size if crop_size is not None else {'height': 224, 'width': 224}
											SCREAMING_SNAKE_CASE_				=				get_size_dict(_lowercase,     param_name='crop_size'       )
											SCREAMING_SNAKE_CASE_				=				do_resize
											SCREAMING_SNAKE_CASE_				=				size
											SCREAMING_SNAKE_CASE_				=				resample
											SCREAMING_SNAKE_CASE_				=				do_center_crop
											SCREAMING_SNAKE_CASE_				=				crop_size
											SCREAMING_SNAKE_CASE_				=				do_rescale
											SCREAMING_SNAKE_CASE_				=				rescale_factor
											SCREAMING_SNAKE_CASE_				=				do_normalize
											SCREAMING_SNAKE_CASE_				=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											SCREAMING_SNAKE_CASE_				=				image_std if image_std is not None else IMAGENET_STANDARD_STD



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase = PILImageResampling.BICUBIC,     _lowercase = None,     **_lowercase,     )     ->							np.ndarray:
											SCREAMING_SNAKE_CASE_				=				get_size_dict(_lowercase,     default_to_square=_lowercase       )
											if "shortest_edge" not in size:
																		raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""       )
											SCREAMING_SNAKE_CASE_				=				get_resize_output_image_size(_lowercase,     size=size['shortest_edge'],     default_to_square=_lowercase       )
											return resize(_lowercase,     size=_lowercase,     resample=_lowercase,     data_format=_lowercase,     **_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase = None,     **_lowercase,     )     ->							np.ndarray:
											SCREAMING_SNAKE_CASE_				=				get_size_dict(_lowercase       )
											if "height" not in size or "width" not in size:
																		raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""       )
											return center_crop(_lowercase,     size=(size['height'], size['width']),     data_format=_lowercase,     **_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase = None,     **_lowercase       )     ->							np.ndarray:
											return rescale(_lowercase,     scale=_lowercase,     data_format=_lowercase,     **_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase = None,     **_lowercase,     )     ->							np.ndarray:
											return normalize(_lowercase,     mean=_lowercase,     std=_lowercase,     data_format=_lowercase,     **_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = ChannelDimension.FIRST,     **_lowercase,     )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				do_resize if do_resize is not None else self.do_resize
											SCREAMING_SNAKE_CASE_				=				size if size is not None else self.size
											SCREAMING_SNAKE_CASE_				=				get_size_dict(_lowercase,     default_to_square=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				resample if resample is not None else self.resample
											SCREAMING_SNAKE_CASE_				=				do_center_crop if do_center_crop is not None else self.do_center_crop
											SCREAMING_SNAKE_CASE_				=				crop_size if crop_size is not None else self.crop_size
											SCREAMING_SNAKE_CASE_				=				get_size_dict(_lowercase,     param_name='crop_size'       )
											SCREAMING_SNAKE_CASE_				=				do_rescale if do_rescale is not None else self.do_rescale
											SCREAMING_SNAKE_CASE_				=				rescale_factor if rescale_factor is not None else self.rescale_factor
											SCREAMING_SNAKE_CASE_				=				do_normalize if do_normalize is not None else self.do_normalize
											SCREAMING_SNAKE_CASE_				=				image_mean if image_mean is not None else self.image_mean
											SCREAMING_SNAKE_CASE_				=				image_std if image_std is not None else self.image_std

											SCREAMING_SNAKE_CASE_				=				make_list_of_images(_lowercase       )

											if not valid_images(_lowercase       ):
																		raise ValueError(
																		    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																		    'torch.Tensor, tf.Tensor or jax.ndarray.'       )

											if do_resize and size is None:
																		raise ValueError('Size must be specified if do_resize is True.'       )

											if do_center_crop and crop_size is None:
																		raise ValueError('Crop size must be specified if do_center_crop is True.'       )

											if do_rescale and rescale_factor is None:
																		raise ValueError('Rescale factor must be specified if do_rescale is True.'       )

											if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('Image mean and std must be specified if do_normalize is True.'       )

											# All transformations expect numpy arrays.
											SCREAMING_SNAKE_CASE_				=				[to_numpy_array(_lowercase       ) for image in images]

											if do_resize:
																		SCREAMING_SNAKE_CASE_				=				[self.resize(image=_lowercase,     size=_lowercase,     resample=_lowercase       ) for image in images]

											if do_center_crop:
																		SCREAMING_SNAKE_CASE_				=				[self.center_crop(image=_lowercase,     size=_lowercase       ) for image in images]

											if do_rescale:
																		SCREAMING_SNAKE_CASE_				=				[self.rescale(image=_lowercase,     scale=_lowercase       ) for image in images]

											if do_normalize:
																		SCREAMING_SNAKE_CASE_				=				[self.normalize(image=_lowercase,     mean=_lowercase,     std=_lowercase       ) for image in images]

											SCREAMING_SNAKE_CASE_				=				[to_channel_dimension_format(_lowercase,     _lowercase       ) for image in images]

											SCREAMING_SNAKE_CASE_				=				{'pixel_values': images}
											return BatchFeature(data=_lowercase,     tensor_type=_lowercase       )





				def   a__					(    self,     _lowercase,     _lowercase = None       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				outputs.logits

											# Resize logits and compute semantic segmentation maps
											if target_sizes is not None:
																		if len(_lowercase       ) != len(_lowercase       ):
																									raise ValueError(
																									    'Make sure that you pass in as many target sizes as the batch dimension of the logits'       )

																		if is_torch_tensor(_lowercase       ):
																									SCREAMING_SNAKE_CASE_				=				target_sizes.numpy()

																		SCREAMING_SNAKE_CASE_				=				[]

																		for idx in range(len(_lowercase       )       ):
																									SCREAMING_SNAKE_CASE_				=				torch.nn.functional.interpolate(
																									    logits[idx].unsqueeze(dim=0       ),     size=target_sizes[idx],     mode='bilinear',     align_corners=_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				resized_logits[0].argmax(dim=0       )
																									semantic_segmentation.append(_lowercase       )
											else:
																		SCREAMING_SNAKE_CASE_				=				logits.argmax(dim=1       )
																		SCREAMING_SNAKE_CASE_				=				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

											return semantic_segmentation








'''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

							SCREAMING_SNAKE_CASE							:				List[Any]     	=			"0.12"  # assumed parallelism: 8


if is_torch_available():
							import torch




def 							_UpperCamelCase				(   lowerCAmelCase__:					Dict		,lowerCAmelCase__:					int		,lowerCAmelCase__:					Tuple=None   )      -> Union[str, Any]:
							if rng is None:
														SCREAMING_SNAKE_CASE_				=				random.Random()

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

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

							SCREAMING_SNAKE_CASE_				=				np.array(lowerCAmelCase__		,dtype=jnp.intaa   ).reshape(lowerCAmelCase__   )

							return output




def 							_UpperCamelCase				(   lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					str=None   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				ids_tensor(lowerCAmelCase__		,vocab_size=2		,rng=lowerCAmelCase__   )
							# make sure that at least one token is attended to for each batch
							SCREAMING_SNAKE_CASE_				=				1
							return attn_mask






@require_flax
class    snake_case     :






				"""simple docstring"""
				_a    	=     None
				_a    	=     ()



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.model_tester.prepare_config_and_inputs_for_common()

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

											SCREAMING_SNAKE_CASE_				=				jnp.ones_like(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				attention_mask[:max_batch_size, :sequence_length]

											# generate max 5 tokens
											SCREAMING_SNAKE_CASE_				=				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_				=				config.eos_token_id
											return config, input_ids, attention_mask, max_length



				@is_pt_flax_cross_test
				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				0

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				flax_model.generate(_lowercase       ).sequences
																		SCREAMING_SNAKE_CASE_				=				pt_model.generate(torch.tensor(_lowercase,     dtype=torch.long       )       )

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

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



				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

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



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

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



				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				2

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

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



				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				2

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

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



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				0.8
											SCREAMING_SNAKE_CASE_				=				10
											SCREAMING_SNAKE_CASE_				=				0.3
											SCREAMING_SNAKE_CASE_				=				1
											SCREAMING_SNAKE_CASE_				=				8
											SCREAMING_SNAKE_CASE_				=				9

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

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



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				1
											SCREAMING_SNAKE_CASE_				=				8
											SCREAMING_SNAKE_CASE_				=				9

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

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



				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				1
											SCREAMING_SNAKE_CASE_				=				8
											SCREAMING_SNAKE_CASE_				=				9

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

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



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()

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

											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase,     attention_mask=_lowercase       ).sequences

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



				def   a__					(    self       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()

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

											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase,     attention_mask=_lowercase       ).sequences

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





				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()

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

											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase,     attention_mask=_lowercase       ).sequences

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






@require_flax
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'       )
											SCREAMING_SNAKE_CASE_				=				FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'       )

											SCREAMING_SNAKE_CASE_				=				'Hello world'
											SCREAMING_SNAKE_CASE_				=				tokenizer(_lowercase,     return_tensors='np'       ).input_ids

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

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





