


import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
	import jax

	from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
	from transformers.models.bert.modeling_flax_bert import FlaxBertModel
	from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					@slow
					def A   (					self    ) ->      Union[str, Any]:







								'''simple docstring'''

								for model_name in ["bert-base-cased", "bert-large-uncased"]:
											with self.subTest(snake_case_    ):
														__lowercase			       = AutoConfig.from_pretrained(snake_case_    )
														self.assertIsNotNone(snake_case_    )
														self.assertIsInstance(snake_case_			,							snake_case_    )

														__lowercase			       = FlaxAutoModel.from_pretrained(snake_case_    )
														self.assertIsNotNone(snake_case_    )
														self.assertIsInstance(snake_case_			,							snake_case_    )






					@slow
					def A   (					self    ) ->      int:







								'''simple docstring'''

								for model_name in ["roberta-base", "roberta-large"]:
											with self.subTest(snake_case_    ):
														__lowercase			       = AutoConfig.from_pretrained(snake_case_    )
														self.assertIsNotNone(snake_case_    )
														self.assertIsInstance(snake_case_			,							snake_case_    )

														__lowercase			       = FlaxAutoModel.from_pretrained(snake_case_    )
														self.assertIsNotNone(snake_case_    )
														self.assertIsInstance(snake_case_			,							snake_case_    )






					@slow
					def A   (					self    ) ->      Union[str, Any]:







								'''simple docstring'''

								for model_name in ["bert-base-cased", "bert-large-uncased"]:
											__lowercase			       = AutoTokenizer.from_pretrained(snake_case_    )
											__lowercase			       = FlaxBertModel.from_pretrained(snake_case_    )
											__lowercase			       = tokenizer('''Do you support jax jitted function?'''			,							return_tensors=TensorType.JAX    )

											@jax.jit
											def eval(**snake_case_    ):
														return model(**snake_case_    )

											eval(**snake_case_    ).block_until_ready()






					@slow
					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								for model_name in ["roberta-base", "roberta-large"]:
											__lowercase			       = AutoTokenizer.from_pretrained(snake_case_    )
											__lowercase			       = FlaxRobertaModel.from_pretrained(snake_case_    )
											__lowercase			       = tokenizer('''Do you support jax jitted function?'''			,							return_tensors=TensorType.JAX    )

											@jax.jit
											def eval(**snake_case_    ):
														return model(**snake_case_    )

											eval(**snake_case_    ).block_until_ready()






					def A   (					self    ) ->      str:







								'''simple docstring'''

								with self.assertRaisesRegex(
								    snake_case_			,							'''bert-base is not a local folder and is not a valid model identifier'''    ):
											__lowercase			       = FlaxAutoModel.from_pretrained('''bert-base'''    )






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







								'''simple docstring'''

								with self.assertRaisesRegex(
								    snake_case_			,							r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''    ):
											__lowercase			       = FlaxAutoModel.from_pretrained(snake_case_			,							revision='''aaaaaa'''    )






					def A   (					self    ) ->      Any:







								'''simple docstring'''

								with self.assertRaisesRegex(
								    snake_case_			,							'''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'''			,							):
											__lowercase			       = FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''    )






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







								'''simple docstring'''

								with self.assertRaisesRegex(snake_case_			,							'''Use `from_pt=True` to load this model'''    ):
											__lowercase			       = FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''    )






from typing import Dict, List, Optional, 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_vision_available, logging


if is_vision_available():
	import PIL


a			:			List[str]												=					logging.get_logger(__name__)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					["pixel_values"]






					def __init__(					self			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = PILImageResampling.BILINEAR			,							snake_case_ = True			,							snake_case_ = 1 / 2_5_5			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = None			,							**snake_case_			,							) ->      None:







								'''simple docstring'''

								super().__init__(**snake_case_    )
								__lowercase			       = size if size is not None else {'''shortest_edge''': 3_8_4}
								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )

								__lowercase			       = do_resize
								__lowercase			       = size
								# Default value set here for backwards compatibility where the value in config is None
								__lowercase			       = crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
								__lowercase			       = resample
								__lowercase			       = do_rescale
								__lowercase			       = rescale_factor
								__lowercase			       = do_normalize
								__lowercase			       = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__lowercase			       = image_std if image_std is not None else IMAGENET_STANDARD_STD






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = PILImageResampling.BICUBIC			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )
								if "shortest_edge" not in size:
											raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'    )
								__lowercase			       = size['''shortest_edge''']

								if shortest_edge < 3_8_4:
											# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
											__lowercase			       = int(shortest_edge / crop_pct    )
											__lowercase			       = get_resize_output_image_size(snake_case_			,							size=snake_case_			,							default_to_square=snake_case_    )
											__lowercase			       = resize(image=snake_case_			,							size=snake_case_			,							resample=snake_case_			,							data_format=snake_case_			,							**snake_case_    )
											# then crop to (shortest_edge, shortest_edge)
											return center_crop(image=snake_case_			,							size=(shortest_edge, shortest_edge)			,							data_format=snake_case_			,							**snake_case_    )
								else:
											# warping (no cropping) when evaluated at 384 or larger
											return resize(
											    snake_case_			,							size=(shortest_edge, shortest_edge)			,							resample=snake_case_			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      Optional[int]:







								'''simple docstring'''

								return rescale(snake_case_			,							scale=snake_case_			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

								return normalize(snake_case_			,							mean=snake_case_			,							std=snake_case_			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = ChannelDimension.FIRST			,							**snake_case_			,							) ->      PIL.Image.Image:







								'''simple docstring'''

								__lowercase			       = do_resize if do_resize is not None else self.do_resize
								__lowercase			       = crop_pct if crop_pct is not None else self.crop_pct
								__lowercase			       = resample if resample is not None else self.resample
								__lowercase			       = do_rescale if do_rescale is not None else self.do_rescale
								__lowercase			       = rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowercase			       = do_normalize if do_normalize is not None else self.do_normalize
								__lowercase			       = image_mean if image_mean is not None else self.image_mean
								__lowercase			       = image_std if image_std is not None else self.image_std

								__lowercase			       = size if size is not None else self.size
								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )

								__lowercase			       = make_list_of_images(snake_case_    )

								if not valid_images(snake_case_    ):
											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 or resample is None:
											raise ValueError('''Size and resample must be specified if do_resize is True.'''    )

								if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
											raise ValueError('''crop_pct must be specified if size < 384.'''    )

								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.
								__lowercase			       = [to_numpy_array(snake_case_    ) for image in images]

								if do_resize:
											__lowercase			       = [self.resize(image=snake_case_			,							size=snake_case_			,							crop_pct=snake_case_			,							resample=snake_case_    ) for image in images]

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

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

								__lowercase			       = [to_channel_dimension_format(snake_case_			,							snake_case_    ) for image in images]

								__lowercase			       = {'''pixel_values''': images}
								return BatchFeature(data=snake_case_			,							tensor_type=snake_case_    )


