


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase   	=  logging.get_logger(__name__)

__lowerCamelCase   	=  {
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}


class 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  str				     =  """flaubert"""
       lowerCamelCase__:  str				     =  {
           """hidden_size""": """emb_dim""",
           """num_attention_heads""": """n_heads""",
           """num_hidden_layers""": """n_layers""",
           """n_words""": """vocab_size""",  # For backward compatibility
       }

       def __init__( self   :  List[Any]   ,				lowerCamelCase_   :  str=False   ,				lowerCamelCase_   :  List[Any]=0.0   ,				lowerCamelCase_   :  int=3_0_1_4_5   ,				lowerCamelCase_   :  str=2_0_4_8   ,				lowerCamelCase_   :  Dict=1_2   ,				lowerCamelCase_   :  Any=1_6   ,				lowerCamelCase_   :  Optional[int]=0.1   ,				lowerCamelCase_   :  Tuple=0.1   ,				lowerCamelCase_   :  List[str]=True   ,				lowerCamelCase_   :  str=False   ,				lowerCamelCase_   :  int=False   ,				lowerCamelCase_   :  Optional[int]=False   ,				lowerCamelCase_   :  Tuple=1   ,				lowerCamelCase_   :  Union[str, Any]=True   ,				lowerCamelCase_   :  str=5_1_2   ,				lowerCamelCase_   :  Optional[Any]=2_0_4_8**-0.5   ,				lowerCamelCase_   :  Optional[int]=1E-12   ,				lowerCamelCase_   :  int=0.0_2   ,				lowerCamelCase_   :  int=0   ,				lowerCamelCase_   :  Optional[int]=1   ,				lowerCamelCase_   :  Dict=2   ,				lowerCamelCase_   :  int=3   ,				lowerCamelCase_   :  str=5   ,				lowerCamelCase_   :  Optional[int]=True   ,				lowerCamelCase_   :  Union[str, Any]="first"   ,				lowerCamelCase_   :  int=True   ,				lowerCamelCase_   :  Dict=None   ,				lowerCamelCase_   :  Union[str, Any]=True   ,				lowerCamelCase_   :  str=0.1   ,				lowerCamelCase_   :  str=5   ,				lowerCamelCase_   :  Any=5   ,				lowerCamelCase_   :  int=0   ,				lowerCamelCase_   :  Any=0   ,				lowerCamelCase_   :  Tuple=2   ,				lowerCamelCase_   :  Optional[Any]=0   ,				**lowerCamelCase_   :  int   ,				):
            a_     :  Dict				      =     pre_norm
            a_     :  List[str]				      =     layerdrop
            a_     :  Any				      =     vocab_size
            a_     :  Tuple				      =     emb_dim
            a_     :  str				      =     n_layers
            a_     :  Tuple				      =     n_heads
            a_     :  List[Any]				      =     dropout
            a_     :  str				      =     attention_dropout
            a_     :  Dict				      =     gelu_activation
            a_     :  Optional[Any]				      =     sinusoidal_embeddings
            a_     :  List[str]				      =     causal
            a_     :  Union[str, Any]				      =     asm
            a_     :  int				      =     n_langs
            a_     :  int				      =     use_lang_emb
            a_     :  int				      =     layer_norm_eps
            a_     :  Dict				      =     bos_index
            a_     :  int				      =     eos_index
            a_     :  Dict				      =     pad_index
            a_     :  Dict				      =     unk_index
            a_     :  Tuple				      =     mask_index
            a_     :  Tuple				      =     is_encoder
            a_     :  Dict				      =     max_position_embeddings
            a_     :  Union[str, Any]				      =     embed_init_std
            a_     :  List[str]				      =     init_std
            a_     :  Union[str, Any]				      =     summary_type
            a_     :  Tuple				      =     summary_use_proj
            a_     :  int				      =     summary_activation
            a_     :  Union[str, Any]				      =     summary_proj_to_labels
            a_     :  List[str]				      =     summary_first_dropout
            a_     :  Optional[int]				      =     start_n_top
            a_     :  List[Any]				      =     end_n_top
            a_     :  Dict				      =     mask_token_id
            a_     :  List[Any]				      =     lang_id

            if "n_words" in kwargs:
                 a_     :  Any				      =     kwargs["""n_words"""]

            super().__init__(pad_token_id=lowerCamelCase_   ,				bos_token_id=lowerCamelCase_   ,				**lowerCamelCase_     )


class 	a__						(    lowerCAmelCase_     ):

       @property
       def     UpperCAmelCase( self   :  Tuple     ):
            if self.task == "multiple-choice":
                 a_     :  Optional[Any]				      =     {0: """batch""", 1: """choice""", 2: """sequence"""}
            else:
                 a_     :  List[Any]				      =     {0: """batch""", 1: """sequence"""}
            return OrderedDict(
                [
                    ("""input_ids""", dynamic_axis),
                    ("""attention_mask""", dynamic_axis),
                ]     )




"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import SwinaSRImageProcessor
					from transformers.image_transforms import get_image_size



class 	lowerCAmelCase_     (      unittest.TestCase       ):



				"""simple docstring"""

				def __init__(					self		,     lowerCAmelCase		,     lowerCAmelCase=7		,     lowerCAmelCase=3		,     lowerCAmelCase=18		,     lowerCAmelCase=30		,     lowerCAmelCase=4_00		,     lowerCAmelCase=True		,     lowerCAmelCase=1 / 2_55		,     lowerCAmelCase=True		,     lowerCAmelCase=8		,     ):






										"""simple docstring"""

										snake_case             =      parent
										snake_case             =      batch_size
										snake_case             =      num_channels
										snake_case             =      image_size
										snake_case             =      min_resolution
										snake_case             =      max_resolution
										snake_case             =      do_rescale
										snake_case             =      rescale_factor
										snake_case             =      do_pad
										snake_case             =      pad_size

				def 		snake_case			(					self     ):






										"""simple docstring"""

										return {
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_pad": self.do_pad,
										    "pad_size": self.pad_size,
										}

				def 		snake_case			(					self		,     lowerCAmelCase=False		,     lowerCAmelCase=False		,     lowerCAmelCase=False     ):






										"""simple docstring"""

										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
																snake_case             =      []
																for i in range(self.batch_size     ):
																						image_inputs.append(
																						    np.random.randint(
																						        2_55		,     size=(self.num_channels, self.max_resolution, self.max_resolution)		,     dtype=np.uinta     )     )
										else:
																snake_case             =      []
																for i in range(self.batch_size     ):
																						snake_case       ,snake_case             =      np.random.choice(np.arange(self.min_resolution		,     self.max_resolution     )		,     2     )
																						image_inputs.append(np.random.randint(2_55		,     size=(self.num_channels, width, height)		,     dtype=np.uinta     )     )

										if not numpify and not torchify:
																# PIL expects the channel dimension as last dimension
																snake_case             =      [Image.fromarray(np.moveaxis(lowerCAmelCase		,     0		,     -1     )     ) for x in image_inputs]

										if torchify:
																snake_case             =      [torch.from_numpy(lowerCAmelCase     ) for x in image_inputs]

										return image_inputs





@require_torch
@require_vision
class 	lowerCAmelCase_     (      lowerCAmelCase  ,     unittest.TestCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     Tuple      	=    SwinaSRImageProcessor if is_vision_available() else None

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      SwinaSRImageProcessingTester(self     )

				@property
				def 		snake_case			(					self     ):






										"""simple docstring"""

										return self.image_processor_tester.prepare_image_processor_dict()

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(lowerCAmelCase		,     'do_rescale'     )     )
										self.assertTrue(hasattr(lowerCAmelCase		,     'rescale_factor'     )     )
										self.assertTrue(hasattr(lowerCAmelCase		,     'do_pad'     )     )
										self.assertTrue(hasattr(lowerCAmelCase		,     'pad_size'     )     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										pass

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      get_image_size(lowerCAmelCase     )
										snake_case             =      self.image_processor_tester.pad_size

										snake_case             =      (old_height // size + 1) * size - old_height
										snake_case             =      (old_width // size + 1) * size - old_width
										return old_height + pad_height, old_width + pad_width

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.image_processing_class(**self.image_processor_dict     )
										# create random PIL images
										snake_case             =      self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase     )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,     Image.Image     )

										# Test not batched input
										snake_case             =      image_processor(image_inputs[0]		,     return_tensors='pt'     ).pixel_values
										snake_case       ,snake_case             =      self.calculate_expected_size(np.array(image_inputs[0]     )     )
										self.assertEqual(
										    encoded_images.shape		,     (
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )		,     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.image_processing_class(**self.image_processor_dict     )
										# create random numpy tensors
										snake_case             =      self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase		,     numpify=lowerCAmelCase     )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,     np.ndarray     )

										# Test not batched input
										snake_case             =      image_processor(image_inputs[0]		,     return_tensors='pt'     ).pixel_values
										snake_case       ,snake_case             =      self.calculate_expected_size(image_inputs[0]     )
										self.assertEqual(
										    encoded_images.shape		,     (
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )		,     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.image_processing_class(**self.image_processor_dict     )
										# create random PyTorch tensors
										snake_case             =      self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase		,     torchify=lowerCAmelCase     )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,     torch.Tensor     )

										# Test not batched input
										snake_case             =      image_processor(image_inputs[0]		,     return_tensors='pt'     ).pixel_values
										snake_case       ,snake_case             =      self.calculate_expected_size(image_inputs[0]     )
										self.assertEqual(
										    encoded_images.shape		,     (
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )		,     )


