'''simple docstring'''





import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ConditionalDetrImageProcessor




class 	__A				(			unittest.TestCase     ):



    def __init__(self   :   List[str]       ,      __a   :   Tuple       ,      __a   :   Tuple=7       ,      __a   :   Union[str, Any]=3       ,      __a   :   Tuple=30       ,      __a   :   str=400       ,      __a   :   Optional[Any]=True       ,      __a   :   int=None       ,      __a   :   Dict=True       ,      __a   :   Dict=[0.5, 0.5, 0.5]       ,      __a   :   List[Any]=[0.5, 0.5, 0.5]       ,      __a   :   str=True       ,      __a   :   List[str]=1 / 255       ,      __a   :   Optional[int]=True       ,      ):
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         UpperCAmelCase_	   =       size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
         UpperCAmelCase_	   =       parent
         UpperCAmelCase_	   =       batch_size
         UpperCAmelCase_	   =       num_channels
         UpperCAmelCase_	   =       min_resolution
         UpperCAmelCase_	   =       max_resolution
         UpperCAmelCase_	   =       do_resize
         UpperCAmelCase_	   =       size
         UpperCAmelCase_	   =       do_normalize
         UpperCAmelCase_	   =       image_mean
         UpperCAmelCase_	   =       image_std
         UpperCAmelCase_	   =       do_rescale
         UpperCAmelCase_	   =       rescale_factor
         UpperCAmelCase_	   =       do_pad



    def  _lowercase						(self   :   int			):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }






    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   Optional[Any]       ,      __a   :   List[str]=False			):
         if not batched:
              UpperCAmelCase_	   =       image_inputs[0]
              if isinstance(__a       ,      Image.Image			):
                   UpperCAmelCase_   ,			UpperCAmelCase_	   =       image.size
              else:
                   UpperCAmelCase_   ,			UpperCAmelCase_	   =       image.shape[1], image.shape[2]
              if w < h:
                   UpperCAmelCase_	   =       int(self.size["shortest_edge"] * h / w			)
                   UpperCAmelCase_	   =       self.size["shortest_edge"]
              elif w > h:
                   UpperCAmelCase_	   =       self.size["shortest_edge"]
                   UpperCAmelCase_	   =       int(self.size["shortest_edge"] * w / h			)
              else:
                   UpperCAmelCase_	   =       self.size["shortest_edge"]
                   UpperCAmelCase_	   =       self.size["shortest_edge"]

         else:
              UpperCAmelCase_	   =       []
              for image in image_inputs:
                   UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.get_expected_values([image]			)
                   expected_values.append((expected_height, expected_width)			)
              UpperCAmelCase_	   =       max(__a       ,      key=lambda __a			: item[0]			)[0]
              UpperCAmelCase_	   =       max(__a       ,      key=lambda __a			: item[1]			)[1]

         return expected_height, expected_width




@require_torch
@require_vision
class 	__A				(			UpperCamelCase__		,     unittest.TestCase     ):
    a__	:						int	       =  ConditionalDetrImageProcessor if is_vision_available() else None



    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_	   =       ConditionalDetrImageProcessingTester(self			)



    @property
    def  _lowercase						(self   :   Optional[int]			):
         return self.image_processor_tester.prepare_image_processor_dict()



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       self.image_processing_class(**self.image_processor_dict			)
         self.assertTrue(hasattr(__a       ,      "image_mean"			)			)
         self.assertTrue(hasattr(__a       ,      "image_std"			)			)
         self.assertTrue(hasattr(__a       ,      "do_normalize"			)			)
         self.assertTrue(hasattr(__a       ,      "do_resize"			)			)
         self.assertTrue(hasattr(__a       ,      "size"			)			)



    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_	   =       self.image_processing_class.from_dict(self.image_processor_dict			)
         self.assertEqual(image_processor.size       ,      {"shortest_edge": 18, "longest_edge": 1333}			)
         self.assertEqual(image_processor.do_pad       ,      __a			)

         UpperCAmelCase_	   =       self.image_processing_class.from_dict(
             self.image_processor_dict       ,      size=42       ,      max_size=84       ,      pad_and_return_pixel_mask=__a			)
         self.assertEqual(image_processor.size       ,      {"shortest_edge": 42, "longest_edge": 84}			)
         self.assertEqual(image_processor.do_pad       ,      __a			)



    def  _lowercase						(self   :   Any			):
         pass



    def  _lowercase						(self   :   Optional[Any]			):
         # Initialize image_processing
         UpperCAmelCase_	   =       self.image_processing_class(**self.image_processor_dict			)
         # create random PIL images
         UpperCAmelCase_	   =       prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=__a			)
         for image in image_inputs:
              self.assertIsInstance(__a       ,      Image.Image			)

         # Test not batched input
         UpperCAmelCase_	   =       image_processing(image_inputs[0]       ,      return_tensors="pt"			).pixel_values

         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.image_processor_tester.get_expected_values(__a			)

         self.assertEqual(
             encoded_images.shape       ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,      )

         # Test batched
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.image_processor_tester.get_expected_values(__a       ,      batched=__a			)

         UpperCAmelCase_	   =       image_processing(__a       ,      return_tensors="pt"			).pixel_values
         self.assertEqual(
             encoded_images.shape       ,      (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,      )



    def  _lowercase						(self   :   Tuple			):
         # Initialize image_processing
         UpperCAmelCase_	   =       self.image_processing_class(**self.image_processor_dict			)
         # create random numpy tensors
         UpperCAmelCase_	   =       prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=__a       ,      numpify=__a			)
         for image in image_inputs:
              self.assertIsInstance(__a       ,      np.ndarray			)

         # Test not batched input
         UpperCAmelCase_	   =       image_processing(image_inputs[0]       ,      return_tensors="pt"			).pixel_values

         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.image_processor_tester.get_expected_values(__a			)

         self.assertEqual(
             encoded_images.shape       ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,      )

         # Test batched
         UpperCAmelCase_	   =       image_processing(__a       ,      return_tensors="pt"			).pixel_values

         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.image_processor_tester.get_expected_values(__a       ,      batched=__a			)

         self.assertEqual(
             encoded_images.shape       ,      (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,      )



    def  _lowercase						(self   :   int			):
         # Initialize image_processing
         UpperCAmelCase_	   =       self.image_processing_class(**self.image_processor_dict			)
         # create random PyTorch tensors
         UpperCAmelCase_	   =       prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=__a       ,      torchify=__a			)
         for image in image_inputs:
              self.assertIsInstance(__a       ,      torch.Tensor			)

         # Test not batched input
         UpperCAmelCase_	   =       image_processing(image_inputs[0]       ,      return_tensors="pt"			).pixel_values

         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.image_processor_tester.get_expected_values(__a			)

         self.assertEqual(
             encoded_images.shape       ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,      )

         # Test batched
         UpperCAmelCase_	   =       image_processing(__a       ,      return_tensors="pt"			).pixel_values

         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.image_processor_tester.get_expected_values(__a       ,      batched=__a			)

         self.assertEqual(
             encoded_images.shape       ,      (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,      )



    @slow
    def  _lowercase						(self   :   Dict			):
         # prepare image and target
         UpperCAmelCase_	   =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
         with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,      "r"			) as f:
              UpperCAmelCase_	   =       json.loads(f.read()			)

         UpperCAmelCase_	   =       {"image_id": 39769, "annotations": target}

         # encode them
         UpperCAmelCase_	   =       ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"			)
         UpperCAmelCase_	   =       image_processing(images=__a       ,      annotations=__a       ,      return_tensors="pt"			)

         # verify pixel values
         UpperCAmelCase_	   =       torch.Size([1, 3, 800, 1066]			)
         self.assertEqual(encoding["pixel_values"].shape       ,      __a			)

         UpperCAmelCase_	   =       torch.tensor([0.27_96, 0.31_38, 0.34_81]			)
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,      __a       ,      atol=1E-4			)			)

         # verify area
         UpperCAmelCase_	   =       torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,      __a			)			)
         # verify boxes
         UpperCAmelCase_	   =       torch.Size([6, 4]			)
         self.assertEqual(encoding["labels"][0]["boxes"].shape       ,      __a			)
         UpperCAmelCase_	   =       torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,      __a       ,      atol=1E-3			)			)
         # verify image_id
         UpperCAmelCase_	   =       torch.tensor([39769]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,      __a			)			)
         # verify is_crowd
         UpperCAmelCase_	   =       torch.tensor([0, 0, 0, 0, 0, 0]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,      __a			)			)
         # verify class_labels
         UpperCAmelCase_	   =       torch.tensor([75, 75, 63, 65, 17, 17]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,      __a			)			)
         # verify orig_size
         UpperCAmelCase_	   =       torch.tensor([480, 640]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,      __a			)			)
         # verify size
         UpperCAmelCase_	   =       torch.tensor([800, 1066]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,      __a			)			)






    @slow
    def  _lowercase						(self   :   List[str]			):
         # prepare image, target and masks_path
         UpperCAmelCase_	   =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
         with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"       ,      "r"			) as f:
              UpperCAmelCase_	   =       json.loads(f.read()			)

         UpperCAmelCase_	   =       {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

         UpperCAmelCase_	   =       pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"			)

         # encode them
         UpperCAmelCase_	   =       ConditionalDetrImageProcessor(format="coco_panoptic"			)
         UpperCAmelCase_	   =       image_processing(images=__a       ,      annotations=__a       ,      masks_path=__a       ,      return_tensors="pt"			)

         # verify pixel values
         UpperCAmelCase_	   =       torch.Size([1, 3, 800, 1066]			)
         self.assertEqual(encoding["pixel_values"].shape       ,      __a			)

         UpperCAmelCase_	   =       torch.tensor([0.27_96, 0.31_38, 0.34_81]			)
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,      __a       ,      atol=1E-4			)			)

         # verify area
         UpperCAmelCase_	   =       torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,      __a			)			)
         # verify boxes
         UpperCAmelCase_	   =       torch.Size([6, 4]			)
         self.assertEqual(encoding["labels"][0]["boxes"].shape       ,      __a			)
         UpperCAmelCase_	   =       torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,      __a       ,      atol=1E-3			)			)
         # verify image_id
         UpperCAmelCase_	   =       torch.tensor([39769]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,      __a			)			)
         # verify is_crowd
         UpperCAmelCase_	   =       torch.tensor([0, 0, 0, 0, 0, 0]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,      __a			)			)
         # verify class_labels
         UpperCAmelCase_	   =       torch.tensor([17, 17, 63, 75, 75, 93]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,      __a			)			)
         # verify masks
         UpperCAmelCase_	   =       822873
         self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,      __a			)
         # verify orig_size
         UpperCAmelCase_	   =       torch.tensor([480, 640]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,      __a			)			)
         # verify size
         UpperCAmelCase_	   =       torch.tensor([800, 1066]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,      __a			)			)





'''simple docstring'''





from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


SCREAMING_SNAKE_CASE_:					Any  =logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:					List[Any]  =OrderedDict(
    [
        # Base model mapping
        ('albert', 'FlaxAlbertModel'),
        ('bart', 'FlaxBartModel'),
        ('beit', 'FlaxBeitModel'),
        ('bert', 'FlaxBertModel'),
        ('big_bird', 'FlaxBigBirdModel'),
        ('blenderbot', 'FlaxBlenderbotModel'),
        ('blenderbot-small', 'FlaxBlenderbotSmallModel'),
        ('clip', 'FlaxCLIPModel'),
        ('distilbert', 'FlaxDistilBertModel'),
        ('electra', 'FlaxElectraModel'),
        ('gpt-sw3', 'FlaxGPT2Model'),
        ('gpt2', 'FlaxGPT2Model'),
        ('gpt_neo', 'FlaxGPTNeoModel'),
        ('gptj', 'FlaxGPTJModel'),
        ('longt5', 'FlaxLongT5Model'),
        ('marian', 'FlaxMarianModel'),
        ('mbart', 'FlaxMBartModel'),
        ('mt5', 'FlaxMT5Model'),
        ('opt', 'FlaxOPTModel'),
        ('pegasus', 'FlaxPegasusModel'),
        ('regnet', 'FlaxRegNetModel'),
        ('resnet', 'FlaxResNetModel'),
        ('roberta', 'FlaxRobertaModel'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
        ('roformer', 'FlaxRoFormerModel'),
        ('t5', 'FlaxT5Model'),
        ('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
        ('vit', 'FlaxViTModel'),
        ('wav2vec2', 'FlaxWav2Vec2Model'),
        ('whisper', 'FlaxWhisperModel'),
        ('xglm', 'FlaxXGLMModel'),
        ('xlm-roberta', 'FlaxXLMRobertaModel'),
    ]
)

SCREAMING_SNAKE_CASE_:					List[Any]  =OrderedDict(
    [
        # Model for pre-training mapping
        ('albert', 'FlaxAlbertForPreTraining'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForPreTraining'),
        ('big_bird', 'FlaxBigBirdForPreTraining'),
        ('electra', 'FlaxElectraForPreTraining'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
        ('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

SCREAMING_SNAKE_CASE_:					Any  =OrderedDict(
    [
        # Model for Masked LM mapping
        ('albert', 'FlaxAlbertForMaskedLM'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForMaskedLM'),
        ('big_bird', 'FlaxBigBirdForMaskedLM'),
        ('distilbert', 'FlaxDistilBertForMaskedLM'),
        ('electra', 'FlaxElectraForMaskedLM'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

SCREAMING_SNAKE_CASE_:					int  =OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
        ('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
        ('encoder-decoder', 'FlaxEncoderDecoderModel'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('marian', 'FlaxMarianMTModel'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('pegasus', 'FlaxPegasusForConditionalGeneration'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
    ]
)

SCREAMING_SNAKE_CASE_:					str  =OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

SCREAMING_SNAKE_CASE_:					str  =OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

SCREAMING_SNAKE_CASE_:					List[Any]  =OrderedDict(
    [
        # Model for Causal LM mapping
        ('bart', 'FlaxBartForCausalLM'),
        ('bert', 'FlaxBertForCausalLM'),
        ('big_bird', 'FlaxBigBirdForCausalLM'),
        ('electra', 'FlaxElectraForCausalLM'),
        ('gpt-sw3', 'FlaxGPT2LMHeadModel'),
        ('gpt2', 'FlaxGPT2LMHeadModel'),
        ('gpt_neo', 'FlaxGPTNeoForCausalLM'),
        ('gptj', 'FlaxGPTJForCausalLM'),
        ('opt', 'FlaxOPTForCausalLM'),
        ('roberta', 'FlaxRobertaForCausalLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
        ('xglm', 'FlaxXGLMForCausalLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
    ]
)

SCREAMING_SNAKE_CASE_:					Optional[int]  =OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('albert', 'FlaxAlbertForSequenceClassification'),
        ('bart', 'FlaxBartForSequenceClassification'),
        ('bert', 'FlaxBertForSequenceClassification'),
        ('big_bird', 'FlaxBigBirdForSequenceClassification'),
        ('distilbert', 'FlaxDistilBertForSequenceClassification'),
        ('electra', 'FlaxElectraForSequenceClassification'),
        ('mbart', 'FlaxMBartForSequenceClassification'),
        ('roberta', 'FlaxRobertaForSequenceClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
        ('roformer', 'FlaxRoFormerForSequenceClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
    ]
)

SCREAMING_SNAKE_CASE_:					Any  =OrderedDict(
    [
        # Model for Question Answering mapping
        ('albert', 'FlaxAlbertForQuestionAnswering'),
        ('bart', 'FlaxBartForQuestionAnswering'),
        ('bert', 'FlaxBertForQuestionAnswering'),
        ('big_bird', 'FlaxBigBirdForQuestionAnswering'),
        ('distilbert', 'FlaxDistilBertForQuestionAnswering'),
        ('electra', 'FlaxElectraForQuestionAnswering'),
        ('mbart', 'FlaxMBartForQuestionAnswering'),
        ('roberta', 'FlaxRobertaForQuestionAnswering'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
        ('roformer', 'FlaxRoFormerForQuestionAnswering'),
        ('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
    ]
)

SCREAMING_SNAKE_CASE_:					Any  =OrderedDict(
    [
        # Model for Token Classification mapping
        ('albert', 'FlaxAlbertForTokenClassification'),
        ('bert', 'FlaxBertForTokenClassification'),
        ('big_bird', 'FlaxBigBirdForTokenClassification'),
        ('distilbert', 'FlaxDistilBertForTokenClassification'),
        ('electra', 'FlaxElectraForTokenClassification'),
        ('roberta', 'FlaxRobertaForTokenClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
        ('roformer', 'FlaxRoFormerForTokenClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
    ]
)

SCREAMING_SNAKE_CASE_:					Optional[Any]  =OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('albert', 'FlaxAlbertForMultipleChoice'),
        ('bert', 'FlaxBertForMultipleChoice'),
        ('big_bird', 'FlaxBigBirdForMultipleChoice'),
        ('distilbert', 'FlaxDistilBertForMultipleChoice'),
        ('electra', 'FlaxElectraForMultipleChoice'),
        ('roberta', 'FlaxRobertaForMultipleChoice'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
        ('roformer', 'FlaxRoFormerForMultipleChoice'),
        ('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
    ]
)

SCREAMING_SNAKE_CASE_:					int  =OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

SCREAMING_SNAKE_CASE_:					List[Any]  =OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

SCREAMING_SNAKE_CASE_:					Optional[int]  =OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

SCREAMING_SNAKE_CASE_:					str  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_:					str  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_:					List[str]  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_:					List[Any]  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Optional[Any]  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Dict  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_:					Optional[Any]  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_:					Any  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					List[str]  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Dict  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Any  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Dict  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Dict  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Optional[Any]  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						int	       =  FLAX_MODEL_MAPPING




SCREAMING_SNAKE_CASE_:					Dict  =auto_class_update(FlaxAutoModel)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						str	       =  FLAX_MODEL_FOR_PRETRAINING_MAPPING




SCREAMING_SNAKE_CASE_:					Dict  =auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Optional[Any]	       =  FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




SCREAMING_SNAKE_CASE_:					Tuple  =auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Optional[Any]	       =  FLAX_MODEL_FOR_MASKED_LM_MAPPING




SCREAMING_SNAKE_CASE_:					Optional[Any]  =auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						List[Any]	       =  FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




SCREAMING_SNAKE_CASE_:					Optional[Any]  =auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Union[str, Any]	       =  FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




SCREAMING_SNAKE_CASE_:					Optional[int]  =auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Optional[int]	       =  FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




SCREAMING_SNAKE_CASE_:					List[Any]  =auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Tuple	       =  FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




SCREAMING_SNAKE_CASE_:					List[Any]  =auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Dict	       =  FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




SCREAMING_SNAKE_CASE_:					Any  =auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Union[str, Any]	       =  FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




SCREAMING_SNAKE_CASE_:					int  =auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						int	       =  FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




SCREAMING_SNAKE_CASE_:					Dict  =auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Any	       =  FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




SCREAMING_SNAKE_CASE_:					Optional[int]  =auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						List[Any]	       =  FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




SCREAMING_SNAKE_CASE_:					Union[str, Any]  =auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)




