



'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
SCREAMING_SNAKE_CASE__      					=     [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def        lowerCamelCase  (				_snake_case  :						Optional[int]     ,_snake_case  :						Any     ,_snake_case  :						Dict     ,_snake_case  :						Union[str, Any]     ,_snake_case  :						Optional[Any]       ):





    '''simple docstring'''


    for attribute in key.split("."       ):
        lowercase__		       =						getattr(_snake_case     ,_snake_case       )

    if weight_type is not None:
        lowercase__		       =						getattr(_snake_case     ,_snake_case       ).shape
    else:
        lowercase__		       =						hf_pointer.shape

    assert hf_shape == value.shape, (
        f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
        f''' {value.shape} for {full_name}'''
    )

    if weight_type == "weight":
        lowercase__		       =						value
    elif weight_type == "weight_g":
        lowercase__		       =						value
    elif weight_type == "weight_v":
        lowercase__		       =						value
    elif weight_type == "bias":
        lowercase__		       =						value
    else:
        lowercase__		       =						value

    logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''       )




def        lowerCamelCase  (				_snake_case  :						Optional[int]     ,_snake_case  :						int       ):





    '''simple docstring'''


    lowercase__		       =						[]
    lowercase__		       =						fairseq_model.state_dict()

    lowercase__		       =						hf_model.feature_extractor

    # if encoder has different dim to decoder -> use proj_weight
    lowercase__		       =						None

    for name, value in fairseq_dict.items():
        lowercase__		       =						False
        if "conv_layers" in name:
            load_conv_layer(
                _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,hf_model.config.feat_extract_norm == "group"     ,)
            lowercase__		       =						True
        elif name.split("."       )[0] == "proj":
            lowercase__		       =						fairseq_model.proj
            lowercase__		       =						True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split("w2v_model."       )[-1] == name.split("."       )[0]:
                    lowercase__		       =						True
                    if "*" in mapped_key:
                        lowercase__		       =						name.split(_snake_case       )[0].split("."       )[-2]
                        lowercase__		       =						mapped_key.replace("*"     ,_snake_case       )
                    if "weight_g" in name:
                        lowercase__		       =						"weight_g"
                    elif "weight_v" in name:
                        lowercase__		       =						"weight_v"
                    elif "bias" in name:
                        lowercase__		       =						"bias"
                    elif "weight" in name:
                        lowercase__		       =						"weight"
                    else:
                        lowercase__		       =						None
                    set_recursively(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case       )
                continue
        if not is_used:
            unused_weights.append(_snake_case       )

    logger.warning(f'''Unused weights: {unused_weights}'''       )

    return proj_weight




def        lowerCamelCase  (				_snake_case  :						Tuple     ,_snake_case  :						List[Any]     ,_snake_case  :						Tuple     ,_snake_case  :						Union[str, Any]     ,_snake_case  :						Optional[int]       ):





    '''simple docstring'''


    lowercase__		       =						full_name.split("conv_layers."       )[-1]
    lowercase__		       =						name.split("."       )
    lowercase__		       =						int(items[0]       )
    lowercase__		       =						int(items[1]       )

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                f'''{full_name} has size {value.shape}, but'''
                f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
            )
            lowercase__		       =						value
            logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                f'''{full_name} has size {value.shape}, but'''
                f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
            )
            lowercase__		       =						value
            logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                " found."
            )
            lowercase__		       =						value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                f'''{full_name} has size {value.shape}, but'''
                f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
            )
            lowercase__		       =						value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
    else:
        unused_weights.append(_snake_case       )




def        lowerCamelCase  (				_snake_case  :						Tuple       ):





    '''simple docstring'''


    lowercase__       ,       lowercase__		       =						emb.weight.shape
    lowercase__		       =						nn.Linear(_snake_case     ,_snake_case     ,bias=_snake_case       )
    lowercase__		       =						emb.weight.data
    return lin_layer




def        lowerCamelCase  (				_snake_case  :						str       ):





    '''simple docstring'''


    with open(_snake_case     ,"r"     ,encoding="utf-8"       ) as f:
        lowercase__		       =						f.readlines()
        lowercase__		       =						[line.split(" "       )[0] for line in lines]

    lowercase__		       =						len(_snake_case       )

    lowercase__		       =						{
        "<s>": 0,
        "<pad>": 1,
        "</s>": 2,
        "<unk>": 3,
    }

    vocab_dict.update(dict(zip(_snake_case     ,range(4     ,num_words + 4       )       )       )       )
    return vocab_dict




@torch.no_grad()
def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						Optional[int]     ,_snake_case  :						str     ,_snake_case  :						Tuple     ,_snake_case  :						Dict     ,_snake_case  :						int     ,_snake_case  :						int     ,):





    '''simple docstring'''


    lowercase__		       =						WavaVecaConfig.from_pretrained(_snake_case       )
    lowercase__		       =						SpeechaTextaConfig.from_pretrained(
        _snake_case     ,vocab_size=_snake_case     ,decoder_layers=_snake_case     ,do_stable_layer_norm=_snake_case       )

    lowercase__		       =						WavaVecaFeatureExtractor(
        feature_size=1     ,sampling_rate=16_000     ,padding_value=0     ,do_normalize=_snake_case     ,return_attention_mask=_snake_case     ,)

    lowercase__       ,       lowercase__       ,       lowercase__		       =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]     ,arg_overrides={"data": "/".join(dict_path.split("/"       )[:-1]       )}       )
    lowercase__		       =						model[0].eval()

    # set weights for wav2vec2 encoder
    lowercase__		       =						WavaVecaModel(_snake_case       )
    lowercase__		       =						recursively_load_weights_wavaveca(model.encoder     ,_snake_case       )

    lowercase__		       =						SpeechaTextaForCausalLM(_snake_case       )
    lowercase__       ,       lowercase__		       =						hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()     ,strict=_snake_case       )

    # set output linear layer
    unexpected_keys.remove("embed_out"       )
    lowercase__		       =						nn.Parameter(model.decoder.embed_out.detach()       )

    # layer norm is init to identity matrix so leaving it is fine
    logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}'''       )
    logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''       )

    lowercase__		       =						SpeechEncoderDecoderModel(encoder=_snake_case     ,decoder=_snake_case       )
    lowercase__		       =						False

    # add projection layer
    lowercase__		       =						nn.Parameter(projection_layer.weight       )
    lowercase__		       =						nn.Parameter(projection_layer.bias       )

    lowercase__		       =						create_vocab_dict(_snake_case       )

    with open(os.path.join(_snake_case     ,"vocab.json"       )     ,"w"       ) as fp:
        json.dump(_snake_case     ,_snake_case       )

    lowercase__		       =						SpeechaTextaTokenizer(os.path.join(_snake_case     ,"vocab.json"       )       )
    tokenizer.save_pretrained(_snake_case       )

    lowercase__		       =						hf_wavavec.config.to_dict()
    lowercase__		       =						tokenizer.pad_token_id
    lowercase__		       =						tokenizer.bos_token_id
    lowercase__		       =						tokenizer.eos_token_id
    lowercase__		       =						"speech_to_text_2"
    lowercase__		       =						"wav2vec2"

    lowercase__		       =						SpeechEncoderDecoderConfig.from_dict(_snake_case       )

    hf_wavavec.save_pretrained(_snake_case       )
    feature_extractor.save_pretrained(_snake_case       )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__      					=     argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
 parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
 parser.add_argument(
     "--encoder_config_path",
     default="facebook/wav2vec2-large-lv60",
     type=str,
     help="Path to hf encoder wav2vec2 checkpoint config",
 )
 parser.add_argument(
     "--decoder_config_path",
     default="facebook/s2t-small-mustc-en-fr-st",
     type=str,
     help="Path to hf decoder s2t checkpoint config",
 )
 parser.add_argument("--vocab_size", default=1_0224, type=int, help="Vocab size of decoder")
 parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

 SCREAMING_SNAKE_CASE__      					=     parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     vocab_size=args.vocab_size,
     num_decoder_layers=args.num_decoder_layers,
 )









'''simple docstring'''





import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     MODEL_MAPPING,
     BeitForImageClassification,
     BeitForMaskedImageModeling,
     BeitForSemanticSegmentation,
     BeitModel,
 )
 from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 import PIL
 from PIL import Image

 from transformers import BeitImageProcessor


class 				snake_case :



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_=100     ,UpperCAmelCase_=13     ,UpperCAmelCase_=30     ,UpperCAmelCase_=2     ,UpperCAmelCase_=3     ,UpperCAmelCase_=True     ,UpperCAmelCase_=True     ,UpperCAmelCase_=32     ,UpperCAmelCase_=4     ,UpperCAmelCase_=4     ,UpperCAmelCase_=37     ,UpperCAmelCase_="gelu"     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=10     ,UpperCAmelCase_=0.02     ,UpperCAmelCase_=3     ,UpperCAmelCase_=None     ,UpperCAmelCase_=[0, 1, 2, 3]     ,)  ->							Any:
           lowercase__		       =						parent
           lowercase__		       =						100
           lowercase__		       =						batch_size
           lowercase__		       =						image_size
           lowercase__		       =						patch_size
           lowercase__		       =						num_channels
           lowercase__		       =						is_training
           lowercase__		       =						use_labels
           lowercase__		       =						hidden_size
           lowercase__		       =						num_hidden_layers
           lowercase__		       =						num_attention_heads
           lowercase__		       =						intermediate_size
           lowercase__		       =						hidden_act
           lowercase__		       =						hidden_dropout_prob
           lowercase__		       =						attention_probs_dropout_prob
           lowercase__		       =						type_sequence_label_size
           lowercase__		       =						initializer_range
           lowercase__		       =						scope
           lowercase__		       =						out_indices
           lowercase__		       =						num_labels

           # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           lowercase__		       =						(image_size // patch_size) ** 2
           lowercase__		       =						num_patches + 1



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

           lowercase__		       =						None
           lowercase__		       =						None
           if self.use_labels:
               lowercase__		       =						ids_tensor([self.batch_size]     ,self.type_sequence_label_size      )
               lowercase__		       =						ids_tensor([self.batch_size, self.image_size, self.image_size]     ,self.num_labels      )

           lowercase__		       =						self.get_config()

           return config, pixel_values, labels, pixel_labels



       def 							_a      (				self      )  ->							List[str]:
           return BeitConfig(
               vocab_size=self.vocab_size     ,image_size=self.image_size     ,patch_size=self.patch_size     ,num_channels=self.num_channels     ,hidden_size=self.hidden_size     ,num_hidden_layers=self.num_hidden_layers     ,num_attention_heads=self.num_attention_heads     ,intermediate_size=self.intermediate_size     ,hidden_act=self.hidden_act     ,hidden_dropout_prob=self.hidden_dropout_prob     ,attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,is_decoder=UpperCAmelCase_     ,initializer_range=self.initializer_range     ,out_indices=self.out_indices     ,)



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[int]:
           lowercase__		       =						BeitModel(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Tuple:
           lowercase__		       =						BeitForMaskedImageModeling(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length - 1, self.vocab_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Dict:
           lowercase__		       =						self.type_sequence_label_size
           lowercase__		       =						BeitForImageClassification(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_     ,labels=UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)      )

           # test greyscale images
           lowercase__		       =						1
           lowercase__		       =						BeitForImageClassification(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()

           lowercase__		       =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
           lowercase__		       =						model(UpperCAmelCase_     ,labels=UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Dict:
           lowercase__		       =						self.num_labels
           lowercase__		       =						BeitForSemanticSegmentation(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(
               result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )
           lowercase__		       =						model(UpperCAmelCase_     ,labels=UpperCAmelCase_      )
           self.parent.assertEqual(
               result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )







       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.prepare_config_and_inputs()
           lowercase__       ,       lowercase__       ,       lowercase__       ,       lowercase__		       =						config_and_inputs
           lowercase__		       =						{"pixel_values": pixel_values}
           return config, inputs_dict





@require_torch
class 				snake_case (UpperCamelCase		,      UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Optional[Any]						   =					(
           (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__  :Optional[int]						   =					(
           {
               "feature-extraction": BeitModel,
               "image-classification": BeitForImageClassification,
               "image-segmentation": BeitForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       lowerCAmelCase__  :Any						   =					False
       lowerCAmelCase__  :Optional[Any]						   =					False
       lowerCAmelCase__  :Optional[Any]						   =					False



       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						BeitModelTester(self      )
           lowercase__		       =						ConfigTester(self     ,config_class=UpperCAmelCase_     ,has_text_modality=UpperCAmelCase_     ,hidden_size=37      )



       def 							_a      (				self      )  ->							Tuple:
           self.config_tester.run_common_tests()



       @unittest.skip(reason="BEiT does not use inputs_embeds"      )
       def 							_a      (				self      )  ->							Dict:
           pass



       @require_torch_multi_gpu
       @unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"      )
       def 							_a      (				self      )  ->							int:
           pass



       def 							_a      (				self      )  ->							Any:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               lowercase__		       =						model_class(UpperCAmelCase_      )
               self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module)      )
               lowercase__		       =						model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(UpperCAmelCase_     ,nn.Linear      )      )



       def 							_a      (				self      )  ->							Tuple:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               lowercase__		       =						model_class(UpperCAmelCase_      )
               lowercase__		       =						inspect.signature(model.forward      )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowercase__		       =						[*signature.parameters.keys()]

               lowercase__		       =						["pixel_values"]
               self.assertListEqual(arg_names[:1]     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           if not self.model_tester.is_training:
               return

           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()
           lowercase__		       =						True

           for model_class in self.all_model_classes:
               # we don't test BeitForMaskedImageModeling
               if model_class in [*get_values(UpperCAmelCase_      ), BeitForMaskedImageModeling]:
                   continue

               lowercase__		       =						model_class(UpperCAmelCase_      )
               model.to(UpperCAmelCase_      )
               model.train()
               lowercase__		       =						self._prepare_for_class(UpperCAmelCase_     ,UpperCAmelCase_     ,return_labels=UpperCAmelCase_      )
               lowercase__		       =						model(**UpperCAmelCase_      ).loss
               loss.backward()



       def 							_a      (				self      )  ->							str:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()
           if not self.model_tester.is_training:
               return

           lowercase__		       =						False
           lowercase__		       =						True

           for model_class in self.all_model_classes:
               # we don't test BeitForMaskedImageModeling
               if (
                   model_class in [*get_values(UpperCAmelCase_      ), BeitForMaskedImageModeling]
                   or not model_class.supports_gradient_checkpointing
               ):
                   continue

               lowercase__		       =						model_class(UpperCAmelCase_      )
               model.gradient_checkpointing_enable()
               model.to(UpperCAmelCase_      )
               model.train()
               lowercase__		       =						self._prepare_for_class(UpperCAmelCase_     ,UpperCAmelCase_     ,return_labels=UpperCAmelCase_      )
               lowercase__		       =						model(**UpperCAmelCase_      ).loss
               loss.backward()



       def 							_a      (				self      )  ->							int:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           lowercase__		       =						_config_zero_init(UpperCAmelCase_      )
           for model_class in self.all_model_classes:
               lowercase__		       =						model_class(config=UpperCAmelCase_      )
               for name, param in model.named_parameters():
                   # we skip lambda parameters as these require special initial values
                   # determined by config.layer_scale_init_value
                   if "lambda" in name:
                       continue
                   if param.requires_grad:
                       self.assertIn(
                           ((param.data.mean() * 1E9).round() / 1E9).item()     ,[0.0, 1.0]     ,msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''     ,)







       @slow
       def 							_a      (				self      )  ->							Optional[Any]:
           for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__		       =						BeitModel.from_pretrained(UpperCAmelCase_      )
               self.assertIsNotNone(UpperCAmelCase_      )





def        lowerCamelCase  (				):





    '''simple docstring'''


    lowercase__		       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image



@require_torch
@require_vision
class 				snake_case (unittest.TestCase   ):



       @cached_property
       def 							_a      (				self      )  ->							Any:
           return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"      ) if is_vision_available() else None



       @slow
       def 							_a      (				self      )  ->							int:
           lowercase__		       =						BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"      ).to(UpperCAmelCase_      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).pixel_values.to(UpperCAmelCase_      )

           # prepare bool_masked_pos
           lowercase__		       =						torch.ones((1, 196)     ,dtype=torch.bool      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(pixel_values=UpperCAmelCase_     ,bool_masked_pos=UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						torch.Size((1, 196, 8_192)      )
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						torch.tensor(
               [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]      ).to(UpperCAmelCase_      )

           self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]     ,UpperCAmelCase_     ,atol=1E-2      )      )



       @slow
       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"      ).to(UpperCAmelCase_      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						torch.Size((1, 1_000)      )
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						torch.tensor([-1.23_85, -1.09_87, -1.01_08]      ).to(UpperCAmelCase_      )

           self.assertTrue(torch.allclose(logits[0, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )

           lowercase__		       =						281
           self.assertEqual(logits.argmax(-1      ).item()     ,UpperCAmelCase_      )



       @slow
       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"      ).to(
               UpperCAmelCase_      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						torch.Size((1, 21_841)      )
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						torch.tensor([1.68_81, -0.27_87, 0.59_01]      ).to(UpperCAmelCase_      )

           self.assertTrue(torch.allclose(logits[0, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )

           lowercase__		       =						2_396
           self.assertEqual(logits.argmax(-1      ).item()     ,UpperCAmelCase_      )



       @slow
       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"      )
           lowercase__		       =						model.to(UpperCAmelCase_      )

           lowercase__		       =						BeitImageProcessor(do_resize=UpperCAmelCase_     ,size=640     ,do_center_crop=UpperCAmelCase_      )

           lowercase__		       =						load_dataset("hf-internal-testing/fixtures_ade20k"     ,split="test"      )
           lowercase__		       =						Image.open(ds[0]["file"]      )
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						torch.Size((1, 150, 160, 160)      )
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						version.parse(PIL.__version__      ) < version.parse("9.0.0"      )

           if is_pillow_less_than_a:
               lowercase__		       =						torch.tensor(
                   [
                       [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
                       [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
                       [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
                   ]     ,device=UpperCAmelCase_     ,)
           else:
               lowercase__		       =						torch.tensor(
                   [
                       [[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
                       [[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
                       [[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
                   ]     ,device=UpperCAmelCase_     ,)

           self.assertTrue(torch.allclose(logits[0, :3, :3, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )







       @slow
       def 							_a      (				self      )  ->							str:
           lowercase__		       =						BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"      )
           lowercase__		       =						model.to(UpperCAmelCase_      )

           lowercase__		       =						BeitImageProcessor(do_resize=UpperCAmelCase_     ,size=640     ,do_center_crop=UpperCAmelCase_      )

           lowercase__		       =						load_dataset("hf-internal-testing/fixtures_ade20k"     ,split="test"      )
           lowercase__		       =						Image.open(ds[0]["file"]      )
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )

           lowercase__		       =						outputs.logits.detach().cpu()

           lowercase__		       =						image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_     ,target_sizes=[(500, 300)]      )
           lowercase__		       =						torch.Size((500, 300)      )
           self.assertEqual(segmentation[0].shape     ,UpperCAmelCase_      )

           lowercase__		       =						image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_      )
           lowercase__		       =						torch.Size((160, 160)      )
           self.assertEqual(segmentation[0].shape     ,UpperCAmelCase_      )




