






"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

A__					: Dict=	{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """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""",
}
A__					: str=	[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Optional[Any]:
    """simple docstring"""

    for attribute in key.split('.'					):
        UpperCamelCase__         =    getattr(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)

    if weight_type is not None:
        UpperCamelCase__         =    getattr(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					).shape
    else:
        UpperCamelCase__         =    hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            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":
        UpperCamelCase__         =    value
    elif weight_type == "weight_g":
        UpperCamelCase__         =    value
    elif weight_type == "weight_v":
        UpperCamelCase__         =    value
    elif weight_type == "bias":
        UpperCamelCase__         =    value
    elif weight_type == "running_mean":
        UpperCamelCase__         =    value
    elif weight_type == "running_var":
        UpperCamelCase__         =    value
    elif weight_type == "num_batches_tracked":
        UpperCamelCase__         =    value
    elif weight_type == "inv_freq":
        UpperCamelCase__         =    value
    else:
        UpperCamelCase__         =    value

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Union[str, Any]:
    """simple docstring"""

    UpperCamelCase__         =    []
    UpperCamelCase__         =    fairseq_model.state_dict()

    UpperCamelCase__         =    hf_model.wavaveca_conformer.feature_extractor

    for name, value in fairseq_dict.items():
        UpperCamelCase__         =    False
        if "conv_layers" in name:
            load_conv_layer(
                SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	hf_model.config.feat_extract_norm == 'group' ,	)
            UpperCamelCase__         =    True
        else:
            for key, mapped_key in MAPPING.items():
                UpperCamelCase__         =    'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                if key in name or key.split('w2v_model.'					)[-1] == name.split('.'					)[0]:
                    UpperCamelCase__         =    True
                    if "*" in mapped_key:
                        UpperCamelCase__         =    name.split(SCREAMING_SNAKE_CASE					)[0].split('.'					)[-2]
                        UpperCamelCase__         =    mapped_key.replace('*' ,	SCREAMING_SNAKE_CASE					)
                    if "pos_bias_u" in name:
                        UpperCamelCase__         =    None
                    elif "pos_bias_v" in name:
                        UpperCamelCase__         =    None
                    elif "weight_g" in name:
                        UpperCamelCase__         =    'weight_g'
                    elif "weight_v" in name:
                        UpperCamelCase__         =    'weight_v'
                    elif "bias" in name:
                        UpperCamelCase__         =    'bias'
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        UpperCamelCase__         =    'weight'
                    elif "running_mean" in name:
                        UpperCamelCase__         =    'running_mean'
                    elif "inv_freq" in name:
                        UpperCamelCase__         =    'inv_freq'
                    elif "running_var" in name:
                        UpperCamelCase__         =    'running_var'
                    elif "num_batches_tracked" in name:
                        UpperCamelCase__         =    'num_batches_tracked'
                    else:
                        UpperCamelCase__         =    None
                    set_recursively(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
                continue
        if not is_used:
            unused_weights.append(SCREAMING_SNAKE_CASE					)

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[Any]:
    """simple docstring"""

    UpperCamelCase__         =    full_name.split('conv_layers.'					)[-1]
    UpperCamelCase__         =    name.split('.'					)
    UpperCamelCase__         =    int(items[0]					)
    UpperCamelCase__         =    int(items[1]					)

    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    F'{full_name} has size {value.shape}, but'
                    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'					)
            UpperCamelCase__         =    value
            logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    F'{full_name} has size {value.shape}, but'
                    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'					)
            UpperCamelCase__         =    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:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    F'{full_name} has size {value.shape}, but'
                    F' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'					)
            UpperCamelCase__         =    value
            logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    F'{full_name} has size {value.shape}, but'
                    F' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'					)
            UpperCamelCase__         =    value
            logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
    else:
        unused_weights.append(SCREAMING_SNAKE_CASE					)







@torch.no_grad()
def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=True					)					->	Any:
    """simple docstring"""

    if config_path is not None:
        UpperCamelCase__         =    WavaVecaConformerConfig.from_pretrained(SCREAMING_SNAKE_CASE ,	hidden_act='swish'					)
    else:
        UpperCamelCase__         =    WavaVecaConformerConfig()

    if "rope" in checkpoint_path:
        UpperCamelCase__         =    'rotary'

    if is_finetuned:
        if dict_path:
            UpperCamelCase__         =    Dictionary.load(SCREAMING_SNAKE_CASE					)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            UpperCamelCase__         =    target_dict.pad_index
            UpperCamelCase__         =    target_dict.bos_index
            UpperCamelCase__         =    target_dict.eos_index
            UpperCamelCase__         =    len(target_dict.symbols					)
            UpperCamelCase__         =    os.path.join(SCREAMING_SNAKE_CASE ,	'vocab.json'					)
            if not os.path.isdir(SCREAMING_SNAKE_CASE					):
                logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(SCREAMING_SNAKE_CASE					)					)
                return
            os.makedirs(SCREAMING_SNAKE_CASE ,	exist_ok=SCREAMING_SNAKE_CASE					)
            UpperCamelCase__         =    target_dict.indices

            # fairseq has the <pad> and <s> switched
            UpperCamelCase__         =    0
            UpperCamelCase__         =    1
            with open(SCREAMING_SNAKE_CASE ,	'w' ,	encoding='utf-8'					) as vocab_handle:
                json.dump(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            UpperCamelCase__         =    WavaVecaCTCTokenizer(
                SCREAMING_SNAKE_CASE ,	unk_token=target_dict.unk_word ,	pad_token=target_dict.pad_word ,	bos_token=target_dict.bos_word ,	eos_token=target_dict.eos_word ,	word_delimiter_token='|' ,	do_lower_case=SCREAMING_SNAKE_CASE ,	)
            UpperCamelCase__         =    True if config.feat_extract_norm == 'layer' else False
            UpperCamelCase__         =    WavaVecaFeatureExtractor(
                feature_size=1 ,	sampling_rate=1_60_00 ,	padding_value=0 ,	do_normalize=SCREAMING_SNAKE_CASE ,	return_attention_mask=SCREAMING_SNAKE_CASE ,	)
            UpperCamelCase__         =    WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE ,	tokenizer=SCREAMING_SNAKE_CASE					)
            processor.save_pretrained(SCREAMING_SNAKE_CASE					)

        UpperCamelCase__         =    WavaVecaConformerForCTC(SCREAMING_SNAKE_CASE					)
    else:
        UpperCamelCase__         =    WavaVecaConformerForPreTraining(SCREAMING_SNAKE_CASE					)

    if is_finetuned:
        UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path] ,	arg_overrides={'data': '/'.join(dict_path.split('/'					)[:-1]					)}					)
    else:
        UpperCamelCase__         =    argparse.Namespace(task='audio_pretraining'					)
        UpperCamelCase__         =    fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE					)

        UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,	task=SCREAMING_SNAKE_CASE					)

    UpperCamelCase__         =    model[0].eval()

    recursively_load_weights(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	not is_finetuned					)

    hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  A__					: Dict=	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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
  parser.add_argument(
      """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
  )
  A__					: Any=	parser.parse_args()
  convert_wavaveca_conformer_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )












"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
  import jax

  from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
  from PIL import Image

  from transformers import BeitImageProcessor
class    __lowerCamelCase    (  unittest.TestCase  ):

   def __init__(					self ,				snake_case_ ,				snake_case_=100 ,				snake_case_=13 ,				snake_case_=30 ,				snake_case_=2 ,				snake_case_=3 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=32 ,				snake_case_=5 ,				snake_case_=4 ,				snake_case_=37 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=10 ,				snake_case_=0.02 ,				snake_case_=3 ,				)		->       Optional[int]:
       UpperCamelCase__         =    parent
       UpperCamelCase__         =    vocab_size
       UpperCamelCase__         =    batch_size
       UpperCamelCase__         =    image_size
       UpperCamelCase__         =    patch_size
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    is_training
       UpperCamelCase__         =    use_labels
       UpperCamelCase__         =    hidden_size
       UpperCamelCase__         =    num_hidden_layers
       UpperCamelCase__         =    num_attention_heads
       UpperCamelCase__         =    intermediate_size
       UpperCamelCase__         =    hidden_act
       UpperCamelCase__         =    hidden_dropout_prob
       UpperCamelCase__         =    attention_probs_dropout_prob
       UpperCamelCase__         =    type_sequence_label_size
       UpperCamelCase__         =    initializer_range

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

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

       UpperCamelCase__         =    None
       if self.use_labels:
           UpperCamelCase__         =    ids_tensor([self.batch_size] ,				self.type_sequence_label_size      )

       UpperCamelCase__         =    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=snake_case_ ,				initializer_range=self.initializer_range ,				)

       return config, pixel_values, labels

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       List[Any]:
       UpperCamelCase__         =    FlaxBeitModel(config=snake_case_      )
       UpperCamelCase__         =    model(snake_case_      )
       self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       List[Any]:
       UpperCamelCase__         =    FlaxBeitForMaskedImageModeling(config=snake_case_      )
       UpperCamelCase__         =    model(snake_case_      )
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length - 1, self.vocab_size)      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       List[str]:
       UpperCamelCase__         =    self.type_sequence_label_size
       UpperCamelCase__         =    FlaxBeitForImageClassification(config=snake_case_      )
       UpperCamelCase__         =    model(snake_case_      )
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       UpperCamelCase__         =    1
       UpperCamelCase__         =    FlaxBeitForImageClassification(snake_case_      )

       UpperCamelCase__         =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       UpperCamelCase__         =    model(snake_case_      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase__
       )       ,						(
           UpperCamelCase__
       )       ,						(
           UpperCamelCase__
       )       ,						
       )         =    config_and_inputs
       UpperCamelCase__         =    {'pixel_values': pixel_values}
       return config, inputs_dict







@require_flax
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				int 						=(
       (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
   )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       None:
       UpperCamelCase__         =    FlaxBeitModelTester(self      )
       UpperCamelCase__         =    ConfigTester(self ,				config_class=snake_case_ ,				has_text_modality=snake_case_ ,				hidden_size=37      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       self.config_tester.run_common_tests()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__       ,						UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase__         =    ['pixel_values']
           self.assertListEqual(arg_names[:1] ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__       ,						UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__      ):
               UpperCamelCase__         =    self._prepare_for_class(snake_case_ ,				snake_case_      )
               UpperCamelCase__         =    model_class(snake_case_      )

               @jax.jit
               def model_jitted(snake_case_ ,				**snake_case_      ):
                   return model(pixel_values=snake_case_ ,				**snake_case_      )

               with self.subTest('JIT Enabled'      ):
                   UpperCamelCase__         =    model_jitted(**snake_case_      ).to_tuple()

               with self.subTest('JIT Disabled'      ):
                   with jax.disable_jit():
                       UpperCamelCase__         =    model_jitted(**snake_case_      ).to_tuple()

               self.assertEqual(len(snake_case_      ) ,				len(snake_case_      )      )
               for jitted_output, output in zip(snake_case_ ,				snake_case_      ):
                   self.assertEqual(jitted_output.shape ,				output.shape      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case_      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       for model_class_name in self.all_model_classes:
           UpperCamelCase__         =    model_class_name.from_pretrained('microsoft/beit-base-patch16-224'      )
           UpperCamelCase__         =    model(np.ones((1, 3, 224, 224)      )      )
           self.assertIsNotNone(snake_case_      )







def   lowerCAmelCase_( )					->	Optional[int]:
    """simple docstring"""

    UpperCamelCase__         =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
    return image





@require_vision
@require_flax
class    __lowerCamelCase    (  unittest.TestCase  ):

   @cached_property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'      ) if is_vision_available() else None

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'      )

       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(images=snake_case_ ,				return_tensors='np'      ).pixel_values

       # prepare bool_masked_pos
       UpperCamelCase__         =    np.ones((1, 196) ,				dtype=snake_case_      )

       # forward pass
       UpperCamelCase__         =    model(pixel_values=snake_case_ ,				bool_masked_pos=snake_case_      )
       UpperCamelCase__         =    outputs.logits

       # verify the logits
       UpperCamelCase__         =    (1, 196, 8192)
       self.assertEqual(logits.shape ,				snake_case_      )

       UpperCamelCase__         =    np.array(
           [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]      )

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

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'      )

       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(images=snake_case_ ,				return_tensors='np'      )

       # forward pass
       UpperCamelCase__         =    model(**snake_case_      )
       UpperCamelCase__         =    outputs.logits

       # verify the logits
       UpperCamelCase__         =    (1, 1000)
       self.assertEqual(logits.shape ,				snake_case_      )

       UpperCamelCase__         =    np.array([-1.2_385, -1.0_987, -1.0_108]      )

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

       UpperCamelCase__         =    281
       self.assertEqual(logits.argmax(-1      ).item() ,				snake_case_      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'      )

       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(images=snake_case_ ,				return_tensors='np'      )

       # forward pass
       UpperCamelCase__         =    model(**snake_case_      )
       UpperCamelCase__         =    outputs.logits

       # verify the logits
       UpperCamelCase__         =    (1, 2_1841)
       self.assertEqual(logits.shape ,				snake_case_      )

       UpperCamelCase__         =    np.array([1.6_881, -0.2_787, 0.5_901]      )

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

       UpperCamelCase__         =    2396
       self.assertEqual(logits.argmax(-1      ).item() ,				snake_case_      )




