'''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()
_lowercase							:      Optional[Any]      							=logging.get_logger(__name__)

_lowercase							:      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""",
}
_lowercase							:      str      							=[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def A__  (			lowercase:     Any,		lowercase:     Optional[int],		lowercase:     Optional[Any],		lowercase:     str,		lowercase:     Optional[Any]					)				->		Optional[Any]:

       for attribute in key.split('.'					):
              A						:     List[str]         =getattr(lowercase,		lowercase					)

       if weight_type is not None:
              A						:     Dict         =getattr(lowercase,		lowercase					).shape
       else:
              A						:     Any         =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":
              A						:     int         =value
       elif weight_type == "weight_g":
              A						:     int         =value
       elif weight_type == "weight_v":
              A						:     Tuple         =value
       elif weight_type == "bias":
              A						:     Optional[Any]         =value
       elif weight_type == "running_mean":
              A						:     Union[str, Any]         =value
       elif weight_type == "running_var":
              A						:     Optional[Any]         =value
       elif weight_type == "num_batches_tracked":
              A						:     Optional[Any]         =value
       elif weight_type == "inv_freq":
              A						:     List[str]         =value
       else:
              A						:     int         =value

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



def A__  (			lowercase:     Tuple,		lowercase:     Optional[int],		lowercase:     Union[str, Any]					)				->		Union[str, Any]:
       A						:     List[Any]         =[]
       A						:     str         =fairseq_model.state_dict()

       A						:     Union[str, Any]         =hf_model.wavaveca_conformer.feature_extractor

       for name, value in fairseq_dict.items():
              A						:     Optional[int]         =False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowercase,		lowercase,		lowercase,		lowercase,		hf_model.config.feat_extract_norm == 'group',		)
                     A						:     Any         =True
              else:
                     for key, mapped_key in MAPPING.items():
                            A						:     Optional[Any]         ='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]:
                                   A						:     List[str]         =True
                                   if "*" in mapped_key:
                                          A						:     str         =name.split(lowercase					)[0].split('.'					)[-2]
                                          A						:     List[Any]         =mapped_key.replace('*',		lowercase					)
                                   if "pos_bias_u" in name:
                                          A						:     Union[str, Any]         =None
                                   elif "pos_bias_v" in name:
                                          A						:     str         =None
                                   elif "weight_g" in name:
                                          A						:     Tuple         ='weight_g'
                                   elif "weight_v" in name:
                                          A						:     Dict         ='weight_v'
                                   elif "bias" in name:
                                          A						:     Optional[Any]         ='bias'
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          A						:     Optional[Any]         ='weight'
                                   elif "running_mean" in name:
                                          A						:     List[Any]         ='running_mean'
                                   elif "inv_freq" in name:
                                          A						:     Optional[Any]         ='inv_freq'
                                   elif "running_var" in name:
                                          A						:     Tuple         ='running_var'
                                   elif "num_batches_tracked" in name:
                                          A						:     str         ='num_batches_tracked'
                                   else:
                                          A						:     Optional[int]         =None
                                   set_recursively(lowercase,		lowercase,		lowercase,		lowercase,		lowercase					)
                            continue
              if not is_used:
                     unused_weights.append(lowercase					)

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



def A__  (			lowercase:     int,		lowercase:     Dict,		lowercase:     List[str],		lowercase:     Union[str, Any],		lowercase:     str					)				->		List[Any]:
       A						:     Optional[Any]         =full_name.split('conv_layers.'					)[-1]
       A						:     Any         =name.split('.'					)
       A						:     Optional[Any]         =int(items[0]					)
       A						:     Tuple         =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.'					)
                     A						:     Optional[Any]         =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.'					)
                     A						:     Optional[int]         =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.'					)
                     A						:     List[Any]         =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.'					)
                     A						:     List[Any]         =value
                     logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
       else:
              unused_weights.append(lowercase					)



@torch.no_grad()
def A__  (			lowercase:     Optional[int],		lowercase:     Optional[int],		lowercase:     Dict=None,		lowercase:     Tuple=None,		lowercase:     str=True					)				->		Any:

       if config_path is not None:
              A						:     Optional[int]         =WavaVecaConformerConfig.from_pretrained(lowercase,		hidden_act='swish'					)
       else:
              A						:     int         =WavaVecaConformerConfig()

       if "rope" in checkpoint_path:
              A						:     List[Any]         ='rotary'

       if is_finetuned:
              if dict_path:
                     A						:     List[Any]         =Dictionary.load(lowercase					)

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

                     # fairseq has the <pad> and <s> switched
                     A						:     Union[str, Any]         =0
                     A						:     Any         =1
                     with open(lowercase,		'w',		encoding='utf-8'					) as vocab_handle:
                            json.dump(lowercase,		lowercase					)
                     A						:     Optional[Any]         =WavaVecaCTCTokenizer(
                         lowercase,		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=lowercase,		)
                     A						:     Optional[Any]         =True if config.feat_extract_norm == 'layer' else False
                     A						:     Tuple         =WavaVecaFeatureExtractor(
                         feature_size=1,		sampling_rate=16_000,		padding_value=0,		do_normalize=lowercase,		return_attention_mask=lowercase,		)
                     A						:     Tuple         =WavaVecaProcessor(feature_extractor=lowercase,		tokenizer=lowercase					)
                     processor.save_pretrained(lowercase					)

              A						:     Dict         =WavaVecaConformerForCTC(lowercase					)
       else:
              A						:     Optional[int]         =WavaVecaConformerForPreTraining(lowercase					)

       if is_finetuned:
              A  ,  A  ,  A						:     List[str]         =fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path],		arg_overrides={'data': '/'.join(dict_path.split('/'					)[:-1]					)}					)
       else:
              A						:     Tuple         =argparse.Namespace(task='audio_pretraining'					)
              A						:     Union[str, Any]         =fairseq.tasks.setup_task(lowercase					)

              A  ,  A  ,  A						:     int         =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path],		task=lowercase					)

       A						:     str         =model[0].eval()

       recursively_load_weights(lowercase,		lowercase,		not is_finetuned					)

       hf_wavavec.save_pretrained(lowercase					)


if __name__ == "__main__":
    _lowercase							:      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'''
    )
    _lowercase							:      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
    )







import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
    import torch

if is_tf_available():
    import tensorflow as tf



@require_vision
@require_torchvision
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Union[str, Any]:
         A						:     Dict         =tempfile.mkdtemp()
         A						:     int         =SamImageProcessor()
         A						:     Union[str, Any]         =SamProcessor(SCREAMING_SNAKE_CASE__     )
         processor.save_pretrained(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]      ,	**SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				Optional[int]:
         return AutoProcessor.from_pretrained(self.tmpdirname      ,	**SCREAMING_SNAKE_CASE__     ).image_processor







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Any:
         shutil.rmtree(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Optional[int]:
         A						:     str         =[np.random.randint(2_55      ,	size=(3, 30, 4_00)      ,	dtype=np.uinta     )]

         A						:     Optional[int]         =[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__      ,	0      ,	-1     )     ) for x in image_inputs]

         return image_inputs







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Tuple:
         A						:     Optional[int]         =SamProcessor(image_processor=self.get_image_processor()     )
         processor.save_pretrained(self.tmpdirname     )

         A						:     str         =self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__      ,	padding_value=1.0     )

         A						:     Union[str, Any]         =SamProcessor.from_pretrained(self.tmpdirname      ,	do_normalize=SCREAMING_SNAKE_CASE__      ,	padding_value=1.0     )

         self.assertEqual(processor.image_processor.to_json_string()      ,	image_processor_add_kwargs.to_json_string()     )
         self.assertIsInstance(processor.image_processor      ,	SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Optional[int]:
         A						:     Optional[Any]         =self.get_image_processor()

         A						:     Optional[Any]         =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__     )

         A						:     Dict         =self.prepare_image_inputs()

         A						:     Optional[int]         =image_processor(SCREAMING_SNAKE_CASE__      ,	return_tensors='np'     )
         A						:     Optional[Any]         =processor(images=SCREAMING_SNAKE_CASE__      ,	return_tensors='np'     )

         input_feat_extract.pop('original_sizes'     )  # pop original_sizes as it is popped in the processor
         input_feat_extract.pop('reshaped_input_sizes'     )  # pop original_sizes as it is popped in the processor

         for key in input_feat_extract.keys():
                self.assertAlmostEqual(input_feat_extract[key].sum()      ,	input_processor[key].sum()      ,	delta=1e-2     )







  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Any:
         A						:     str         =self.get_image_processor()

         A						:     Union[str, Any]         =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__     )
         A						:     str         =[torch.ones((1, 3, 5, 5)     )]

         A						:     Optional[Any]         =[[17_64, 26_46]]

         A						:     List[Any]         =[[6_83, 10_24]]
         A						:     Union[str, Any]         =processor.post_process_masks(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         self.assertEqual(masks[0].shape      ,	(1, 3, 17_64, 26_46)     )

         A						:     Any         =processor.post_process_masks(
             SCREAMING_SNAKE_CASE__      ,	torch.tensor(SCREAMING_SNAKE_CASE__     )      ,	torch.tensor(SCREAMING_SNAKE_CASE__     )     )
         self.assertEqual(masks[0].shape      ,	(1, 3, 17_64, 26_46)     )

         # should also work with np
         A						:     str         =[np.ones((1, 3, 5, 5)     )]
         A						:     int         =processor.post_process_masks(SCREAMING_SNAKE_CASE__      ,	np.array(SCREAMING_SNAKE_CASE__     )      ,	np.array(SCREAMING_SNAKE_CASE__     )     )

         self.assertEqual(masks[0].shape      ,	(1, 3, 17_64, 26_46)     )

         A						:     Any         =[[1, 0], [0, 1]]
         with self.assertRaises(SCREAMING_SNAKE_CASE__     ):
                A						:     Any         =processor.post_process_masks(SCREAMING_SNAKE_CASE__      ,	np.array(SCREAMING_SNAKE_CASE__     )      ,	np.array(SCREAMING_SNAKE_CASE__     )     )







@require_vision
@require_tf
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				str:
         A						:     Tuple         =tempfile.mkdtemp()
         A						:     Union[str, Any]         =SamImageProcessor()
         A						:     Union[str, Any]         =SamProcessor(SCREAMING_SNAKE_CASE__     )
         processor.save_pretrained(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	**SCREAMING_SNAKE_CASE__    :					str     )    ->				Union[str, Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname      ,	**SCREAMING_SNAKE_CASE__     ).image_processor







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				List[str]:
         shutil.rmtree(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Tuple:
         A						:     Optional[Any]         =[np.random.randint(2_55      ,	size=(3, 30, 4_00)      ,	dtype=np.uinta     )]

         A						:     Any         =[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__      ,	0      ,	-1     )     ) for x in image_inputs]

         return image_inputs







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				List[str]:
         A						:     Optional[Any]         =SamProcessor(image_processor=self.get_image_processor()     )
         processor.save_pretrained(self.tmpdirname     )

         A						:     Optional[Any]         =self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__      ,	padding_value=1.0     )

         A						:     Dict         =SamProcessor.from_pretrained(self.tmpdirname      ,	do_normalize=SCREAMING_SNAKE_CASE__      ,	padding_value=1.0     )

         self.assertEqual(processor.image_processor.to_json_string()      ,	image_processor_add_kwargs.to_json_string()     )
         self.assertIsInstance(processor.image_processor      ,	SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				Any:
         A						:     Any         =self.get_image_processor()

         A						:     Any         =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__     )

         A						:     int         =self.prepare_image_inputs()

         A						:     Tuple         =image_processor(SCREAMING_SNAKE_CASE__      ,	return_tensors='np'     )
         A						:     List[Any]         =processor(images=SCREAMING_SNAKE_CASE__      ,	return_tensors='np'     )

         input_feat_extract.pop('original_sizes'     )  # pop original_sizes as it is popped in the processor
         input_feat_extract.pop('reshaped_input_sizes'     )  # pop reshaped_input_sizes as it is popped in the processor

         for key in input_feat_extract.keys():
                self.assertAlmostEqual(input_feat_extract[key].sum()      ,	input_processor[key].sum()      ,	delta=1e-2     )







  @require_tf
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Tuple:
         A						:     int         =self.get_image_processor()

         A						:     Any         =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         =[tf.ones((1, 3, 5, 5)     )]

         A						:     Tuple         =[[17_64, 26_46]]

         A						:     Union[str, Any]         =[[6_83, 10_24]]
         A						:     int         =processor.post_process_masks(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	return_tensors='tf'     )
         self.assertEqual(masks[0].shape      ,	(1, 3, 17_64, 26_46)     )

         A						:     List[Any]         =processor.post_process_masks(
             SCREAMING_SNAKE_CASE__      ,	tf.convert_to_tensor(SCREAMING_SNAKE_CASE__     )      ,	tf.convert_to_tensor(SCREAMING_SNAKE_CASE__     )      ,	return_tensors='tf'      ,	)
         self.assertEqual(masks[0].shape      ,	(1, 3, 17_64, 26_46)     )

         # should also work with np
         A						:     Any         =[np.ones((1, 3, 5, 5)     )]
         A						:     Optional[Any]         =processor.post_process_masks(
             SCREAMING_SNAKE_CASE__      ,	np.array(SCREAMING_SNAKE_CASE__     )      ,	np.array(SCREAMING_SNAKE_CASE__     )      ,	return_tensors='tf'     )

         self.assertEqual(masks[0].shape      ,	(1, 3, 17_64, 26_46)     )

         A						:     Any         =[[1, 0], [0, 1]]
         with self.assertRaises(tf.errors.InvalidArgumentError     ):
                A						:     List[str]         =processor.post_process_masks(
                    SCREAMING_SNAKE_CASE__      ,	np.array(SCREAMING_SNAKE_CASE__     )      ,	np.array(SCREAMING_SNAKE_CASE__     )      ,	return_tensors='tf'     )







@require_vision
@require_torchvision
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Union[str, Any]:
         A						:     Optional[int]         =tempfile.mkdtemp()
         A						:     Union[str, Any]         =SamImageProcessor()
         A						:     Dict         =SamProcessor(SCREAMING_SNAKE_CASE__     )
         processor.save_pretrained(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	**SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				Any:
         return AutoProcessor.from_pretrained(self.tmpdirname      ,	**SCREAMING_SNAKE_CASE__     ).image_processor







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				Any:
         shutil.rmtree(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				Tuple:
         A						:     Any         =[np.random.randint(2_55      ,	size=(3, 30, 4_00)      ,	dtype=np.uinta     )]

         A						:     Tuple         =[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__      ,	0      ,	-1     )     ) for x in image_inputs]

         return image_inputs







  @is_pt_tf_cross_test
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				List[str]:
         A						:     Optional[Any]         =self.get_image_processor()

         A						:     Dict         =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         =np.random.randint(0      ,	2      ,	size=(1, 3, 5, 5)     ).astype(np.floataa     )
         A						:     Optional[int]         =[tf.convert_to_tensor(SCREAMING_SNAKE_CASE__     )]
         A						:     Union[str, Any]         =[torch.tensor(SCREAMING_SNAKE_CASE__     )]

         A						:     int         =[[17_64, 26_46]]

         A						:     int         =[[6_83, 10_24]]
         A						:     Dict         =processor.post_process_masks(
             SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	return_tensors='tf'     )
         A						:     Optional[Any]         =processor.post_process_masks(
             SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     )

         self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()     )     )







  @is_pt_tf_cross_test
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Any:
         A						:     Union[str, Any]         =self.get_image_processor()

         A						:     int         =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__     )

         A						:     int         =self.prepare_image_inputs()

         A						:     List[Any]         =image_processor(SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     )['pixel_values'].numpy()
         A						:     Tuple         =processor(images=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     )['pixel_values'].numpy()

         A						:     Optional[int]         =image_processor(SCREAMING_SNAKE_CASE__      ,	return_tensors='tf'     )['pixel_values'].numpy()
         A						:     Dict         =processor(images=SCREAMING_SNAKE_CASE__      ,	return_tensors='tf'     )['pixel_values'].numpy()

         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )     )
         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )     )
         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )     )






