




"""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__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    "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__  		=		[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):



    for attribute in key.split('.'	):
        _lowerCamelCase  :    List[Any]					=					getattr(_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE	)

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

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







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
    _lowerCamelCase  :    List[Any]					=					[]
    _lowerCamelCase  :    int					=					fairseq_model.state_dict()

    _lowerCamelCase  :    int					=					hf_model.wavaveca_conformer.feature_extractor

    for name, value in fairseq_dict.items():
        _lowerCamelCase  :    Optional[int]					=					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'							,					)
            _lowerCamelCase  :    Optional[int]					=					True
        else:
            for key, mapped_key in MAPPING.items():
                _lowerCamelCase  :    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]:
                    _lowerCamelCase  :    Optional[int]					=					True
                    if "*" in mapped_key:
                        _lowerCamelCase  :    str					=					name.split(_SCREAMING_SNAKE_CASE	)[0].split('.'	)[-2]
                        _lowerCamelCase  :    List[str]					=					mapped_key.replace('*'							,					_SCREAMING_SNAKE_CASE	)
                    if "pos_bias_u" in name:
                        _lowerCamelCase  :    Optional[int]					=					None
                    elif "pos_bias_v" in name:
                        _lowerCamelCase  :    Tuple					=					None
                    elif "weight_g" in name:
                        _lowerCamelCase  :    Tuple					=					'weight_g'
                    elif "weight_v" in name:
                        _lowerCamelCase  :    Any					=					'weight_v'
                    elif "bias" in name:
                        _lowerCamelCase  :    Optional[int]					=					'bias'
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        _lowerCamelCase  :    Optional[int]					=					'weight'
                    elif "running_mean" in name:
                        _lowerCamelCase  :    str					=					'running_mean'
                    elif "inv_freq" in name:
                        _lowerCamelCase  :    str					=					'inv_freq'
                    elif "running_var" in name:
                        _lowerCamelCase  :    List[str]					=					'running_var'
                    elif "num_batches_tracked" in name:
                        _lowerCamelCase  :    Dict					=					'num_batches_tracked'
                    else:
                        _lowerCamelCase  :    int					=					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   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
    _lowerCamelCase  :    Optional[Any]					=					full_name.split('conv_layers.'	)[-1]
    _lowerCamelCase  :    Optional[int]					=					name.split('.'	)
    _lowerCamelCase  :    str					=					int(items[0]	)
    _lowerCamelCase  :    str					=					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.'''	)
            _lowerCamelCase  :    List[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.'''	)
            _lowerCamelCase  :    Dict					=					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.'''	)
            _lowerCamelCase  :    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.'''	)
            _lowerCamelCase  :    Optional[int]					=					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   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__=None							,					lowercase__=None							,					lowercase__=True	):



    if config_path is not None:
        _lowerCamelCase  :    Union[str, Any]					=					WavaVecaConformerConfig.from_pretrained(_SCREAMING_SNAKE_CASE							,					hidden_act='swish'	)
    else:
        _lowerCamelCase  :    int					=					WavaVecaConformerConfig()

    if "rope" in checkpoint_path:
        _lowerCamelCase  :    Tuple					=					'rotary'

    if is_finetuned:
        if dict_path:
            _lowerCamelCase  :    Optional[Any]					=					Dictionary.load(_SCREAMING_SNAKE_CASE	)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            _lowerCamelCase  :    Optional[int]					=					target_dict.pad_index
            _lowerCamelCase  :    List[Any]					=					target_dict.bos_index
            _lowerCamelCase  :    str					=					target_dict.eos_index
            _lowerCamelCase  :    Union[str, Any]					=					len(target_dict.symbols	)
            _lowerCamelCase  :    List[str]					=					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	)
            _lowerCamelCase  :    Dict					=					target_dict.indices

            # fairseq has the <pad> and <s> switched
            _lowerCamelCase  :    int					=					0
            _lowerCamelCase  :    List[Any]					=					1
            with open(_SCREAMING_SNAKE_CASE							,					'w'							,					encoding='utf-8'	) as vocab_handle:
                json.dump(_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE	)
            _lowerCamelCase  :    List[Any]					=					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							,					)
            _lowerCamelCase  :    List[Any]					=					True if config.feat_extract_norm == 'layer' else False
            _lowerCamelCase  :    int					=					WavaVecaFeatureExtractor(
                feature_size=1							,					sampling_rate=16000							,					padding_value=0							,					do_normalize=_SCREAMING_SNAKE_CASE							,					return_attention_mask=_SCREAMING_SNAKE_CASE							,					)
            _lowerCamelCase  :    Dict					=					WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE							,					tokenizer=_SCREAMING_SNAKE_CASE	)
            processor.save_pretrained(_SCREAMING_SNAKE_CASE	)

        _lowerCamelCase  :    str					=					WavaVecaConformerForCTC(_SCREAMING_SNAKE_CASE	)
    else:
        _lowerCamelCase  :    Tuple					=					WavaVecaConformerForPreTraining(_SCREAMING_SNAKE_CASE	)

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

        _lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    Optional[Any]					=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,					task=_SCREAMING_SNAKE_CASE	)

    _lowerCamelCase  :    List[str]					=					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__":
   lowercase__  		=		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__  		=		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 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 DetrImageProcessor






class lowerCAmelCase__			(  unittest.TestCase		):






			'''simple docstring'''



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






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

							else:
											_lowerCamelCase  :    Union[str, Any]					=					[]
											for image in image_inputs:
															_lowerCamelCase,				_lowerCamelCase  :    Tuple					=					self.get_expected_values([image]						)
															expected_values.append((expected_height, expected_width)						)
											_lowerCamelCase  :    Tuple					=					max(lowercase						,   key=lambda lowercase						: item[0]						)[0]
											_lowerCamelCase  :    Any					=					max(lowercase						,   key=lambda lowercase						: item[1]						)[1]

							return expected_height, expected_width






@require_torch
@require_vision
class lowerCAmelCase__			(  lowercase,			unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__   =    DetrImageProcessor if is_vision_available() else None
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					DetrImageProcessingTester(self						)
			@property
			def       A_					(						self						):
							return self.image_processor_tester.prepare_image_processor_dict()
			def       A_					(						self						):
							_lowerCamelCase  :    str					=					self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(lowercase						,   'image_mean'						)						)
							self.assertTrue(hasattr(lowercase						,   'image_std'						)						)
							self.assertTrue(hasattr(lowercase						,   'do_normalize'						)						)
							self.assertTrue(hasattr(lowercase						,   'do_rescale'						)						)
							self.assertTrue(hasattr(lowercase						,   'rescale_factor'						)						)
							self.assertTrue(hasattr(lowercase						,   'do_resize'						)						)
							self.assertTrue(hasattr(lowercase						,   'size'						)						)
							self.assertTrue(hasattr(lowercase						,   'do_pad'						)						)
			def       A_					(						self						):
							_lowerCamelCase  :    List[str]					=					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						,   lowercase						)

							_lowerCamelCase  :    Union[str, Any]					=					self.image_processing_class.from_dict(
							    self.image_processor_dict						,   size=42						,   max_size=84						,   pad_and_return_pixel_mask=lowercase						)
							self.assertEqual(image_processor.size						,   {'shortest_edge': 42, 'longest_edge': 84}						)
							self.assertEqual(image_processor.do_pad						,   lowercase						)
			def       A_					(						self						):
							pass
			def       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    Optional[int]					=					self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							_lowerCamelCase  :    List[str]					=					prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowercase						)
							for image in image_inputs:
											self.assertIsInstance(lowercase						,   Image.Image						)

							# Test not batched input
							_lowerCamelCase  :    Optional[int]					=					image_processing(image_inputs[0]						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    List[Any]					=					self.image_processor_tester.get_expected_values(lowercase						)

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

							# Test batched
							_lowerCamelCase,				_lowerCamelCase  :    str					=					self.image_processor_tester.get_expected_values(lowercase						,   batched=lowercase						)

							_lowerCamelCase  :    Union[str, Any]					=					image_processing(lowercase						,   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       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    Optional[int]					=					self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							_lowerCamelCase  :    Union[str, Any]					=					prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowercase						,   numpify=lowercase						)
							for image in image_inputs:
											self.assertIsInstance(lowercase						,   np.ndarray						)

							# Test not batched input
							_lowerCamelCase  :    Optional[int]					=					image_processing(image_inputs[0]						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    List[Any]					=					self.image_processor_tester.get_expected_values(lowercase						)

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

							# Test batched
							_lowerCamelCase  :    str					=					image_processing(lowercase						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    Union[str, Any]					=					self.image_processor_tester.get_expected_values(lowercase						,   batched=lowercase						)

							self.assertEqual(
							    encoded_images.shape						,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )						,   )
			def       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    List[str]					=					self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							_lowerCamelCase  :    Optional[Any]					=					prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowercase						,   torchify=lowercase						)
							for image in image_inputs:
											self.assertIsInstance(lowercase						,   torch.Tensor						)

							# Test not batched input
							_lowerCamelCase  :    int					=					image_processing(image_inputs[0]						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    Optional[Any]					=					self.image_processor_tester.get_expected_values(lowercase						)

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

							# Test batched
							_lowerCamelCase  :    Dict					=					image_processing(lowercase						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					self.image_processor_tester.get_expected_values(lowercase						,   batched=lowercase						)

							self.assertEqual(
							    encoded_images.shape						,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )						,   )
			@slow
			def       A_					(						self						):
							# prepare image and target
							_lowerCamelCase  :    Tuple					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'						,   'r'						) as f:
											_lowerCamelCase  :    int					=					json.loads(f.read()						)

							_lowerCamelCase  :    str					=					{'image_id': 39769, 'annotations': target}

							# encode them
							_lowerCamelCase  :    str					=					DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'						)
							_lowerCamelCase  :    int					=					image_processing(images=lowercase						,   annotations=lowercase						,   return_tensors='pt'						)

							# verify pixel values
							_lowerCamelCase  :    Optional[Any]					=					torch.Size([1, 3, 800, 1066]						)
							self.assertEqual(encoding['pixel_values'].shape						,   lowercase						)

							_lowerCamelCase  :    List[str]					=					torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,   lowercase						,   atol=1E-4						)						)

							# verify area
							_lowerCamelCase  :    Dict					=					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']						,   lowercase						)						)
							# verify boxes
							_lowerCamelCase  :    int					=					torch.Size([6, 4]						)
							self.assertEqual(encoding['labels'][0]['boxes'].shape						,   lowercase						)
							_lowerCamelCase  :    Tuple					=					torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,   lowercase						,   atol=1E-3						)						)
							# verify image_id
							_lowerCamelCase  :    List[Any]					=					torch.tensor([39769]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,   lowercase						)						)
							# verify is_crowd
							_lowerCamelCase  :    str					=					torch.tensor([0, 0, 0, 0, 0, 0]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,   lowercase						)						)
							# verify class_labels
							_lowerCamelCase  :    Dict					=					torch.tensor([75, 75, 63, 65, 17, 17]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,   lowercase						)						)
							# verify orig_size
							_lowerCamelCase  :    Optional[int]					=					torch.tensor([480, 640]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,   lowercase						)						)
							# verify size
							_lowerCamelCase  :    List[str]					=					torch.tensor([800, 1066]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,   lowercase						)						)






			@slow
			def       A_					(						self						):
							# prepare image, target and masks_path
							_lowerCamelCase  :    Optional[int]					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'						,   'r'						) as f:
											_lowerCamelCase  :    List[Any]					=					json.loads(f.read()						)

							_lowerCamelCase  :    str					=					{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

							_lowerCamelCase  :    List[Any]					=					pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'						)

							# encode them
							_lowerCamelCase  :    List[Any]					=					DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'						)
							_lowerCamelCase  :    Dict					=					image_processing(images=lowercase						,   annotations=lowercase						,   masks_path=lowercase						,   return_tensors='pt'						)

							# verify pixel values
							_lowerCamelCase  :    Optional[int]					=					torch.Size([1, 3, 800, 1066]						)
							self.assertEqual(encoding['pixel_values'].shape						,   lowercase						)

							_lowerCamelCase  :    List[Any]					=					torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,   lowercase						,   atol=1E-4						)						)

							# verify area
							_lowerCamelCase  :    Optional[Any]					=					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']						,   lowercase						)						)
							# verify boxes
							_lowerCamelCase  :    Dict					=					torch.Size([6, 4]						)
							self.assertEqual(encoding['labels'][0]['boxes'].shape						,   lowercase						)
							_lowerCamelCase  :    Union[str, Any]					=					torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,   lowercase						,   atol=1E-3						)						)
							# verify image_id
							_lowerCamelCase  :    List[str]					=					torch.tensor([39769]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,   lowercase						)						)
							# verify is_crowd
							_lowerCamelCase  :    Optional[Any]					=					torch.tensor([0, 0, 0, 0, 0, 0]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,   lowercase						)						)
							# verify class_labels
							_lowerCamelCase  :    List[str]					=					torch.tensor([17, 17, 63, 75, 75, 93]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,   lowercase						)						)
							# verify masks
							_lowerCamelCase  :    List[str]					=					822873
							self.assertEqual(encoding['labels'][0]['masks'].sum().item()						,   lowercase						)
							# verify orig_size
							_lowerCamelCase  :    Optional[Any]					=					torch.tensor([480, 640]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,   lowercase						)						)
							# verify size
							_lowerCamelCase  :    List[str]					=					torch.tensor([800, 1066]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,   lowercase						)						)