



import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__lowerCamelCase     :							List[str]  							=				logging.get_logger(__name__)

__lowerCamelCase     :							Any  							=				{
    "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",
}
__lowerCamelCase     :							Optional[Any]  							=				[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       Any:
						for attribute in key.split("."    ):
												UpperCAmelCase         =     getattr(lowerCamelCase_    ,   lowerCamelCase_    )

						if weight_type is not None:
												UpperCAmelCase         =     getattr(lowerCamelCase_    ,   lowerCamelCase_    ).shape
						else:
												UpperCAmelCase         =     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":
												UpperCAmelCase         =     value
						elif weight_type == "weight_g":
												UpperCAmelCase         =     value
						elif weight_type == "weight_v":
												UpperCAmelCase         =     value
						elif weight_type == "bias":
												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_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       Optional[int]:
						UpperCAmelCase         =     []
						UpperCAmelCase         =     fairseq_model.state_dict()

						UpperCAmelCase         =     hf_model.feature_extractor
						UpperCAmelCase         =     hf_model.adapter

						for name, value in fairseq_dict.items():
												UpperCAmelCase         =     False
												if "conv_layers" in name:
																		load_conv_layer(
																		    lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   hf_model.config.feat_extract_norm == "group"    ,   )
																		UpperCAmelCase         =     True
												elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]    ):
																		load_adapter(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    )
																		UpperCAmelCase         =     True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split("w2v_model."    )[-1] == name.split("."    )[0]:
																														UpperCAmelCase         =     True
																														if "*" in mapped_key:
																																				UpperCAmelCase         =     name.split(lowerCamelCase_    )[0].split("."    )[-2]
																																				UpperCAmelCase         =     mapped_key.replace("*"    ,   lowerCamelCase_    )
																														if "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:
																																				UpperCAmelCase         =     "weight"
																														else:
																																				UpperCAmelCase         =     None
																														set_recursively(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    )
																								continue
												if not is_used:
																		unused_weights.append(lowerCamelCase_    )

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

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       Tuple:
						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:
																		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.'
																		)
																		UpperCAmelCase         =     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.'
																		)
																		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:
																		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."
																		)
																		UpperCAmelCase         =     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.'
																		)
																		UpperCAmelCase         =     value
																		logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
						else:
												unused_weights.append(lowerCamelCase_    )

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       Optional[Any]:
						UpperCAmelCase         =     full_name.split("adaptor."    )[-1]
						UpperCAmelCase         =     name.split("."    )

						if items[1].isdigit():
												UpperCAmelCase         =     int(items[1]    )
						else:
												UpperCAmelCase         =     None

						if "adaptor" not in full_name:
												if "proj_ln" in full_name:
																		# has to be layer norm
																		if "bias" in name:
																								assert (
																								    value.shape == adapter.proj_layer_norm.bias.data.shape
																								), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
																								UpperCAmelCase         =     value
																								logger.info(F'Adapter proj layer norm bias was initialized from {full_name}.'    )
																		if "weight" in name:
																								assert (
																								    value.shape == adapter.proj_layer_norm.weight.data.shape
																								), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
																								UpperCAmelCase         =     value
												else:
																		# has to be projection layer
																		if "bias" in name:
																								assert (
																								    value.shape == adapter.proj.bias.data.shape
																								), F'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
																								UpperCAmelCase         =     value
																								logger.info(F'Adapter proj layer bias was initialized from {full_name}.'    )
																		if "weight" in name:
																								assert (
																								    value.shape == adapter.proj.weight.data.shape
																								), F'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
																								UpperCAmelCase         =     value
																								logger.info(F'Adapter proj layer weight was initialized from {full_name}.'    )
						elif isinstance(lowerCamelCase_    ,   lowerCamelCase_    ):
												if "bias" in name:
																		assert (
																		    value.shape == adapter.layers[layer_id].conv.bias.data.shape
																		), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
																		UpperCAmelCase         =     value
																		logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'    )
												elif "weight" in name:
																		assert (
																		    value.shape == adapter.layers[layer_id].conv.weight.data.shape
																		), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
																		UpperCAmelCase         =     value
																		logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'    )
						else:
												unused_weights.append(lowerCamelCase_    )

def    lowerCamelCase_(lowerCamelCase_    ) ->       List[Any]:
						UpperCAmelCase       ,		UpperCAmelCase         =     emb.weight.shape
						UpperCAmelCase         =     nn.Linear(lowerCamelCase_    ,   lowerCamelCase_    ,   bias=lowerCamelCase_    )
						UpperCAmelCase         =     emb.weight.data
						return lin_layer

@torch.no_grad()
def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   ) ->       Union[str, Any]:
						UpperCAmelCase         =     WavaVecaConfig.from_pretrained(
						    lowerCamelCase_    ,   add_adapter=lowerCamelCase_    ,   adapter_stride=lowerCamelCase_    ,   adapter_kernel_size=lowerCamelCase_    ,   use_auth_token=lowerCamelCase_    ,   output_hidden_size=lowerCamelCase_    ,   )
						UpperCAmelCase         =     MBartConfig.from_pretrained(lowerCamelCase_    )

						# load model
						UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]    ,   arg_overrides={
						        "config_yaml": config_yaml_path,
						        "data": "/".join(dict_path.split("/"    )[:-1]    ),
						        "w2v_path": checkpoint_path,
						        "load_pretrained_decoder_from": None,
						    }    ,   )
						UpperCAmelCase         =     model[0].eval()

						# load feature extractor
						UpperCAmelCase         =     WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_    ,   use_auth_token=lowerCamelCase_    )

						# set weights for wav2vec2 encoder
						UpperCAmelCase         =     WavaVecaModel(lowerCamelCase_    )

						recursively_load_weights_wavaveca(model.encoder    ,   lowerCamelCase_    )

						# load decoder weights
						UpperCAmelCase         =     MBartForCausalLM(lowerCamelCase_    )
						UpperCAmelCase       ,		UpperCAmelCase         =     hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()    ,   strict=lowerCamelCase_    )
						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}'    )

						UpperCAmelCase         =     SpeechEncoderDecoderModel(encoder=lowerCamelCase_    ,   decoder=lowerCamelCase_    )
						UpperCAmelCase         =     False

						UpperCAmelCase         =     MBartaaTokenizer(lowerCamelCase_    )
						tokenizer.save_pretrained(lowerCamelCase_    )

						UpperCAmelCase         =     hf_wavavec.config.to_dict()
						UpperCAmelCase         =     tokenizer.pad_token_id
						UpperCAmelCase         =     tokenizer.bos_token_id
						UpperCAmelCase         =     tokenizer.eos_token_id
						UpperCAmelCase         =     "mbart50"
						UpperCAmelCase         =     "wav2vec2"

						UpperCAmelCase         =     tokenizer.eos_token_id
						UpperCAmelCase         =     250_004
						UpperCAmelCase         =     tokenizer.eos_token_id

						UpperCAmelCase         =     SpeechEncoderDecoderConfig.from_dict(lowerCamelCase_    )

						hf_wavavec.save_pretrained(lowerCamelCase_    )
						feature_extractor.save_pretrained(lowerCamelCase_    )


if __name__ == "__main__":
		__lowerCamelCase     :							Union[str, Any]  							=				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_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
		parser.add_argument(
		    "--encoder_config_path",
		    default="facebook/wav2vec2-xls-r-1b",
		    type=str,
		    help="Path to hf encoder wav2vec2 checkpoint config",
		)
		parser.add_argument(
		    "--decoder_config_path",
		    default="facebook/mbart-large-50-one-to-many-mmt",
		    type=str,
		    help="Path to hf decoder checkpoint config",
		)
		parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
		parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
		parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
		parser.add_argument("--encoder_output_dim", default=1_024, type=int, help="encoder output dim")
		parser.add_argument("--start_token_id", default=250_004, type=int, help="`decoder_start_token_id` of model config")

		__lowerCamelCase     :							Optional[Any]  							=				parser.parse_args()
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.dict_path,
		    args.config_yaml_path,
		    encoder_config_path=args.encoder_config_path,
		    decoder_config_path=args.decoder_config_path,
		    add_adapter=args.add_adapter,
		    adapter_kernel_size=args.adapter_kernel_size,
		    adapter_stride=args.adapter_stride,
		    decoder_start_token_id=args.start_token_id,
		    encoder_output_dim=args.encoder_output_dim,
		)








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  __magic_name__   :






		def __init__(						self      :     Any				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     str=1_00				,		UpperCamelCase__      :     List[str]=13				,		UpperCamelCase__      :     Optional[int]=30				,		UpperCamelCase__      :     Union[str, Any]=2				,		UpperCamelCase__      :     List[str]=3				,		UpperCamelCase__      :     Union[str, Any]=True				,		UpperCamelCase__      :     Tuple=True				,		UpperCamelCase__      :     List[Any]=32				,		UpperCamelCase__      :     Any=4				,		UpperCamelCase__      :     Optional[Any]=4				,		UpperCamelCase__      :     Tuple=37				,		UpperCamelCase__      :     Any="gelu"				,		UpperCamelCase__      :     Any=0.1				,		UpperCamelCase__      :     Optional[Any]=0.1				,		UpperCamelCase__      :     int=10				,		UpperCamelCase__      :     Tuple=0.02				,		UpperCamelCase__      :     str=3				,		UpperCamelCase__      :     Optional[int]=None				,		UpperCamelCase__      :     Dict=[0, 1, 2, 3]				,		)				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     parent
								UpperCAmelCase         =     1_00
								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
								UpperCAmelCase         =     scope
								UpperCAmelCase         =     out_indices
								UpperCAmelCase         =     num_labels

								# 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      :     Optional[Any]      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

								UpperCAmelCase         =     self.get_config()

								return config, pixel_values, labels, pixel_labels






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					str:
								'''simple docstring'''







								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 SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     List[str]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     BeitModel(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__      )
								self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     str      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     BeitForMaskedImageModeling(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__      )
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length - 1, self.vocab_size)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     int      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.type_sequence_label_size
								UpperCAmelCase         =     BeitForImageClassification(UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__				,		labels=UpperCamelCase__      )
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.type_sequence_label_size)      )

								# test greyscale images
								UpperCAmelCase         =     1
								UpperCAmelCase         =     BeitForImageClassification(UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     List[Any]      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.num_labels
								UpperCAmelCase         =     BeitForSemanticSegmentation(UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__      )
								self.parent.assertEqual(
								    result.logits.shape				,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )
								UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     self.prepare_config_and_inputs()
								UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     config_and_inputs
								UpperCAmelCase         =     {"pixel_values": pixel_values}
								return config, inputs_dict





@require_torch
class  __magic_name__   (							A__,  A__,  unittest.TestCase						):
		lowercase      :       Optional[Any]          =(
		    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)
		lowercase      :       Optional[Any]          =(
		    {
		        '''feature-extraction''': BeitModel,
		        '''image-classification''': BeitForImageClassification,
		        '''image-segmentation''': BeitForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		lowercase      :       Dict          =False
		lowercase      :       int          =False
		lowercase      :       Union[str, Any]          =False






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     BeitModelTester(self      )
								UpperCAmelCase         =     ConfigTester(self				,		config_class=UpperCamelCase__				,		has_text_modality=UpperCamelCase__				,		hidden_size=37      )






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Tuple:
								'''simple docstring'''







								self.config_tester.run_common_tests()






		@unittest.skip(reason="BEiT does not use inputs_embeds"      )
		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Optional[Any]:
								'''simple docstring'''







								pass






		@require_torch_multi_gpu
		@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"      )
		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Tuple:
								'''simple docstring'''







								pass






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					int:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

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

														UpperCAmelCase         =     ["pixel_values"]
														self.assertListEqual(arg_names[:1]				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					int:
								'''simple docstring'''







								if not self.model_tester.is_training:
														return

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

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

														UpperCAmelCase         =     model_class(UpperCamelCase__      )
														model.to(UpperCamelCase__      )
														model.train()
														UpperCAmelCase         =     self._prepare_for_class(UpperCamelCase__				,		UpperCamelCase__				,		return_labels=UpperCamelCase__      )
														UpperCAmelCase         =     model(**UpperCamelCase__      ).loss
														loss.backward()






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()
								if not self.model_tester.is_training:
														return

								UpperCAmelCase         =     False
								UpperCAmelCase         =     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

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

								UpperCAmelCase         =     _config_zero_init(UpperCamelCase__      )
								for model_class in self.all_model_classes:
														UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					str:
								'''simple docstring'''







								for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														UpperCAmelCase         =     BeitModel.from_pretrained(UpperCamelCase__      )
														self.assertIsNotNone(UpperCamelCase__      )

def    lowerCamelCase_() ->       str:
						UpperCAmelCase         =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
						return image





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






		@cached_property
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[Any]:
								'''simple docstring'''







								return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"      ) if is_vision_available() else None






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"      ).to(UpperCamelCase__      )

								UpperCAmelCase         =     self.default_image_processor
								UpperCAmelCase         =     prepare_img()
								UpperCAmelCase         =     image_processor(images=UpperCamelCase__				,		return_tensors="pt"      ).pixel_values.to(UpperCamelCase__      )

								# prepare bool_masked_pos
								UpperCAmelCase         =     torch.ones((1, 1_96)				,		dtype=torch.bool      ).to(UpperCamelCase__      )

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

								# verify the logits
								UpperCAmelCase         =     torch.Size((1, 1_96, 81_92)      )
								self.assertEqual(logits.shape				,		UpperCamelCase__      )

								UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"      ).to(UpperCamelCase__      )

								UpperCAmelCase         =     self.default_image_processor
								UpperCAmelCase         =     prepare_img()
								UpperCAmelCase         =     image_processor(images=UpperCamelCase__				,		return_tensors="pt"      ).to(UpperCamelCase__      )

								# forward pass
								with torch.no_grad():
														UpperCAmelCase         =     model(**UpperCamelCase__      )
								UpperCAmelCase         =     outputs.logits

								# verify the logits
								UpperCAmelCase         =     torch.Size((1, 10_00)      )
								self.assertEqual(logits.shape				,		UpperCamelCase__      )

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

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

								UpperCAmelCase         =     2_81
								self.assertEqual(logits.argmax(-1      ).item()				,		UpperCamelCase__      )






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"      ).to(
								    UpperCamelCase__      )

								UpperCAmelCase         =     self.default_image_processor
								UpperCAmelCase         =     prepare_img()
								UpperCAmelCase         =     image_processor(images=UpperCamelCase__				,		return_tensors="pt"      ).to(UpperCamelCase__      )

								# forward pass
								with torch.no_grad():
														UpperCAmelCase         =     model(**UpperCamelCase__      )
								UpperCAmelCase         =     outputs.logits

								# verify the logits
								UpperCAmelCase         =     torch.Size((1, 2_18_41)      )
								self.assertEqual(logits.shape				,		UpperCamelCase__      )

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

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

								UpperCAmelCase         =     23_96
								self.assertEqual(logits.argmax(-1      ).item()				,		UpperCamelCase__      )






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"      )
								UpperCAmelCase         =     model.to(UpperCamelCase__      )

								UpperCAmelCase         =     BeitImageProcessor(do_resize=UpperCamelCase__				,		size=6_40				,		do_center_crop=UpperCamelCase__      )

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

								# forward pass
								with torch.no_grad():
														UpperCAmelCase         =     model(**UpperCamelCase__      )
								UpperCAmelCase         =     outputs.logits

								# verify the logits
								UpperCAmelCase         =     torch.Size((1, 1_50, 1_60, 1_60)      )
								self.assertEqual(logits.shape				,		UpperCamelCase__      )

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

								if is_pillow_less_than_a:
														UpperCAmelCase         =     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:
														UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"      )
								UpperCAmelCase         =     model.to(UpperCamelCase__      )

								UpperCAmelCase         =     BeitImageProcessor(do_resize=UpperCamelCase__				,		size=6_40				,		do_center_crop=UpperCamelCase__      )

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

								# forward pass
								with torch.no_grad():
														UpperCAmelCase         =     model(**UpperCamelCase__      )

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

								UpperCAmelCase         =     image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__				,		target_sizes=[(5_00, 3_00)]      )
								UpperCAmelCase         =     torch.Size((5_00, 3_00)      )
								self.assertEqual(segmentation[0].shape				,		UpperCamelCase__      )

								UpperCAmelCase         =     image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__      )
								UpperCAmelCase         =     torch.Size((1_60, 1_60)      )
								self.assertEqual(segmentation[0].shape				,		UpperCamelCase__      )



