





"""simple docstring"""






import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
A 			=  logging.get_logger('''transformers.models.speecht5''')

A 			=  {
    '''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
    '''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
    '''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
    '''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
A 			=  {
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
A 			=  {
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
    '''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
    '''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
    '''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
A 			=  {
    '''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
    '''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
    '''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
    '''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
    '''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
    '''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
    '''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
    '''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
A 			=  {
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
A 			=  {
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
A 			=  {
    '''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
    '''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
    '''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
    '''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
    '''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
    '''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
    '''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
    '''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
    '''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
A 			=  {
    '''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
    '''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
    '''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
    '''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
    '''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
    '''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
    '''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
    '''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
    '''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
    '''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
    '''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
    '''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
    '''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
A 			=  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
A 			=  {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A 			=  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A 			=  []
A 			=  [
    '''encoder.version''',
    '''encoder.layers.*.norm_k.weight''',
    '''encoder.layers.*.norm_k.bias''',
    '''decoder.version''',
    '''decoder.layers.*.norm_k.weight''',
    '''decoder.layers.*.norm_k.bias''',
    '''decoder.pos_emb.pe_k''',
    '''speech_encoder_prenet.embed_positions._float_tensor''',
    '''text_decoder_prenet.embed_positions._float_tensor''',
]
A 			=  IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
A 			=  IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
A 			=  IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]

def     __A      (			a_						:Any   ,       a_						:List[str]   ,       a_						:str   ,       a_						:Optional[Any]   ,       a_						:Optional[Any])		->		str:
				for attribute in key.split('''.'''):
								__a :						Optional[Any]			       =  getattr(a_   ,       a_)

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

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

def     __A      (			a_						:str   ,       a_						:Optional[int])		->		int:
				for key in ignore_keys:
								if key.endswith('''.*'''):
												if name.startswith(key[:-1]):
																return True
								elif ".*." in key:
												__a       ,						__a :						Optional[Any]			       =  key.split('''.*.''')
												if prefix in name and suffix in name:
																return True
								elif key in name:
												return True
				return False

def     __A      (			a_						:str   ,       a_						:str   ,       a_						:Union[str, Any])		->		Dict:
				__a :						List[Any]			       =  []

				if task == "s2t":
								__a :						Optional[int]			       =  hf_model.speechta.encoder.prenet.feature_encoder
								__a :						int			       =  MAPPING_S2T
								__a :						List[str]			       =  IGNORE_KEYS_S2T
				elif task == "t2s":
								__a :						int			       =  None
								__a :						List[Any]			       =  MAPPING_T2S
								__a :						List[Any]			       =  IGNORE_KEYS_T2S
				elif task == "s2s":
								__a :						Dict			       =  hf_model.speechta.encoder.prenet.feature_encoder
								__a :						int			       =  MAPPING_S2S
								__a :						Dict			       =  IGNORE_KEYS_S2S
				else:
								raise ValueError(F"""Unsupported task: {task}""")

				for name, value in fairseq_dict.items():
								if should_ignore(a_   ,       a_):
												logger.info(F"""{name} was ignored""")
												continue

								__a :						int			       =  False
								if "conv_layers" in name:
												load_conv_layer(
												    a_   ,       a_   ,       a_   ,       a_   ,       hf_model.config.feat_extract_norm == '''group'''   ,       )
												__a :						str			       =  True
								else:
												for key, mapped_key in MAPPING.items():
																# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

																if "*" in key:
																				__a       ,						__a :						List[Any]			       =  key.split('''.*.''')
																				if prefix in name and suffix in name:
																								__a :						List[Any]			       =  suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																if key in name:
																				__a :						List[Any]			       =  True
																				if "*" in mapped_key:
																								__a :						Union[str, Any]			       =  name.split(a_)[0].split('''.''')[-2]
																								__a :						Optional[Any]			       =  mapped_key.replace('''*'''   ,       a_)
																				if "weight_g" in name:
																								__a :						Dict			       =  '''weight_g'''
																				elif "weight_v" in name:
																								__a :						List[str]			       =  '''weight_v'''
																				elif "bias" in name:
																								__a :						List[Any]			       =  '''bias'''
																				elif "weight" in name:
																								__a :						List[Any]			       =  '''weight'''
																				elif "running_mean" in name:
																								__a :						Tuple			       =  '''running_mean'''
																				elif "running_var" in name:
																								__a :						Tuple			       =  '''running_var'''
																				elif "num_batches_tracked" in name:
																								__a :						Tuple			       =  '''num_batches_tracked'''
																				else:
																								__a :						Union[str, Any]			       =  None
																				set_recursively(a_   ,       a_   ,       a_   ,       a_   ,       a_)
																continue
								if not is_used:
												unused_weights.append(a_)

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

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

@torch.no_grad()
def     __A      (			a_						:str   ,       a_						:str   ,       a_						:Tuple   ,       a_						:Union[str, Any]=None   ,       a_						:Any=None   ,       a_						:Union[str, Any]=None   ,       )		->		Optional[Any]:
				if config_path is not None:
								__a :						int			       =  SpeechTaConfig.from_pretrained(a_)
				else:
								__a :						List[str]			       =  SpeechTaConfig()

				if task == "s2t":
								__a :						Any			       =  config.max_text_positions
								__a :						Optional[Any]			       =  SpeechTaForSpeechToText(a_)
				elif task == "t2s":
								__a :						Dict			       =  18_76
								__a :						str			       =  6_00
								__a :						str			       =  config.max_speech_positions
								__a :						List[Any]			       =  SpeechTaForTextToSpeech(a_)
				elif task == "s2s":
								__a :						List[Any]			       =  18_76
								__a :						Optional[int]			       =  config.max_speech_positions
								__a :						Optional[int]			       =  SpeechTaForSpeechToSpeech(a_)
				else:
								raise ValueError(F"""Unknown task name: {task}""")

				if vocab_path:
								__a :						Any			       =  SpeechTaTokenizer(a_   ,       model_max_length=config.max_text_positions)

								# Mask token behaves like a normal word, i.e. include the space before it
								__a :						Optional[Any]			       =  AddedToken('''<mask>'''   ,       lstrip=a_   ,       rstrip=a_)
								__a :						int			       =  mask_token
								tokenizer.add_special_tokens({'''mask_token''': mask_token})
								tokenizer.add_tokens(['''<ctc_blank>'''])

				__a :						int			       =  SpeechTaFeatureExtractor()
				__a :						Dict			       =  SpeechTaProcessor(tokenizer=a_   ,       feature_extractor=a_)
				processor.save_pretrained(a_)

				__a :						str			       =  torch.load(a_)
				recursively_load_weights(fairseq_checkpoint['''model''']   ,       a_   ,       a_)

				model.save_pretrained(a_)

				if repo_id:
								print('''Pushing to the hub...''')
								processor.push_to_hub(a_)
								model.push_to_hub(a_)


if __name__ == "__main__":
		A 			=  argparse.ArgumentParser()
		parser.add_argument(
		    '''--task''',
		    default='''s2t''',
		    type=str,
		    help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
		)
		parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
		parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
		)

		A 			=  parser.parse_args()
		convert_speechta_checkpoint(
		    args.task,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.vocab_path,
		    args.push_to_hub,
		)






"""simple docstring"""






import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
		    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    LayoutLMvaConfig,
		    LayoutLMvaForQuestionAnswering,
		    LayoutLMvaForSequenceClassification,
		    LayoutLMvaForTokenClassification,
		    LayoutLMvaModel,
		)
		from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
		from PIL import Image

		from transformers import LayoutLMvaImageProcessor




class 				__lowercase			:







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=2	,			_UpperCAmelCase=3	,			_UpperCAmelCase=4	,			_UpperCAmelCase=2	,			_UpperCAmelCase=7	,			_UpperCAmelCase=True	,			_UpperCAmelCase=True	,			_UpperCAmelCase=True	,			_UpperCAmelCase=True	,			_UpperCAmelCase=99	,			_UpperCAmelCase=36	,			_UpperCAmelCase=3	,			_UpperCAmelCase=4	,			_UpperCAmelCase=37	,			_UpperCAmelCase="gelu"	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=512	,			_UpperCAmelCase=16	,			_UpperCAmelCase=2	,			_UpperCAmelCase=0.0_2	,			_UpperCAmelCase=6	,			_UpperCAmelCase=6	,			_UpperCAmelCase=3	,			_UpperCAmelCase=4	,			_UpperCAmelCase=None	,			_UpperCAmelCase=1000	,			):
									__a :						Dict			       =  parent
									__a :						Optional[int]			       =  batch_size
									__a :						Optional[int]			       =  num_channels
									__a :						List[Any]			       =  image_size
									__a :						int			       =  patch_size
									__a :						Tuple			       =  text_seq_length
									__a :						Dict			       =  is_training
									__a :						str			       =  use_input_mask
									__a :						Optional[int]			       =  use_token_type_ids
									__a :						List[Any]			       =  use_labels
									__a :						Tuple			       =  vocab_size
									__a :						str			       =  hidden_size
									__a :						Any			       =  num_hidden_layers
									__a :						List[str]			       =  num_attention_heads
									__a :						str			       =  intermediate_size
									__a :						int			       =  hidden_act
									__a :						List[Any]			       =  hidden_dropout_prob
									__a :						Tuple			       =  attention_probs_dropout_prob
									__a :						Tuple			       =  max_position_embeddings
									__a :						List[Any]			       =  type_vocab_size
									__a :						int			       =  type_sequence_label_size
									__a :						str			       =  initializer_range
									__a :						Dict			       =  coordinate_size
									__a :						int			       =  shape_size
									__a :						int			       =  num_labels
									__a :						Optional[int]			       =  num_choices
									__a :						Any			       =  scope
									__a :						Any			       =  range_bbox

									# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
									__a :						Optional[int]			       =  text_seq_length
									__a :						str			       =  (image_size // patch_size) ** 2 + 1
									__a :						Tuple			       =  self.text_seq_length + self.image_seq_length







					def  _lowerCamelCase		(						self     ):
									__a :						List[Any]			       =  ids_tensor([self.batch_size, self.text_seq_length]	,			self.vocab_size     )

									__a :						Union[str, Any]			       =  ids_tensor([self.batch_size, self.text_seq_length, 4]	,			self.range_bbox     )
									# Ensure that bbox is legal
									for i in range(bbox.shape[0]     ):
													for j in range(bbox.shape[1]     ):
																	if bbox[i, j, 3] < bbox[i, j, 1]:
																					__a :						Optional[Any]			       =  bbox[i, j, 3]
																					__a :						Union[str, Any]			       =  bbox[i, j, 1]
																					__a :						Tuple			       =  t
																	if bbox[i, j, 2] < bbox[i, j, 0]:
																					__a :						Optional[int]			       =  bbox[i, j, 2]
																					__a :						Optional[int]			       =  bbox[i, j, 0]
																					__a :						str			       =  t

									__a :						str			       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

									__a :						Union[str, Any]			       =  None
									if self.use_input_mask:
													__a :						int			       =  random_attention_mask([self.batch_size, self.text_seq_length]     )

									__a :						int			       =  None
									if self.use_token_type_ids:
													__a :						Union[str, Any]			       =  ids_tensor([self.batch_size, self.text_seq_length]	,			self.type_vocab_size     )

									__a :						Optional[int]			       =  None
									__a :						Dict			       =  None
									if self.use_labels:
													__a :						Optional[Any]			       =  ids_tensor([self.batch_size]	,			self.type_sequence_label_size     )
													__a :						Optional[int]			       =  ids_tensor([self.batch_size, self.text_seq_length]	,			self.num_labels     )

									__a :						int			       =  LayoutLMvaConfig(
									    vocab_size=self.vocab_size	,			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	,			max_position_embeddings=self.max_position_embeddings	,			type_vocab_size=self.type_vocab_size	,			initializer_range=self.initializer_range	,			coordinate_size=self.coordinate_size	,			shape_size=self.shape_size	,			input_size=self.image_size	,			patch_size=self.patch_size	,			)

									return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						Union[str, Any]			       =  LayoutLMvaModel(config=_UpperCAmelCase     )
									model.to(_UpperCAmelCase     )
									model.eval()

									# text + image
									__a :						Union[str, Any]			       =  model(_UpperCAmelCase	,			pixel_values=_UpperCAmelCase     )
									__a :						List[Any]			       =  model(
									    _UpperCAmelCase	,			bbox=_UpperCAmelCase	,			pixel_values=_UpperCAmelCase	,			attention_mask=_UpperCAmelCase	,			token_type_ids=_UpperCAmelCase     )
									__a :						Optional[int]			       =  model(_UpperCAmelCase	,			bbox=_UpperCAmelCase	,			pixel_values=_UpperCAmelCase	,			token_type_ids=_UpperCAmelCase     )
									__a :						List[str]			       =  model(_UpperCAmelCase	,			bbox=_UpperCAmelCase	,			pixel_values=_UpperCAmelCase     )

									self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, self.seq_length, self.hidden_size)     )

									# text only
									__a :						int			       =  model(_UpperCAmelCase     )
									self.parent.assertEqual(
									    result.last_hidden_state.shape	,			(self.batch_size, self.text_seq_length, self.hidden_size)     )

									# image only
									__a :						Tuple			       =  model(pixel_values=_UpperCAmelCase     )
									self.parent.assertEqual(
									    result.last_hidden_state.shape	,			(self.batch_size, self.image_seq_length, self.hidden_size)     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						Optional[Any]			       =  self.num_labels
									__a :						Optional[Any]			       =  LayoutLMvaForSequenceClassification(_UpperCAmelCase     )
									model.to(_UpperCAmelCase     )
									model.eval()
									__a :						Union[str, Any]			       =  model(
									    _UpperCAmelCase	,			bbox=_UpperCAmelCase	,			pixel_values=_UpperCAmelCase	,			attention_mask=_UpperCAmelCase	,			token_type_ids=_UpperCAmelCase	,			labels=_UpperCAmelCase	,			)
									self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.num_labels)     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						List[str]			       =  self.num_labels
									__a :						int			       =  LayoutLMvaForTokenClassification(config=_UpperCAmelCase     )
									model.to(_UpperCAmelCase     )
									model.eval()
									__a :						List[str]			       =  model(
									    _UpperCAmelCase	,			bbox=_UpperCAmelCase	,			pixel_values=_UpperCAmelCase	,			attention_mask=_UpperCAmelCase	,			token_type_ids=_UpperCAmelCase	,			labels=_UpperCAmelCase	,			)
									self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.text_seq_length, self.num_labels)     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						List[Any]			       =  LayoutLMvaForQuestionAnswering(config=_UpperCAmelCase     )
									model.to(_UpperCAmelCase     )
									model.eval()
									__a :						List[str]			       =  model(
									    _UpperCAmelCase	,			bbox=_UpperCAmelCase	,			pixel_values=_UpperCAmelCase	,			attention_mask=_UpperCAmelCase	,			token_type_ids=_UpperCAmelCase	,			start_positions=_UpperCAmelCase	,			end_positions=_UpperCAmelCase	,			)
									self.parent.assertEqual(result.start_logits.shape	,			(self.batch_size, self.seq_length)     )
									self.parent.assertEqual(result.end_logits.shape	,			(self.batch_size, self.seq_length)     )







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  self.prepare_config_and_inputs()
									(
									    (
									    __a
									)       ,						(
									    __a
									)       ,						(
									    __a
									)       ,						(
									    __a
									)       ,						(
									    __a
									)       ,						(
									    __a
									)       ,						(
									    __a
									)       ,						(
									    __a
									)       ,						
									) :						List[Any]			       =  config_and_inputs
									__a :						Optional[int]			       =  {
									    '''input_ids''': input_ids,
									    '''bbox''': bbox,
									    '''pixel_values''': pixel_values,
									    '''token_type_ids''': token_type_ids,
									    '''attention_mask''': input_mask,
									}
									return config, inputs_dict






@require_torch
class 				__lowercase			(			_UpperCamelCase   ,      _UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  False
					__lowerCAmelCase  =  False
					__lowerCAmelCase  =  False

					__lowerCAmelCase  =  (
					    (
					        LayoutLMvaModel,
					        LayoutLMvaForSequenceClassification,
					        LayoutLMvaForTokenClassification,
					        LayoutLMvaForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					__lowerCAmelCase  =  (
					    {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
					    if is_torch_available()
					    else {}
					)







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
									# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
									# the sequence dimension of the text embedding only.
									# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
									return True







					def  _lowerCamelCase		(						self     ):
									__a :						str			       =  LayoutLMvaModelTester(self     )
									__a :						int			       =  ConfigTester(self	,			config_class=_UpperCAmelCase	,			hidden_size=37     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase=False     ):
									__a :						int			       =  copy.deepcopy(_UpperCAmelCase     )
									if model_class in get_values(_UpperCAmelCase     ):
													__a :						List[str]			       =  {
													    k: v.unsqueeze(1     ).expand(-1	,			self.model_tester.num_choices	,			-1     ).contiguous()
													    if isinstance(_UpperCAmelCase	,			torch.Tensor     ) and v.ndim > 1
													    else v
													    for k, v in inputs_dict.items()
													}
									if return_labels:
													if model_class in get_values(_UpperCAmelCase     ):
																	__a :						Dict			       =  torch.ones(self.model_tester.batch_size	,			dtype=torch.long	,			device=_UpperCAmelCase     )
													elif model_class in get_values(_UpperCAmelCase     ):
																	__a :						int			       =  torch.zeros(
																	    self.model_tester.batch_size	,			dtype=torch.long	,			device=_UpperCAmelCase     )
																	__a :						Any			       =  torch.zeros(
																	    self.model_tester.batch_size	,			dtype=torch.long	,			device=_UpperCAmelCase     )
													elif model_class in [
													    *get_values(_UpperCAmelCase     ),
													]:
																	__a :						Any			       =  torch.zeros(
																	    self.model_tester.batch_size	,			dtype=torch.long	,			device=_UpperCAmelCase     )
													elif model_class in [
													    *get_values(_UpperCAmelCase     ),
													]:
																	__a :						int			       =  torch.zeros(
																	    (self.model_tester.batch_size, self.model_tester.text_seq_length)	,			dtype=torch.long	,			device=_UpperCAmelCase	,			)

									return inputs_dict







					def  _lowerCamelCase		(						self     ):
									self.config_tester.run_common_tests()







					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
													__a :						Optional[Any]			       =  type
													self.model_tester.create_and_check_model(*_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						int			       =  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase     )







					@slow
					def  _lowerCamelCase		(						self     ):
									for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__a :						Tuple			       =  LayoutLMvaModel.from_pretrained(_UpperCAmelCase     )
													self.assertIsNotNone(_UpperCAmelCase     )






def     __A      (			)		->		Dict:
				__a :						Tuple			       =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
				return image






@require_torch
class 				__lowercase			(			unittest.TestCase							):







					'''simple docstring'''




					@cached_property
					def  _lowerCamelCase		(						self     ):
									return LayoutLMvaImageProcessor(apply_ocr=_UpperCAmelCase     ) if is_vision_available() else None







					@slow
					def  _lowerCamelCase		(						self     ):
									__a :						List[Any]			       =  LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''     ).to(_UpperCAmelCase     )

									__a :						Dict			       =  self.default_image_processor
									__a :						Tuple			       =  prepare_img()
									__a :						Optional[Any]			       =  image_processor(images=_UpperCAmelCase	,			return_tensors='''pt'''     ).pixel_values.to(_UpperCAmelCase     )

									__a :						Tuple			       =  torch.tensor([[1, 2]]     )
									__a :						Dict			       =  torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]     ).unsqueeze(0     )

									# forward pass
									__a :						Tuple			       =  model(
									    input_ids=input_ids.to(_UpperCAmelCase     )	,			bbox=bbox.to(_UpperCAmelCase     )	,			pixel_values=pixel_values.to(_UpperCAmelCase     )	,			)

									# verify the logits
									__a :						Any			       =  torch.Size((1, 199, 768)     )
									self.assertEqual(outputs.last_hidden_state.shape	,			_UpperCAmelCase     )

									__a :						Union[str, Any]			       =  torch.tensor(
									    [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]     ).to(_UpperCAmelCase     )

									self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]	,			_UpperCAmelCase	,			atol=1e-4     )     )