
import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
__a         =   logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__a         =   []
for i in range(6):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append(
						    (f"transformer.encoder.layers.{i}.self_attn.out_proj.weight", f"encoder.layers.{i}.self_attn.out_proj.weight")
						)
						rename_keys.append(
						    (f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias")
						)
						rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight"))
						rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias"))
						rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight"))
						rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias"))
						rename_keys.append(
						    (f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight")
						)
						rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias"))
						rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight"))
						rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias"))
						# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
						rename_keys.append(
						    (f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"decoder.layers.{i}.self_attn.out_proj.weight")
						)
						rename_keys.append(
						    (f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias")
						)
						rename_keys.append(
						    (
						        f"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
						        f"decoder.layers.{i}.encoder_attn.out_proj.weight",
						    )
						)
						rename_keys.append(
						    (
						        f"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
						        f"decoder.layers.{i}.encoder_attn.out_proj.bias",
						    )
						)
						rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight"))
						rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias"))
						rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight"))
						rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias"))
						rename_keys.append(
						    (f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight")
						)
						rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias"))
						rename_keys.append(
						    (f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight")
						)
						rename_keys.append(
						    (f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias")
						)
						rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight"))
						rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias"))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.encoder.norm.weight', 'encoder.layernorm.weight'),
        ('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
    ]
)




def 							a		(				snake_case__:   List[str]		,  snake_case__:   Any		,  snake_case__:   Optional[int]   ):


				'''simple docstring'''
				lowercase_	    =   state_dict.pop(snake_case__   )
				lowercase_	    =   val




def 							a		(				snake_case__:   List[str]   ):


				'''simple docstring'''
				lowercase_	    =   OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												lowercase_	    =   key.replace('''backbone.0.body'''		,  '''backbone.conv_encoder.model'''   )
												lowercase_	    =   value
								else:
												lowercase_	    =   value

				return new_state_dict




def 							a		(				snake_case__:   Dict   ):


				'''simple docstring'''
				lowercase_	    =   ''''''

				# first: transformer encoder
				for i in range(6   ):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								lowercase_	    =   state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''   )
								lowercase_	    =   state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''   )
								# next, add query, keys and values (in that order) to the state dict
								lowercase_	    =   in_proj_weight[:256, :]
								lowercase_	    =   in_proj_bias[:256]
								lowercase_	    =   in_proj_weight[256:512, :]
								lowercase_	    =   in_proj_bias[256:512]
								lowercase_	    =   in_proj_weight[-256:, :]
								lowercase_	    =   in_proj_bias[-256:]
				# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
				for i in range(6   ):
								# read in weights + bias of input projection layer of self-attention
								lowercase_	    =   state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''   )
								lowercase_	    =   state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''   )
								# next, add query, keys and values (in that order) to the state dict
								lowercase_	    =   in_proj_weight[:256, :]
								lowercase_	    =   in_proj_bias[:256]
								lowercase_	    =   in_proj_weight[256:512, :]
								lowercase_	    =   in_proj_bias[256:512]
								lowercase_	    =   in_proj_weight[-256:, :]
								lowercase_	    =   in_proj_bias[-256:]
								# read in weights + bias of input projection layer of cross-attention
								lowercase_	    =   state_dict.pop(
								    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''   )
								lowercase_	    =   state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''   )
								# next, add query, keys and values (in that order) of cross-attention to the state dict
								lowercase_	    =   in_proj_weight_cross_attn[:256, :]
								lowercase_	    =   in_proj_bias_cross_attn[:256]
								lowercase_	    =   in_proj_weight_cross_attn[256:512, :]
								lowercase_	    =   in_proj_bias_cross_attn[256:512]
								lowercase_	    =   in_proj_weight_cross_attn[-256:, :]
								lowercase_	    =   in_proj_bias_cross_attn[-256:]




def 							a		(				snake_case__:   List[str]		,  snake_case__:   Union[str, Any]   ):


				'''simple docstring'''
				lowercase_    ,      lowercase_	    =   image.size
				lowercase_	    =   max(snake_case__		,  snake_case__   )
				lowercase_	    =   800 if '''detection''' in checkpoint_url else 1_000
				lowercase_	    =   target_max_size / current_max_size
				lowercase_	    =   image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

				return resized_image




def 							a		(				snake_case__:   Optional[Any]   ):


				'''simple docstring'''
				lowercase_	    =   F.to_tensor(snake_case__   )
				lowercase_	    =   F.normalize(snake_case__		,  mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]		,  std=[0.2_2_9, 0.2_2_4, 0.2_2_5]   )
				return image




@torch.no_grad()
def 							a		(				snake_case__:   str		,  snake_case__:   List[str]		,  snake_case__:   Union[str, Any]   ):


				'''simple docstring'''
				logger.info('''Converting model...'''   )

				# load original state dict
				lowercase_	    =   torch.hub.load_state_dict_from_url(snake_case__		,  map_location='''cpu'''   )
				# rename keys
				for src, dest in rename_keys:
								rename_key(snake_case__		,  snake_case__		,  snake_case__   )
				lowercase_	    =   rename_backbone_keys(snake_case__   )
				# query, key and value matrices need special treatment
				read_in_q_k_v(snake_case__   )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				lowercase_	    =   '''model.'''
				for key in state_dict.copy().keys():
								if not key.startswith('''class_labels_classifier'''   ) and not key.startswith('''bbox_predictor'''   ):
												lowercase_	    =   state_dict.pop(snake_case__   )
												lowercase_	    =   val
    # create HuggingFace model and load state dict
				lowercase_	    =   TableTransformerConfig(
				    backbone='''resnet18'''		,  mask_loss_coefficient=1		,  dice_loss_coefficient=1		,  ce_loss_coefficient=1		,  bbox_loss_coefficient=5		,  giou_loss_coefficient=2		,  eos_coefficient=0.4		,  class_cost=1		,  bbox_cost=5		,  giou_cost=2		,  )

				if "detection" in checkpoint_url:
								lowercase_	    =   15
								lowercase_	    =   2
								lowercase_	    =   {0: '''table''', 1: '''table rotated'''}
								lowercase_	    =   idalabel
								lowercase_	    =   {v: k for k, v in idalabel.items()}
				else:
								lowercase_	    =   125
								lowercase_	    =   6
								lowercase_	    =   {
								    0: '''table''',
								    1: '''table column''',
								    2: '''table row''',
								    3: '''table column header''',
								    4: '''table projected row header''',
								    5: '''table spanning cell''',
								}
								lowercase_	    =   idalabel
								lowercase_	    =   {v: k for k, v in idalabel.items()}

				lowercase_	    =   DetrImageProcessor(
				    format='''coco_detection'''		,  max_size=800 if '''detection''' in checkpoint_url else 1_000   )
				lowercase_	    =   TableTransformerForObjectDetection(snake_case__   )
				model.load_state_dict(snake_case__   )
				model.eval()

				# verify our conversion
				lowercase_	    =   '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
				lowercase_	    =   hf_hub_download(repo_id='''nielsr/example-pdf'''		,  repo_type='''dataset'''		,  filename=snake_case__   )
				lowercase_	    =   Image.open(snake_case__   ).convert('''RGB'''   )
				lowercase_	    =   normalize(resize(snake_case__		,  snake_case__   )   ).unsqueeze(0   )

				lowercase_	    =   model(snake_case__   )

				if "detection" in checkpoint_url:
								lowercase_	    =   (1, 15, 3)
								lowercase_	    =   torch.tensor(
								    [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]   )
								lowercase_	    =   torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]   )

				else:
								lowercase_	    =   (1, 125, 7)
								lowercase_	    =   torch.tensor(
								    [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]   )
								lowercase_	    =   torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]   )

				assert outputs.logits.shape == expected_shape
				assert torch.allclose(outputs.logits[0, :3, :3]		,  snake_case__		,  atol=1e-4   )
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,  snake_case__		,  atol=1e-4   )
				print('''Looks ok!'''   )

				if pytorch_dump_folder_path is not None:
								# Save model and image processor
								logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''   )
								Path(snake_case__   ).mkdir(exist_ok=snake_case__   )
								model.save_pretrained(snake_case__   )
								image_processor.save_pretrained(snake_case__   )

				if push_to_hub:
								# Push model to HF hub
								logger.info('''Pushing model to the hub...'''   )
								lowercase_	    =   (
								    '''microsoft/table-transformer-detection'''
								    if '''detection''' in checkpoint_url
								    else '''microsoft/table-transformer-structure-recognition'''
								)
								model.push_to_hub(snake_case__   )
								image_processor.push_to_hub(snake_case__   )


if __name__ == "__main__":
						__a         =   argparse.ArgumentParser()

						parser.add_argument(
						    '--checkpoint_url',
						    default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
						    type=str,
						    choices=[
						        'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
						        'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
						    ],
						    help='URL of the Table Transformer checkpoint you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
						)
						parser.add_argument(
						    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
						)
						__a         =   parser.parse_args()
						convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Union[str, Any]						=     {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     ["""SpeechT5Tokenizer"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Any						=     [
     """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SpeechT5ForSpeechToText""",
     """SpeechT5ForSpeechToSpeech""",
     """SpeechT5ForTextToSpeech""",
     """SpeechT5Model""",
     """SpeechT5PreTrainedModel""",
     """SpeechT5HifiGan""",
 ]

if TYPE_CHECKING:
 from .configuration_speechta import (
     SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
     SpeechTaConfig,
     SpeechTaHifiGanConfig,
 )
 from .feature_extraction_speechta import SpeechTaFeatureExtractor
 from .processing_speechta import SpeechTaProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speechta import SpeechTaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speechta import (
      SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechTaForSpeechToSpeech,
      SpeechTaForSpeechToText,
      SpeechTaForTextToSpeech,
      SpeechTaHifiGan,
      SpeechTaModel,
      SpeechTaPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE		:List[str]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
