



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()
snake_case__					:     Union[str, Any]    		=   logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case__					:     Optional[int]    		=   []
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    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			state_dict.pop(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			val



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__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    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__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[:2_5_6, :]
		__lowercase		       =			in_proj_bias[:2_5_6]
		__lowercase		       =			in_proj_weight[2_5_6:5_1_2, :]
		__lowercase		       =			in_proj_bias[2_5_6:5_1_2]
		__lowercase		       =			in_proj_weight[-2_5_6:, :]
		__lowercase		       =			in_proj_bias[-2_5_6:]
	# 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[:2_5_6, :]
		__lowercase		       =			in_proj_bias[:2_5_6]
		__lowercase		       =			in_proj_weight[2_5_6:5_1_2, :]
		__lowercase		       =			in_proj_bias[2_5_6:5_1_2]
		__lowercase		       =			in_proj_weight[-2_5_6:, :]
		__lowercase		       =			in_proj_bias[-2_5_6:]
		# 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[:2_5_6, :]
		__lowercase		       =			in_proj_bias_cross_attn[:2_5_6]
		__lowercase		       =			in_proj_weight_cross_attn[2_5_6:5_1_2, :]
		__lowercase		       =			in_proj_bias_cross_attn[2_5_6:5_1_2]
		__lowercase		       =			in_proj_weight_cross_attn[-2_5_6:, :]
		__lowercase		       =			in_proj_bias_cross_attn[-2_5_6:]



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase       ,    __lowercase		       =			image.size
	__lowercase		       =			max(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			8_0_0 if "detection" in checkpoint_url else 1_0_0_0
	__lowercase		       =			target_max_size / current_max_size
	__lowercase		       =			image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

	return resized_image



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			F.to_tensor(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			F.normalize(_SCREAMING_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    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	logger.info("Converting model..."   )

	# load original state dict
	__lowercase		       =			torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE  ,	map_location="cpu"   )
	# rename keys
	for src, dest in rename_keys:
		rename_key(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			rename_backbone_keys(_SCREAMING_SNAKE_CASE   )
	# query, key and value matrices need special treatment
	read_in_q_k_v(_SCREAMING_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(_SCREAMING_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		       =			1_5
		__lowercase		       =			2
		__lowercase		       =			{0: "table", 1: "table rotated"}
		__lowercase		       =			idalabel
		__lowercase		       =			{v: k for k, v in idalabel.items()}
	else:
		__lowercase		       =			1_2_5
		__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=8_0_0 if "detection" in checkpoint_url else 1_0_0_0   )
	__lowercase		       =			TableTransformerForObjectDetection(_SCREAMING_SNAKE_CASE   )
	model.load_state_dict(_SCREAMING_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=_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			Image.open(_SCREAMING_SNAKE_CASE   ).convert("RGB"   )
	__lowercase		       =			normalize(resize(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )   ).unsqueeze(0   )

	__lowercase		       =			model(_SCREAMING_SNAKE_CASE   )

	if "detection" in checkpoint_url:
		__lowercase		       =			(1, 1_5, 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, 1_2_5, 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]  ,	_SCREAMING_SNAKE_CASE  ,	atol=1E-4   )
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,	_SCREAMING_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(_SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE   )
		model.save_pretrained(_SCREAMING_SNAKE_CASE   )
		image_processor.save_pretrained(_SCREAMING_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(_SCREAMING_SNAKE_CASE   )
		image_processor.push_to_hub(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	snake_case__					:     Tuple    		=   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."""
	)
	snake_case__					:     List[str]    		=   parser.parse_args()
	convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
snake_case__					:     Optional[Any]    		=   logging.getLogger()



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			"\n".join(_SCREAMING_SNAKE_CASE   )
	Path(_SCREAMING_SNAKE_CASE   ).open("w"   ).writelines(_SCREAMING_SNAKE_CASE   )


snake_case__					:     List[str]    		=   """patrickvonplaten/t5-tiny-random"""
snake_case__					:     int    		=   """sshleifer/bart-tiny-random"""
snake_case__					:     Union[str, Any]    		=   """sshleifer/tiny-mbart"""

snake_case__					:     List[str]    		=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks

class   _A   ( _lowercase	):


						'''simple docstring'''





						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
							__lowercase		       =			input_file_name.parent / "utest_output.txt"
							assert not output_file_name.exists()
							__lowercase		       =			[" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
							_dump_articles(lowerCamelCase							,      lowerCamelCase	)

							__lowercase		       =			str(Path(self.get_auto_remove_tmp_dir()	) / "scores.json"	)
							__lowercase		       =			"translation_en_to_de" if model == T5_TINY else "summarization"
							__lowercase		       =			f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

							with patch.object(lowerCamelCase							,      "argv"							,      lowerCamelCase	):
								run_generate()
								assert Path(lowerCamelCase	).exists()
								# os.remove(Path(output_file_name))



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							self.run_eval_tester(lowerCamelCase	)



						@parameterized.expand([BART_TINY, MBART_TINY]	)
						@slow
						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							self.run_eval_tester(lowerCamelCase	)



						@parameterized.expand([T5_TINY, MBART_TINY]	)
						@slow
						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
							__lowercase		       =			input_file_name.parent / "utest_output.txt"
							assert not output_file_name.exists()

							__lowercase		       =			{
							    "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
							    "de": [
							        "Maschinelles Lernen ist großartig, oder?",
							        "Ich esse gerne Bananen",
							        "Morgen ist wieder ein toller Tag!",
							    ],
							}

							__lowercase		       =			Path(self.get_auto_remove_tmp_dir()	)
							__lowercase		       =			str(tmp_dir / "scores.json"	)
							__lowercase		       =			str(tmp_dir / "val.target"	)
							_dump_articles(lowerCamelCase							,      text["en"]	)
							_dump_articles(lowerCamelCase							,      text["de"]	)
							__lowercase		       =			"translation_en_to_de" if model == T5_TINY else "summarization"
							__lowercase		       =			f"""
            run_eval_search.py
            {model}
            {str(lowerCamelCase	)}
            {str(lowerCamelCase	)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
							testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"]	)

							with patch.object(lowerCamelCase							,      "argv"							,      lowerCamelCase	):
								with CaptureStdout() as cs:
									run_search()
								__lowercase		       =			[" num_beams | length_penalty", model, "Best score args"]
								__lowercase		       =			["Info"]
								if "translation" in task:
									expected_strings.append("bleu"	)
								else:
									expected_strings.extend(lowerCamelCase	)
								for w in expected_strings:
									assert w in cs.out
								for w in un_expected_strings:
									assert w not in cs.out
								assert Path(lowerCamelCase	).exists()
								os.remove(Path(lowerCamelCase	)	)



