'''simple docstring'''

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:			Tuple	       =       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__a:			str	       =       []
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       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Any						= state_dict.pop(UpperCAmelCase					)
 lowercase__    :   List[Any]						= val
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Optional[Any]						= OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   lowercase__    :   Optional[int]						= key.replace('''backbone.0.body'''		,					'''backbone.conv_encoder.model'''					)
   lowercase__    :   str						= value
  else:
   lowercase__    :   List[str]						= value

 return new_state_dict
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Any						= ''''''

 # 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__    :   Tuple						= state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""					)
  lowercase__    :   List[str]						= 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__    :   List[Any]						= in_proj_weight[:256, :]
  lowercase__    :   Optional[int]						= in_proj_bias[:256]
  lowercase__    :   Optional[Any]						= in_proj_weight[256:512, :]
  lowercase__    :   Optional[Any]						= in_proj_bias[256:512]
  lowercase__    :   str						= in_proj_weight[-256:, :]
  lowercase__    :   Tuple						= 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__    :   Tuple						= state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
  lowercase__    :   Dict						= 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__    :   str						= in_proj_weight[:256, :]
  lowercase__    :   List[Any]						= in_proj_bias[:256]
  lowercase__    :   Any						= in_proj_weight[256:512, :]
  lowercase__    :   Optional[Any]						= in_proj_bias[256:512]
  lowercase__    :   int						= in_proj_weight[-256:, :]
  lowercase__    :   int						= in_proj_bias[-256:]
  # read in weights + bias of input projection layer of cross-attention
  lowercase__    :   Union[str, Any]						= state_dict.pop(
      F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""					)
  lowercase__    :   Optional[int]						= 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__    :   Union[str, Any]						= in_proj_weight_cross_attn[:256, :]
  lowercase__    :   Any						= in_proj_bias_cross_attn[:256]
  lowercase__    :   Optional[int]						= in_proj_weight_cross_attn[256:512, :]
  lowercase__    :   Optional[Any]						= in_proj_bias_cross_attn[256:512]
  lowercase__    :   Any						= in_proj_weight_cross_attn[-256:, :]
  lowercase__    :   Any						= in_proj_bias_cross_attn[-256:]
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__			,							lowercase__    :   Union[str, Any]						= image.size
 lowercase__    :   Any						= max(UpperCAmelCase		,					UpperCAmelCase					)
 lowercase__    :   Optional[int]						= 800 if '''detection''' in checkpoint_url else 1000
 lowercase__    :   Optional[Any]						= target_max_size / current_max_size
 lowercase__    :   int						= image.resize((int(round(scale * width					)					), int(round(scale * height					)					))					)

 return resized_image
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Any						= F.to_tensor(UpperCAmelCase					)
 lowercase__    :   List[Any]						= F.normalize(UpperCAmelCase		,					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       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 logger.info('''Converting model...'''					)

 # load original state dict
 lowercase__    :   Dict						= torch.hub.load_state_dict_from_url(UpperCAmelCase		,					map_location='''cpu'''					)
 # rename keys
 for src, dest in rename_keys:
  rename_key(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					)
 lowercase__    :   Dict						= rename_backbone_keys(UpperCAmelCase					)
 # query, key and value matrices need special treatment
 read_in_q_k_v(UpperCAmelCase					)
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 lowercase__    :   List[Any]						= '''model.'''
 for key in state_dict.copy().keys():
  if not key.startswith('''class_labels_classifier'''					) and not key.startswith('''bbox_predictor'''					):
   lowercase__    :   Optional[Any]						= state_dict.pop(UpperCAmelCase					)
   lowercase__    :   Dict						= val
    # create HuggingFace model and load state dict
 lowercase__    :   Optional[Any]						= 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__    :   Any						= 15
  lowercase__    :   Union[str, Any]						= 2
  lowercase__    :   Optional[int]						= {0: '''table''', 1: '''table rotated'''}
  lowercase__    :   List[Any]						= idalabel
  lowercase__    :   int						= {v: k for k, v in idalabel.items()}
 else:
  lowercase__    :   Dict						= 125
  lowercase__    :   Optional[int]						= 6
  lowercase__    :   List[str]						= {
      0: '''table''',
      1: '''table column''',
      2: '''table row''',
      3: '''table column header''',
      4: '''table projected row header''',
      5: '''table spanning cell''',
  }
  lowercase__    :   List[Any]						= idalabel
  lowercase__    :   Any						= {v: k for k, v in idalabel.items()}

 lowercase__    :   Tuple						= DetrImageProcessor(
     format='''coco_detection'''		,					max_size=800 if '''detection''' in checkpoint_url else 1000					)
 lowercase__    :   Tuple						= TableTransformerForObjectDetection(UpperCAmelCase					)
 model.load_state_dict(UpperCAmelCase					)
 model.eval()

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

 lowercase__    :   Dict						= model(UpperCAmelCase					)

 if "detection" in checkpoint_url:
  lowercase__    :   str						= (1, 15, 3)
  lowercase__    :   Union[str, Any]						= 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__    :   Dict						= 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__    :   Any						= (1, 125, 7)
  lowercase__    :   Any						= 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__    :   Tuple						= 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]		,					UpperCAmelCase		,					atol=1E-4					)
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,					UpperCAmelCase		,					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(UpperCAmelCase					).mkdir(exist_ok=UpperCAmelCase					)
  model.save_pretrained(UpperCAmelCase					)
  image_processor.save_pretrained(UpperCAmelCase					)

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


if __name__ == "__main__":
  __a:			Optional[Any]	       =       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:			Dict	       =       parser.parse_args()
  convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   DDIMPipeline
       SCREAMING_SNAKE_CASE 		=   UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       SCREAMING_SNAKE_CASE 		=   PipelineTesterMixin.required_optional_params - {
           "num_images_per_prompt",
           "latents",
           "callback",
           "callback_steps",
       }
       SCREAMING_SNAKE_CASE 		=   UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
       SCREAMING_SNAKE_CASE 		=   False

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        torch.manual_seed(0			)
        lowercase__    :   Optional[int]						= UNetaDModel(
            block_out_channels=(32, 64)     ,       layers_per_block=2     ,       sample_size=32     ,       in_channels=3     ,       out_channels=3     ,       down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')     ,       up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')     ,       )
        lowercase__    :   Any						= DDIMScheduler()
        lowercase__    :   Union[str, Any]						= {'''unet''': unet, '''scheduler''': scheduler}
        return components

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=0			)  ->  int:
        if str(__lowerCAmelCase			).startswith('''mps'''			):
         lowercase__    :   Optional[Any]						= torch.manual_seed(__lowerCAmelCase			)
        else:
         lowercase__    :   int						= torch.Generator(device=__lowerCAmelCase			).manual_seed(__lowerCAmelCase			)
        lowercase__    :   List[Any]						= {
            '''batch_size''': 1,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''output_type''': '''numpy''',
        }
        return inputs

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   int						= '''cpu'''

        lowercase__    :   Optional[Any]						= self.get_dummy_components()
        lowercase__    :   str						= self.pipeline_class(**__lowerCAmelCase			)
        pipe.to(__lowerCAmelCase			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   str						= self.get_dummy_inputs(__lowerCAmelCase			)
        lowercase__    :   str						= pipe(**__lowerCAmelCase			).images
        lowercase__    :   List[str]						= image[0, -3:, -3:, -1]

        self.assertEqual(image.shape     ,       (1, 32, 32, 3)			)
        lowercase__    :   Optional[int]						= np.array(
            [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]			)
        lowercase__    :   Optional[Any]						= np.abs(image_slice.flatten() - expected_slice			).max()
        self.assertLessEqual(__lowerCAmelCase     ,       1E-3			)

       def _lowerCAmelCase(						self			)  ->  str:
        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        super().test_save_load_local(expected_max_difference=3E-3			)

       def _lowerCAmelCase(						self			)  ->  Tuple:
        super().test_save_load_optional_components(expected_max_difference=3E-3			)


       def _lowerCAmelCase(						self			)  ->  int:
        super().test_inference_batch_single_identical(expected_max_diff=3E-3			)



@slow
@require_torch_gpu
class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   List[str]						= '''google/ddpm-cifar10-32'''

        lowercase__    :   List[Any]						= UNetaDModel.from_pretrained(__lowerCAmelCase			)
        lowercase__    :   str						= DDIMScheduler()

        lowercase__    :   List[Any]						= DDIMPipeline(unet=__lowerCAmelCase     ,       scheduler=__lowerCAmelCase			)
        ddim.to(__lowerCAmelCase			)
        ddim.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Dict						= torch.manual_seed(0			)
        lowercase__    :   Optional[int]						= ddim(generator=__lowerCAmelCase     ,       eta=0.0     ,       output_type='''numpy'''			).images

        lowercase__    :   List[str]						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        lowercase__    :   Optional[int]						= np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2


       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Optional[Any]						= '''google/ddpm-ema-bedroom-256'''

        lowercase__    :   Tuple						= UNetaDModel.from_pretrained(__lowerCAmelCase			)
        lowercase__    :   Union[str, Any]						= DDIMScheduler.from_pretrained(__lowerCAmelCase			)

        lowercase__    :   List[Any]						= DDIMPipeline(unet=__lowerCAmelCase     ,       scheduler=__lowerCAmelCase			)
        ddpm.to(__lowerCAmelCase			)
        ddpm.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Union[str, Any]						= torch.manual_seed(0			)
        lowercase__    :   Optional[Any]						= ddpm(generator=__lowerCAmelCase     ,       output_type='''numpy'''			).images

        lowercase__    :   List[Any]						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        lowercase__    :   Union[str, Any]						= np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

