






'''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()
__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase      :       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	(				SCREAMING_SNAKE_CASE__:	Union[str, Any],					SCREAMING_SNAKE_CASE__:	Any,					SCREAMING_SNAKE_CASE__:	Any    )  ->			Union[str, Any]:

   """simple docstring"""
   __a				     =    state_dict.pop(SCREAMING_SNAKE_CASE__    )
   __a				     =    val



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	str    )  ->			int:

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

   return new_state_dict



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Tuple    )  ->			Dict:

   """simple docstring"""
   __a				     =    ''

   # 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)
      __a				     =    state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""    )
      __a				     =    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
      __a				     =    in_proj_weight[:256, :]
      __a				     =    in_proj_bias[:256]
      __a				     =    in_proj_weight[256:512, :]
      __a				     =    in_proj_bias[256:512]
      __a				     =    in_proj_weight[-256:, :]
      __a				     =    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
      __a				     =    state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""    )
      __a				     =    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
      __a				     =    in_proj_weight[:256, :]
      __a				     =    in_proj_bias[:256]
      __a				     =    in_proj_weight[256:512, :]
      __a				     =    in_proj_bias[256:512]
      __a				     =    in_proj_weight[-256:, :]
      __a				     =    in_proj_bias[-256:]
      # read in weights + bias of input projection layer of cross-attention
      __a				     =    state_dict.pop(
          f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""    )
      __a				     =    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
      __a				     =    in_proj_weight_cross_attn[:256, :]
      __a				     =    in_proj_bias_cross_attn[:256]
      __a				     =    in_proj_weight_cross_attn[256:512, :]
      __a				     =    in_proj_bias_cross_attn[256:512]
      __a				     =    in_proj_weight_cross_attn[-256:, :]
      __a				     =    in_proj_bias_cross_attn[-256:]



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Union[str, Any],					SCREAMING_SNAKE_CASE__:	Optional[int]    )  ->			Any:

   """simple docstring"""
   __a     ,			__a				     =    image.size
   __a				     =    max(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )
   __a				     =    800 if 'detection' in checkpoint_url else 1000
   __a				     =    target_max_size / current_max_size
   __a				     =    image.resize((int(round(scale * width    )    ), int(round(scale * height    )    ))    )

   return resized_image



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			str:

   """simple docstring"""
   __a				     =    F.to_tensor(SCREAMING_SNAKE_CASE__    )
   __a				     =    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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Tuple,					SCREAMING_SNAKE_CASE__:	Any,					SCREAMING_SNAKE_CASE__:	Any    )  ->			Tuple:

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

   # load original state dict
   __a				     =    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__    )
   __a				     =    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
   __a				     =    'model.'
   for key in state_dict.copy().keys():
      if not key.startswith('class_labels_classifier'    ) and not key.startswith('bbox_predictor'    ):
         __a				     =    state_dict.pop(SCREAMING_SNAKE_CASE__    )
         __a				     =    val
    # create HuggingFace model and load state dict
   __a				     =    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:
      __a				     =    15
      __a				     =    2
      __a				     =    {0: 'table', 1: 'table rotated'}
      __a				     =    idalabel
      __a				     =    {v: k for k, v in idalabel.items()}
   else:
      __a				     =    125
      __a				     =    6
      __a				     =    {
          0: 'table',
          1: 'table column',
          2: 'table row',
          3: 'table column header',
          4: 'table projected row header',
          5: 'table spanning cell',
      }
      __a				     =    idalabel
      __a				     =    {v: k for k, v in idalabel.items()}

   __a				     =    DetrImageProcessor(
       format='coco_detection',					max_size=800 if 'detection' in checkpoint_url else 1000    )
   __a				     =    TableTransformerForObjectDetection(SCREAMING_SNAKE_CASE__    )
   model.load_state_dict(SCREAMING_SNAKE_CASE__    )
   model.eval()

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

   __a				     =    model(SCREAMING_SNAKE_CASE__    )

   if "detection" in checkpoint_url:
      __a				     =    (1, 15, 3)
      __a				     =    torch.tensor(
          [[-6.7_8_9_7, -16.9985, 6.7_9_3_7], [-8.0_1_8_6, -22.2192, 6.9_6_7_7], [-7.3_1_1_7, -21.0708, 7.4_0_5_5]]    )
      __a				     =    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:
      __a				     =    (1, 125, 7)
      __a				     =    torch.tensor(
          [[-18.1430, -8.3_2_1_4, 4.8_2_7_4], [-18.4685, -7.1_3_6_1, -4.2_6_6_7], [-26.3693, -9.3_4_2_9, -4.9_9_6_2]]    )
      __a				     =    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...'    )
      __a				     =    (
          '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__":
 __UpperCamelCase      :       Optional[int]      			=      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."""
 )
 __UpperCamelCase      :       Tuple      			=      parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
 from datasets import load_dataset
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="microsoft/speecht5_tts"
      __a  					=(
          "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
          "text to read (in English) and returns a waveform object containing the sound."
      )
      __a  					="text_reader"
      __a  					=SpeechTaProcessor
      __a  					=SpeechTaForTextToSpeech
      __a  					=SpeechTaHifiGan

      __a  					=["text"]
      __a  					=["audio"]




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         if self.post_processor is None:
            __a				     =    'microsoft/speecht5_hifigan'
         super().setup()




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=None      )							->str:



         '''simple docstring'''
         __a				     =    self.pre_processor(text=lowerCamelCase ,   return_tensors='pt' ,   truncation=lowerCamelCase      )

         if speaker_embeddings is None:
            if not is_datasets_available():
               raise ImportError('Datasets needs to be installed if not passing speaker embeddings.'      )

            __a				     =    load_dataset('Matthijs/cmu-arctic-xvectors' ,   split='validation'      )
            __a				     =    torch.tensor(embeddings_dataset[7305]['xvector']      ).unsqueeze(0      )

         return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.model.generate_speech(**lowerCamelCase      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.post_processor(lowerCamelCase      ).cpu().detach()