




"""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()
lowerCAmelCase							:    Optional[Any]  							=							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase							:    Optional[Any]  							=							[]
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__ ,				snake_case__ ,				snake_case__       )		->      List[str]:
			lowerCamelCase       =      state_dict.pop(snake_case__       )
			lowerCamelCase       =      val



def 					a__				(       snake_case__       )		->      List[str]:
			lowerCamelCase       =      OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									lowerCamelCase       =      key.replace("""backbone.0.body""" ,				"""backbone.conv_encoder.model"""       )
									lowerCamelCase       =      value
						else:
									lowerCamelCase       =      value

			return new_state_dict



def 					a__				(       snake_case__       )		->      List[str]:
			lowerCamelCase       =      """"""

			# 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)
						lowerCamelCase       =      state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'       )
						lowerCamelCase       =      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
						lowerCamelCase       =      in_proj_weight[:2_56, :]
						lowerCamelCase       =      in_proj_bias[:2_56]
						lowerCamelCase       =      in_proj_weight[2_56:5_12, :]
						lowerCamelCase       =      in_proj_bias[2_56:5_12]
						lowerCamelCase       =      in_proj_weight[-2_56:, :]
						lowerCamelCase       =      in_proj_bias[-2_56:]
			# 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
						lowerCamelCase       =      state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'       )
						lowerCamelCase       =      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
						lowerCamelCase       =      in_proj_weight[:2_56, :]
						lowerCamelCase       =      in_proj_bias[:2_56]
						lowerCamelCase       =      in_proj_weight[2_56:5_12, :]
						lowerCamelCase       =      in_proj_bias[2_56:5_12]
						lowerCamelCase       =      in_proj_weight[-2_56:, :]
						lowerCamelCase       =      in_proj_bias[-2_56:]
						# read in weights + bias of input projection layer of cross-attention
						lowerCamelCase       =      state_dict.pop(
						    F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'       )
						lowerCamelCase       =      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
						lowerCamelCase       =      in_proj_weight_cross_attn[:2_56, :]
						lowerCamelCase       =      in_proj_bias_cross_attn[:2_56]
						lowerCamelCase       =      in_proj_weight_cross_attn[2_56:5_12, :]
						lowerCamelCase       =      in_proj_bias_cross_attn[2_56:5_12]
						lowerCamelCase       =      in_proj_weight_cross_attn[-2_56:, :]
						lowerCamelCase       =      in_proj_bias_cross_attn[-2_56:]



def 					a__				(       snake_case__ ,				snake_case__       )		->      str:
			lowerCamelCase   ,     lowerCamelCase       =      image.size
			lowerCamelCase       =      max(snake_case__ ,				snake_case__       )
			lowerCamelCase       =      8_00 if """detection""" in checkpoint_url else 10_00
			lowerCamelCase       =      target_max_size / current_max_size
			lowerCamelCase       =      image.resize((int(round(scale * width       )       ), int(round(scale * height       )       ))       )

			return resized_image



def 					a__				(       snake_case__       )		->      List[str]:
			lowerCamelCase       =      F.to_tensor(snake_case__       )
			lowerCamelCase       =      F.normalize(snake_case__ ,				mean=[0.485, 0.456, 0.406] ,				std=[0.229, 0.224, 0.225]       )
			return image



@torch.no_grad()
def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      Tuple:
			logger.info("""Converting model..."""       )

			# load original state dict
			lowerCamelCase       =      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__       )
			lowerCamelCase       =      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
			lowerCamelCase       =      """model."""
			for key in state_dict.copy().keys():
						if not key.startswith("""class_labels_classifier"""       ) and not key.startswith("""bbox_predictor"""       ):
									lowerCamelCase       =      state_dict.pop(snake_case__       )
									lowerCamelCase       =      val
    # create HuggingFace model and load state dict
			lowerCamelCase       =      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:
						lowerCamelCase       =      15
						lowerCamelCase       =      2
						lowerCamelCase       =      {0: """table""", 1: """table rotated"""}
						lowerCamelCase       =      idalabel
						lowerCamelCase       =      {v: k for k, v in idalabel.items()}
			else:
						lowerCamelCase       =      1_25
						lowerCamelCase       =      6
						lowerCamelCase       =      {
						    0: """table""",
						    1: """table column""",
						    2: """table row""",
						    3: """table column header""",
						    4: """table projected row header""",
						    5: """table spanning cell""",
						}
						lowerCamelCase       =      idalabel
						lowerCamelCase       =      {v: k for k, v in idalabel.items()}

			lowerCamelCase       =      DetrImageProcessor(
			    format="""coco_detection""" ,				max_size=8_00 if """detection""" in checkpoint_url else 10_00       )
			lowerCamelCase       =      TableTransformerForObjectDetection(snake_case__       )
			model.load_state_dict(snake_case__       )
			model.eval()

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

			lowerCamelCase       =      model(snake_case__       )

			if "detection" in checkpoint_url:
						lowerCamelCase       =      (1, 15, 3)
						lowerCamelCase       =      torch.tensor(
						    [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]       )
						lowerCamelCase       =      torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]       )

			else:
						lowerCamelCase       =      (1, 1_25, 7)
						lowerCamelCase       =      torch.tensor(
						    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]       )
						lowerCamelCase       =      torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]]       )

			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..."""       )
						lowerCamelCase       =      (
						    """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__":
					lowerCAmelCase							:    str  							=							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."""
					)
					lowerCAmelCase							:    int  							=							parser.parse_args()
					convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""




import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
					import tensorflow as tf

if is_torch_available():
					import torch


lowerCAmelCase							:    List[str]  							=							logging.get_logger(__name__)






class 					__magic_name__							:






				'''simple docstring'''





				def __init__( self	,				_a = None	,				_a = None	,				_a=None	,				_a=None				):





							"""simple docstring"""
							if not conversation_id:
										lowerCamelCase       =      uuid.uuida()
							if past_user_inputs is None:
										lowerCamelCase       =      []
							if generated_responses is None:
										lowerCamelCase       =      []

							lowerCamelCase       =      conversation_id
							lowerCamelCase       =      past_user_inputs
							lowerCamelCase       =      generated_responses
							lowerCamelCase       =      text





				def __eq__( self	,				_a				):





							"""simple docstring"""
							if not isinstance(_a	,				_a				):
										return False
							if self.uuid == other.uuid:
										return True
							return (
							    self.new_user_input == other.new_user_input
							    and self.past_user_inputs == other.past_user_inputs
							    and self.generated_responses == other.generated_responses
							)





				def      _lowerCAmelCase       ( self	,				_a	,				_a = False				):





							"""simple docstring"""
							if self.new_user_input:
										if overwrite:
													logger.warning(
													    f'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
													    f'with: "{text}".'				)
													lowerCamelCase       =      text
										else:
													logger.warning(
													    f'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
													    f'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'				)
							else:
										lowerCamelCase       =      text





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							if self.new_user_input:
										self.past_user_inputs.append(self.new_user_input				)
							lowerCamelCase       =      None





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							self.generated_responses.append(_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							for user_input, generated_response in zip(self.past_user_inputs	,				self.generated_responses				):
										yield True, user_input
										yield False, generated_response
							if self.new_user_input:
										yield True, self.new_user_input





				def __repr__( self				):





							"""simple docstring"""
							lowerCamelCase       =      f'Conversation id: {self.uuid} \n'
							for is_user, text in self.iter_texts():
										lowerCamelCase       =      """user""" if is_user else """bot"""
										output += f'{name} >> {text} \n'
							return output






@add_end_docstrings(
    UpperCAmelCase__		,      R"\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    "		,      )
class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''





				def __init__( self	,				*_a	,				**_a				):





							"""simple docstring"""
							super().__init__(*_a	,				**_a				)
							if self.tokenizer.pad_token_id is None:
										lowerCamelCase       =      self.tokenizer.eos_token





				def      _lowerCAmelCase       ( self	,				_a=None	,				_a=None	,				_a=None	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      {}
							lowerCamelCase       =      {}
							lowerCamelCase       =      {}

							if min_length_for_response is not None:
										lowerCamelCase       =      min_length_for_response
							if minimum_tokens is not None:
										lowerCamelCase       =      minimum_tokens

							if "max_length" in generate_kwargs:
										lowerCamelCase       =      generate_kwargs["""max_length"""]
										# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
							if clean_up_tokenization_spaces is not None:
										lowerCamelCase       =      clean_up_tokenization_spaces

							if generate_kwargs:
										forward_params.update(_a				)
							return preprocess_params, forward_params, postprocess_params





				def __call__( self	,				_a	,				_a=0	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      super().__call__(_a	,				num_workers=_a	,				**_a				)
							if isinstance(_a	,				_a				) and len(_a				) == 1:
										return outputs[0]
							return outputs





				def      _lowerCAmelCase       ( self	,				_a	,				_a=32				):





							"""simple docstring"""
							if not isinstance(_a	,				_a				):
										raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""				)
							if conversation.new_user_input is None:
										raise ValueError(
										    f'Conversation with UUID {type(conversation.uuid				)} does not contain new user input to process. '
										    """Add user inputs with the conversation's `add_user_input` method"""				)
							if hasattr(self.tokenizer	,				"""_build_conversation_input_ids"""				):
										lowerCamelCase       =      self.tokenizer._build_conversation_input_ids(_a				)
							else:
										# If the tokenizer cannot handle conversations, we default to only the old version
										lowerCamelCase       =      self._legacy_parse_and_tokenize(_a				)

							if self.framework == "pt":
										lowerCamelCase       =      torch.LongTensor([input_ids]				)
							elif self.framework == "tf":
										lowerCamelCase       =      tf.constant([input_ids]				)
							return {"input_ids": input_ids, "conversation": conversation}





				def      _lowerCAmelCase       ( self	,				_a	,				_a=10	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      generate_kwargs.get("""max_length"""	,				self.model.config.max_length				)

							lowerCamelCase       =      model_inputs["""input_ids"""].shape[1]
							if max_length - minimum_tokens < n:
										logger.warning(f'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'				)
										lowerCamelCase       =      max_length - minimum_tokens
										lowerCamelCase       =      model_inputs["""input_ids"""][:, -trim:]
										if "attention_mask" in model_inputs:
													lowerCamelCase       =      model_inputs["""attention_mask"""][:, -trim:]
							lowerCamelCase       =      model_inputs.pop("""conversation"""				)
							lowerCamelCase       =      max_length
							lowerCamelCase       =      self.model.generate(**_a	,				**_a				)
							if self.model.config.is_encoder_decoder:
										lowerCamelCase       =      1
							else:
										lowerCamelCase       =      n
							return {"output_ids": output_ids[:, start_position:], "conversation": conversation}





				def      _lowerCAmelCase       ( self	,				_a	,				_a=True				):





							"""simple docstring"""
							lowerCamelCase       =      model_outputs["""output_ids"""]
							lowerCamelCase       =      self.tokenizer.decode(
							    output_ids[0]	,				skip_special_tokens=_a	,				clean_up_tokenization_spaces=_a	,				)
							lowerCamelCase       =      model_outputs["""conversation"""]
							conversation.mark_processed()
							conversation.append_response(_a				)
							return conversation





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.tokenizer.eos_token_id
							lowerCamelCase       =      []
							for is_user, text in conversation.iter_texts():
										if eos_token_id is not None:
													input_ids.extend(self.tokenizer.encode(_a	,				add_special_tokens=_a				) + [eos_token_id]				)
										else:
													input_ids.extend(self.tokenizer.encode(_a	,				add_special_tokens=_a				)				)

							if len(_a				) > self.tokenizer.model_max_length:
										lowerCamelCase       =      input_ids[-self.tokenizer.model_max_length :]
							return input_ids



