import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_=1	):



			"""simple docstring"""





			if n_shave_prefix_segments >= 0:
						return ".".join(path.split("."	)[n_shave_prefix_segments:]	)
			else:
						return ".".join(path.split("."	)[:n_shave_prefix_segments]	)






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_=0	):



			"""simple docstring"""





			_snake_case														=   []
			for old_item in old_list:
						_snake_case														=   old_item
						_snake_case														=   new_item.replace("block."				,						"resnets."	)
						_snake_case														=   new_item.replace("conv_shorcut"				,						"conv1"	)
						_snake_case														=   new_item.replace("in_shortcut"				,						"conv_shortcut"	)
						_snake_case														=   new_item.replace("temb_proj"				,						"time_emb_proj"	)

						_snake_case														=   shave_segments(lowerCamelCase_				,						n_shave_prefix_segments=lowerCamelCase_	)

						mapping.append({"old": old_item, "new": new_item}	)

			return mapping






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_=0				,						lowerCamelCase_=False	):



			"""simple docstring"""





			_snake_case														=   []
			for old_item in old_list:
						_snake_case														=   old_item

						# In `model.mid`, the layer is called `attn`.
						if not in_mid:
									_snake_case														=   new_item.replace("attn"				,						"attentions"	)
						_snake_case														=   new_item.replace(".k."				,						".key."	)
						_snake_case														=   new_item.replace(".v."				,						".value."	)
						_snake_case														=   new_item.replace(".q."				,						".query."	)

						_snake_case														=   new_item.replace("proj_out"				,						"proj_attn"	)
						_snake_case														=   new_item.replace("norm"				,						"group_norm"	)

						_snake_case														=   shave_segments(lowerCamelCase_				,						n_shave_prefix_segments=lowerCamelCase_	)
						mapping.append({"old": old_item, "new": new_item}	)

			return mapping






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_=None				,						lowerCamelCase_=None				,						lowerCamelCase_=None	):



			"""simple docstring"""





			assert isinstance(lowerCamelCase_				,						lowerCamelCase_	), "Paths should be a list of dicts containing 'old' and 'new' keys."

			if attention_paths_to_split is not None:
						if config is None:
									raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'."	)

						for path, path_map in attention_paths_to_split.items():
									_snake_case														=   old_checkpoint[path]
									_snake_case														=   old_tensor.shape[0] // 3

									_snake_case														=   (-1, channels) if len(old_tensor.shape	) == 3 else (-1)

									_snake_case														=   old_tensor.shape[0] // config.get("num_head_channels"				,						1	) // 3

									_snake_case														=   old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]	)
									_snake_case							,			_snake_case							,			_snake_case														=   old_tensor.split(channels // num_heads				,						dim=1	)

									_snake_case														=   query.reshape(lowerCamelCase_	).squeeze()
									_snake_case														=   key.reshape(lowerCamelCase_	).squeeze()
									_snake_case														=   value.reshape(lowerCamelCase_	).squeeze()

			for path in paths:
						_snake_case														=   path["new"]

						if attention_paths_to_split is not None and new_path in attention_paths_to_split:
									continue

						_snake_case														=   new_path.replace("down."				,						"down_blocks."	)
						_snake_case														=   new_path.replace("up."				,						"up_blocks."	)

						if additional_replacements is not None:
									for replacement in additional_replacements:
												_snake_case														=   new_path.replace(replacement["old"]				,						replacement["new"]	)

						if "attentions" in new_path:
									_snake_case														=   old_checkpoint[path["old"]].squeeze()
						else:
									_snake_case														=   old_checkpoint[path["old"]]






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   {}

			_snake_case														=   checkpoint["temb.dense.0.weight"]
			_snake_case														=   checkpoint["temb.dense.0.bias"]
			_snake_case														=   checkpoint["temb.dense.1.weight"]
			_snake_case														=   checkpoint["temb.dense.1.bias"]

			_snake_case														=   checkpoint["norm_out.weight"]
			_snake_case														=   checkpoint["norm_out.bias"]

			_snake_case														=   checkpoint["conv_in.weight"]
			_snake_case														=   checkpoint["conv_in.bias"]
			_snake_case														=   checkpoint["conv_out.weight"]
			_snake_case														=   checkpoint["conv_out.bias"]

			_snake_case														=   len({".".join(layer.split("."	)[:2]	) for layer in checkpoint if "down" in layer}	)
			_snake_case														=   {
			    layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(lowerCamelCase_	)
			}

			_snake_case														=   len({".".join(layer.split("."	)[:2]	) for layer in checkpoint if "up" in layer}	)
			_snake_case														=   {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(lowerCamelCase_	)}

			for i in range(lowerCamelCase_	):
						_snake_case														=   (i - 1) // (config["layers_per_block"] + 1)

						if any("downsample" in layer for layer in down_blocks[i]	):
									_snake_case														=   checkpoint[
									    f'down.{i}.downsample.op.weight'
									]
									_snake_case														=   checkpoint[f'down.{i}.downsample.op.bias']
						#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
						#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

						if any("block" in layer for layer in down_blocks[i]	):
									_snake_case														=   len(
									    {".".join(shave_segments(lowerCamelCase_				,						2	).split("."	)[:2]	) for layer in down_blocks[i] if "block" in layer}	)
									_snake_case														=   {
									    layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
									    for layer_id in range(lowerCamelCase_	)
									}

									if num_blocks > 0:
												for j in range(config["layers_per_block"]	):
															_snake_case														=   renew_resnet_paths(blocks[j]	)
															assign_to_checkpoint(lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	)

						if any("attn" in layer for layer in down_blocks[i]	):
									_snake_case														=   len(
									    {".".join(shave_segments(lowerCamelCase_				,						2	).split("."	)[:2]	) for layer in down_blocks[i] if "attn" in layer}	)
									_snake_case														=   {
									    layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
									    for layer_id in range(lowerCamelCase_	)
									}

									if num_attn > 0:
												for j in range(config["layers_per_block"]	):
															_snake_case														=   renew_attention_paths(attns[j]	)
															assign_to_checkpoint(lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						config=lowerCamelCase_	)

			_snake_case														=   [key for key in checkpoint if "mid.block_1" in key]
			_snake_case														=   [key for key in checkpoint if "mid.block_2" in key]
			_snake_case														=   [key for key in checkpoint if "mid.attn_1" in key]

			# Mid new 2
			_snake_case														=   renew_resnet_paths(lowerCamelCase_	)
			assign_to_checkpoint(
			    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]				,						)

			_snake_case														=   renew_resnet_paths(lowerCamelCase_	)
			assign_to_checkpoint(
			    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]				,						)

			_snake_case														=   renew_attention_paths(lowerCamelCase_				,						in_mid=lowerCamelCase_	)
			assign_to_checkpoint(
			    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]				,						)

			for i in range(lowerCamelCase_	):
						_snake_case														=   num_up_blocks - 1 - i

						if any("upsample" in layer for layer in up_blocks[i]	):
									_snake_case														=   checkpoint[
									    f'up.{i}.upsample.conv.weight'
									]
									_snake_case														=   checkpoint[f'up.{i}.upsample.conv.bias']

						if any("block" in layer for layer in up_blocks[i]	):
									_snake_case														=   len(
									    {".".join(shave_segments(lowerCamelCase_				,						2	).split("."	)[:2]	) for layer in up_blocks[i] if "block" in layer}	)
									_snake_case														=   {
									    layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(lowerCamelCase_	)
									}

									if num_blocks > 0:
												for j in range(config["layers_per_block"] + 1	):
															_snake_case														=   {"old": f'up_blocks.{i}', "new": f'up_blocks.{block_id}'}
															_snake_case														=   renew_resnet_paths(blocks[j]	)
															assign_to_checkpoint(lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						additional_replacements=[replace_indices]	)

						if any("attn" in layer for layer in up_blocks[i]	):
									_snake_case														=   len(
									    {".".join(shave_segments(lowerCamelCase_				,						2	).split("."	)[:2]	) for layer in up_blocks[i] if "attn" in layer}	)
									_snake_case														=   {
									    layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(lowerCamelCase_	)
									}

									if num_attn > 0:
												for j in range(config["layers_per_block"] + 1	):
															_snake_case														=   {"old": f'up_blocks.{i}', "new": f'up_blocks.{block_id}'}
															_snake_case														=   renew_attention_paths(attns[j]	)
															assign_to_checkpoint(lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						additional_replacements=[replace_indices]	)

			_snake_case														=   {k.replace("mid_new_2"				,						"mid_block"	): v for k, v in new_checkpoint.items()}
			return new_checkpoint






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   {}

			_snake_case														=   checkpoint["encoder.norm_out.weight"]
			_snake_case														=   checkpoint["encoder.norm_out.bias"]

			_snake_case														=   checkpoint["encoder.conv_in.weight"]
			_snake_case														=   checkpoint["encoder.conv_in.bias"]
			_snake_case														=   checkpoint["encoder.conv_out.weight"]
			_snake_case														=   checkpoint["encoder.conv_out.bias"]

			_snake_case														=   checkpoint["decoder.norm_out.weight"]
			_snake_case														=   checkpoint["decoder.norm_out.bias"]

			_snake_case														=   checkpoint["decoder.conv_in.weight"]
			_snake_case														=   checkpoint["decoder.conv_in.bias"]
			_snake_case														=   checkpoint["decoder.conv_out.weight"]
			_snake_case														=   checkpoint["decoder.conv_out.bias"]

			_snake_case														=   len({".".join(layer.split("."	)[:3]	) for layer in checkpoint if "down" in layer}	)
			_snake_case														=   {
			    layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(lowerCamelCase_	)
			}

			_snake_case														=   len({".".join(layer.split("."	)[:3]	) for layer in checkpoint if "up" in layer}	)
			_snake_case														=   {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(lowerCamelCase_	)}

			for i in range(lowerCamelCase_	):
						_snake_case														=   (i - 1) // (config["layers_per_block"] + 1)

						if any("downsample" in layer for layer in down_blocks[i]	):
									_snake_case														=   checkpoint[
									    f'encoder.down.{i}.downsample.conv.weight'
									]
									_snake_case														=   checkpoint[
									    f'encoder.down.{i}.downsample.conv.bias'
									]

						if any("block" in layer for layer in down_blocks[i]	):
									_snake_case														=   len(
									    {".".join(shave_segments(lowerCamelCase_				,						3	).split("."	)[:3]	) for layer in down_blocks[i] if "block" in layer}	)
									_snake_case														=   {
									    layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
									    for layer_id in range(lowerCamelCase_	)
									}

									if num_blocks > 0:
												for j in range(config["layers_per_block"]	):
															_snake_case														=   renew_resnet_paths(blocks[j]	)
															assign_to_checkpoint(lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	)

						if any("attn" in layer for layer in down_blocks[i]	):
									_snake_case														=   len(
									    {".".join(shave_segments(lowerCamelCase_				,						3	).split("."	)[:3]	) for layer in down_blocks[i] if "attn" in layer}	)
									_snake_case														=   {
									    layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
									    for layer_id in range(lowerCamelCase_	)
									}

									if num_attn > 0:
												for j in range(config["layers_per_block"]	):
															_snake_case														=   renew_attention_paths(attns[j]	)
															assign_to_checkpoint(lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						config=lowerCamelCase_	)

			_snake_case														=   [key for key in checkpoint if "mid.block_1" in key]
			_snake_case														=   [key for key in checkpoint if "mid.block_2" in key]
			_snake_case														=   [key for key in checkpoint if "mid.attn_1" in key]

			# Mid new 2
			_snake_case														=   renew_resnet_paths(lowerCamelCase_	)
			assign_to_checkpoint(
			    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]				,						)

			_snake_case														=   renew_resnet_paths(lowerCamelCase_	)
			assign_to_checkpoint(
			    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]				,						)

			_snake_case														=   renew_attention_paths(lowerCamelCase_				,						in_mid=lowerCamelCase_	)
			assign_to_checkpoint(
			    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]				,						)

			for i in range(lowerCamelCase_	):
						_snake_case														=   num_up_blocks - 1 - i

						if any("upsample" in layer for layer in up_blocks[i]	):
									_snake_case														=   checkpoint[
									    f'decoder.up.{i}.upsample.conv.weight'
									]
									_snake_case														=   checkpoint[
									    f'decoder.up.{i}.upsample.conv.bias'
									]

						if any("block" in layer for layer in up_blocks[i]	):
									_snake_case														=   len(
									    {".".join(shave_segments(lowerCamelCase_				,						3	).split("."	)[:3]	) for layer in up_blocks[i] if "block" in layer}	)
									_snake_case														=   {
									    layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(lowerCamelCase_	)
									}

									if num_blocks > 0:
												for j in range(config["layers_per_block"] + 1	):
															_snake_case														=   {"old": f'up_blocks.{i}', "new": f'up_blocks.{block_id}'}
															_snake_case														=   renew_resnet_paths(blocks[j]	)
															assign_to_checkpoint(lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						additional_replacements=[replace_indices]	)

						if any("attn" in layer for layer in up_blocks[i]	):
									_snake_case														=   len(
									    {".".join(shave_segments(lowerCamelCase_				,						3	).split("."	)[:3]	) for layer in up_blocks[i] if "attn" in layer}	)
									_snake_case														=   {
									    layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(lowerCamelCase_	)
									}

									if num_attn > 0:
												for j in range(config["layers_per_block"] + 1	):
															_snake_case														=   {"old": f'up_blocks.{i}', "new": f'up_blocks.{block_id}'}
															_snake_case														=   renew_attention_paths(attns[j]	)
															assign_to_checkpoint(lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						additional_replacements=[replace_indices]	)

			_snake_case														=   {k.replace("mid_new_2"				,						"mid_block"	): v for k, v in new_checkpoint.items()}
			_snake_case														=   checkpoint["quant_conv.weight"]
			_snake_case														=   checkpoint["quant_conv.bias"]
			if "quantize.embedding.weight" in checkpoint:
						_snake_case														=   checkpoint["quantize.embedding.weight"]
			_snake_case														=   checkpoint["post_quant_conv.weight"]
			_snake_case														=   checkpoint["post_quant_conv.bias"]

			return new_checkpoint


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE:    List[Any]     						=							argparse.ArgumentParser()

			parser.add_argument(
			    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
			)

			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help='''The config json file corresponding to the architecture.''',
			)

			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

			_SCREAMING_SNAKE_CASE:    List[Any]     						=							parser.parse_args()
			_SCREAMING_SNAKE_CASE:    List[str]     						=							torch.load(args.checkpoint_path)

			with open(args.config_file) as f:
						_SCREAMING_SNAKE_CASE:    str     						=							json.loads(f.read())

			# unet case
			_SCREAMING_SNAKE_CASE:    Tuple     						=							{key.split('''.''')[0] for key in checkpoint.keys()}
			if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
						_SCREAMING_SNAKE_CASE:    Any     						=							convert_vq_autoenc_checkpoint(checkpoint, config)
			else:
						_SCREAMING_SNAKE_CASE:    List[Any]     						=							convert_ddpm_checkpoint(checkpoint, config)

			if "ddpm" in config:
						del config["ddpm"]

			if config["_class_name"] == "VQModel":
						_SCREAMING_SNAKE_CASE:    List[Any]     						=							VQModel(**config)
						model.load_state_dict(converted_checkpoint)
						model.save_pretrained(args.dump_path)
			elif config["_class_name"] == "AutoencoderKL":
						_SCREAMING_SNAKE_CASE:    Optional[int]     						=							AutoencoderKL(**config)
						model.load_state_dict(converted_checkpoint)
						model.save_pretrained(args.dump_path)
			else:
						_SCREAMING_SNAKE_CASE:    Tuple     						=							UNetaDModel(**config)
						model.load_state_dict(converted_checkpoint)

						_SCREAMING_SNAKE_CASE:    List[str]     						=							DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

						_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							DDPMPipeline(unet=model, scheduler=scheduler)
						pipe.save_pretrained(args.dump_path)


import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
			import torch

			from ..models.auto.modeling_auto import (
			    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
			    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
			)

if is_tf_available() and is_tensorflow_probability_available():
			import tensorflow as tf
			import tensorflow_probability as tfp

			from ..models.auto.modeling_tf_auto import (
			    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
			    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
			)

class 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		def __call__(      self     :					Union[str, Any]	,       __SCREAMING_SNAKE_CASE     :					int=None	,       __SCREAMING_SNAKE_CASE     :					str=None	,       **__SCREAMING_SNAKE_CASE     :					Union[str, Any] ):






					"""simple docstring"""
					# Returns tqa_pipeline_inputs of shape:
					# [
					#   {"table": pd.DataFrame, "query": List[str]},
					#   ...,
					#   {"table": pd.DataFrame, "query" : List[str]}
					# ]
					requires_backends(self	,       "pandas" )
					import pandas as pd

					if table is None:
								raise ValueError("Keyword argument `table` cannot be None." )
					elif query is None:
								if isinstance(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) and table.get("query" ) is not None and table.get("table" ) is not None:
											_snake_case														=   [table]
								elif isinstance(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) and len(__SCREAMING_SNAKE_CASE ) > 0:
											if not all(isinstance(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) for d in table ):
														raise ValueError(
														    F'Keyword argument `table` should be a list of dict, but is {(type(__SCREAMING_SNAKE_CASE ) for d in table)}' )

											if table[0].get("query" ) is not None and table[0].get("table" ) is not None:
														_snake_case														=   table
											else:
														raise ValueError(
														    "If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"
														    F' and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys.' )
								elif Dataset is not None and isinstance(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) or isinstance(__SCREAMING_SNAKE_CASE	,       types.GeneratorType ):
											return table
								else:
											raise ValueError(
											    "Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
											    F'is {type(__SCREAMING_SNAKE_CASE )})' )
					else:
								_snake_case														=   [{"table": table, "query": query}]

					for tqa_pipeline_input in tqa_pipeline_inputs:
								if not isinstance(tqa_pipeline_input["table"]	,       pd.DataFrame ):
											if tqa_pipeline_input["table"] is None:
														raise ValueError("Table cannot be None." )

											_snake_case														=   pd.DataFrame(tqa_pipeline_input["table"] )

					return tqa_pipeline_inputs

@add_end_docstrings(_lowercase				)
class 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		A_    			=   '''table,query'''



		def __init__(      self     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					Tuple=TableQuestionAnsweringArgumentHandler()	,       *__SCREAMING_SNAKE_CASE     :					Optional[int]	,       **__SCREAMING_SNAKE_CASE     :					List[Any] ):






					"""simple docstring"""
					super().__init__(*__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )
					_snake_case														=   args_parser

					self.check_model_type(
					    dict(
					        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
					        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items() )
					    if self.framework == "tf"
					    else dict(
					        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items() ) )

					_snake_case														=   bool(getattr(self.model.config	,       "aggregation_labels"	,       __SCREAMING_SNAKE_CASE ) ) and bool(
					    getattr(self.model.config	,       "num_aggregation_labels"	,       __SCREAMING_SNAKE_CASE ) )
					_snake_case														=   "tapas" if hasattr(self.model.config	,       "aggregation_labels" ) else None



		def 					_a					(      self     :					List[Any]	,       **__SCREAMING_SNAKE_CASE     :					str ):






					"""simple docstring"""
					return self.model(**__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Tuple	,       **__SCREAMING_SNAKE_CASE     :					List[Any] ):






					"""simple docstring"""
					if self.framework == "pt":
								_snake_case														=   []
								_snake_case														=   []
								_snake_case														=   None
								_snake_case														=   inputs["input_ids"].shape[0]

								_snake_case														=   inputs["input_ids"].to(self.device )
								_snake_case														=   inputs["attention_mask"].to(self.device )
								_snake_case														=   inputs["token_type_ids"].to(self.device )
								_snake_case														=   None

								for index in range(__SCREAMING_SNAKE_CASE ):
											# If sequences have already been processed, the token type IDs will be created according to the previous
											# answer.
											if prev_answers is not None:
														_snake_case														=   token_type_ids_example[:, 3]  # shape (seq_len,)
														_snake_case														=   np.zeros_like(prev_labels_example.cpu().numpy() )  # shape (seq_len,)

														_snake_case														=   token_type_ids[index]  # shape (seq_len, 7)
														for i in range(model_labels.shape[0] ):
																	_snake_case														=   token_type_ids_example[:, 0].tolist()[i]
																	_snake_case														=   token_type_ids_example[:, 1].tolist()[i] - 1
																	_snake_case														=   token_type_ids_example[:, 2].tolist()[i] - 1

																	if row_id >= 0 and col_id >= 0 and segment_id == 1:
																				_snake_case														=   int(prev_answers[(col_id, row_id)] )

														_snake_case														=   torch.from_numpy(__SCREAMING_SNAKE_CASE ).type(torch.long ).to(self.device )

											_snake_case														=   input_ids[index]
											_snake_case														=   attention_mask[index]  # shape (seq_len,)
											_snake_case														=   token_type_ids[index]  # shape (seq_len, 7)
											_snake_case														=   self.model(
											    input_ids=input_ids_example.unsqueeze(0 )	,       attention_mask=attention_mask_example.unsqueeze(0 )	,       token_type_ids=token_type_ids_example.unsqueeze(0 )	,       )
											_snake_case														=   outputs.logits

											if self.aggregate:
														all_aggregations.append(outputs.logits_aggregation )

											all_logits.append(__SCREAMING_SNAKE_CASE )

											_snake_case														=   torch.distributions.Bernoulli(logits=__SCREAMING_SNAKE_CASE )
											_snake_case														=   dist_per_token.probs * attention_mask_example.type(torch.floataa ).to(
											    dist_per_token.probs.device )

											_snake_case														=   collections.defaultdict(__SCREAMING_SNAKE_CASE )
											for i, p in enumerate(probabilities.squeeze().tolist() ):
														_snake_case														=   token_type_ids_example[:, 0].tolist()[i]
														_snake_case														=   token_type_ids_example[:, 1].tolist()[i] - 1
														_snake_case														=   token_type_ids_example[:, 2].tolist()[i] - 1
														if col >= 0 and row >= 0 and segment_id == 1:
																	coords_to_probs[(col, row)].append(__SCREAMING_SNAKE_CASE )

											_snake_case														=   {key: np.array(coords_to_probs[key] ).mean() > 0.5 for key in coords_to_probs}

								_snake_case														=   torch.cat(tuple(__SCREAMING_SNAKE_CASE )	,       0 )

								return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(__SCREAMING_SNAKE_CASE )	,       0 ))
					else:
								_snake_case														=   []
								_snake_case														=   []
								_snake_case														=   None
								_snake_case														=   inputs["input_ids"].shape[0]

								_snake_case														=   inputs["input_ids"]
								_snake_case														=   inputs["attention_mask"]
								_snake_case														=   inputs["token_type_ids"].numpy()
								_snake_case														=   None

								for index in range(__SCREAMING_SNAKE_CASE ):
											# If sequences have already been processed, the token type IDs will be created according to the previous
											# answer.
											if prev_answers is not None:
														_snake_case														=   token_type_ids_example[:, 3]  # shape (seq_len,)
														_snake_case														=   np.zeros_like(__SCREAMING_SNAKE_CASE	,       dtype=np.intaa )  # shape (seq_len,)

														_snake_case														=   token_type_ids[index]  # shape (seq_len, 7)
														for i in range(model_labels.shape[0] ):
																	_snake_case														=   token_type_ids_example[:, 0].tolist()[i]
																	_snake_case														=   token_type_ids_example[:, 1].tolist()[i] - 1
																	_snake_case														=   token_type_ids_example[:, 2].tolist()[i] - 1

																	if row_id >= 0 and col_id >= 0 and segment_id == 1:
																				_snake_case														=   int(prev_answers[(col_id, row_id)] )

														_snake_case														=   model_labels

											_snake_case														=   input_ids[index]
											_snake_case														=   attention_mask[index]  # shape (seq_len,)
											_snake_case														=   token_type_ids[index]  # shape (seq_len, 7)
											_snake_case														=   self.model(
											    input_ids=np.expand_dims(__SCREAMING_SNAKE_CASE	,       axis=0 )	,       attention_mask=np.expand_dims(__SCREAMING_SNAKE_CASE	,       axis=0 )	,       token_type_ids=np.expand_dims(__SCREAMING_SNAKE_CASE	,       axis=0 )	,       )
											_snake_case														=   outputs.logits

											if self.aggregate:
														all_aggregations.append(outputs.logits_aggregation )

											all_logits.append(__SCREAMING_SNAKE_CASE )

											_snake_case														=   tfp.distributions.Bernoulli(logits=__SCREAMING_SNAKE_CASE )
											_snake_case														=   dist_per_token.probs_parameter() * tf.cast(__SCREAMING_SNAKE_CASE	,       tf.floataa )

											_snake_case														=   collections.defaultdict(__SCREAMING_SNAKE_CASE )
											_snake_case														=   token_type_ids_example
											for i, p in enumerate(tf.squeeze(__SCREAMING_SNAKE_CASE ).numpy().tolist() ):
														_snake_case														=   token_type_ids_example[:, 0].tolist()[i]
														_snake_case														=   token_type_ids_example[:, 1].tolist()[i] - 1
														_snake_case														=   token_type_ids_example[:, 2].tolist()[i] - 1
														if col >= 0 and row >= 0 and segment_id == 1:
																	coords_to_probs[(col, row)].append(__SCREAMING_SNAKE_CASE )

											_snake_case														=   {key: np.array(coords_to_probs[key] ).mean() > 0.5 for key in coords_to_probs}

								_snake_case														=   tf.concat(tuple(__SCREAMING_SNAKE_CASE )	,       0 )

								return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(__SCREAMING_SNAKE_CASE )	,       0 ))



		def __call__(      self     :					str	,       *__SCREAMING_SNAKE_CASE     :					Optional[int]	,       **__SCREAMING_SNAKE_CASE     :					List[Any] ):






					"""simple docstring"""
					_snake_case														=   self._args_parser(*__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )

					_snake_case														=   super().__call__(__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )
					if len(__SCREAMING_SNAKE_CASE ) == 1:
								return results[0]
					return results



		def 					_a					(      self     :					Optional[int]	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=None	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=None	,       __SCREAMING_SNAKE_CASE     :					str=None	,       **__SCREAMING_SNAKE_CASE     :					Optional[int] ):






					"""simple docstring"""
					_snake_case														=   {}
					if padding is not None:
								_snake_case														=   padding
					if truncation is not None:
								_snake_case														=   truncation

					_snake_case														=   {}
					if sequential is not None:
								_snake_case														=   sequential
					return preprocess_params, forward_params, {}



		def 					_a					(      self     :					Any	,       __SCREAMING_SNAKE_CASE     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]=None	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=True	,       __SCREAMING_SNAKE_CASE     :					str=None ):






					"""simple docstring"""
					if truncation is None:
								if self.type == "tapas":
											_snake_case														=   "drop_rows_to_fit"
								else:
											_snake_case														=   "do_not_truncate"

					_snake_case							,			_snake_case														=   pipeline_input["table"], pipeline_input["query"]
					if table.empty:
								raise ValueError("table is empty" )
					if query is None or query == "":
								raise ValueError("query is empty" )
					_snake_case														=   self.tokenizer(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       return_tensors=self.framework	,       truncation=__SCREAMING_SNAKE_CASE	,       padding=__SCREAMING_SNAKE_CASE )
					_snake_case														=   table
					return inputs



		def 					_a					(      self     :					Optional[int]	,       __SCREAMING_SNAKE_CASE     :					str	,       __SCREAMING_SNAKE_CASE     :					List[Any]=False ):






					"""simple docstring"""
					_snake_case														=   model_inputs.pop("table" )

					if self.type == "tapas":
								if sequential:
											_snake_case														=   self.sequential_inference(**__SCREAMING_SNAKE_CASE )
								else:
											_snake_case														=   self.batch_inference(**__SCREAMING_SNAKE_CASE )
					else:
								_snake_case														=   self.model.generate(**__SCREAMING_SNAKE_CASE )
					_snake_case														=   {"model_inputs": model_inputs, "table": table, "outputs": outputs}
					return model_outputs







		def 					_a					(      self     :					Dict	,       __SCREAMING_SNAKE_CASE     :					Optional[int] ):






					"""simple docstring"""
					_snake_case														=   model_outputs["model_inputs"]
					_snake_case														=   model_outputs["table"]
					_snake_case														=   model_outputs["outputs"]
					if self.type == "tapas":
								if self.aggregate:
											_snake_case							,			_snake_case														=   outputs[:2]
											_snake_case														=   self.tokenizer.convert_logits_to_predictions(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
											_snake_case							,			_snake_case														=   predictions
											_snake_case														=   {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(__SCREAMING_SNAKE_CASE )}

											_snake_case														=   self.model.config.no_aggregation_label_index
											_snake_case														=   {
											    i: aggregators[i] + " > " for i, pred in enumerate(__SCREAMING_SNAKE_CASE ) if pred != no_agg_label_index
											}
								else:
											_snake_case														=   outputs[0]
											_snake_case														=   self.tokenizer.convert_logits_to_predictions(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
											_snake_case														=   predictions[0]
											_snake_case														=   {}
											_snake_case														=   {}
								_snake_case														=   []
								for index, coordinates in enumerate(__SCREAMING_SNAKE_CASE ):
											_snake_case														=   [table.iat[coordinate] for coordinate in coordinates]
											_snake_case														=   aggregators.get(__SCREAMING_SNAKE_CASE	,       "" )
											_snake_case														=   aggregators_prefix.get(__SCREAMING_SNAKE_CASE	,       "" )
											_snake_case														=   {
											    "answer": aggregator_prefix + ", ".join(__SCREAMING_SNAKE_CASE ),
											    "coordinates": coordinates,
											    "cells": [table.iat[coordinate] for coordinate in coordinates],
											}
											if aggregator:
														_snake_case														=   aggregator

											answers.append(__SCREAMING_SNAKE_CASE )
								if len(__SCREAMING_SNAKE_CASE ) == 0:
											raise PipelineException("Empty answer" )
					else:
								_snake_case														=   [{"answer": answer} for answer in self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	,       skip_special_tokens=__SCREAMING_SNAKE_CASE )]

					return answers if len(__SCREAMING_SNAKE_CASE ) > 1 else answers[0]

