

'''simple docstring'''

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 			A_					(							_snake_case				):






     '''simple docstring'''



     def __call__(						self					:	List[str]	,     lowercase_					:	Optional[int]=None	,     lowercase_					:	Optional[Any]=None	,     **lowercase_					:	Union[str, Any]			)		->      Tuple:
      # 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(lowercase_	,     lowercase_			) and table.get('query'			) is not None and table.get('table'			) is not None:
        UpperCAmelCase			:      Optional[int]		   =					[table]
       elif isinstance(lowercase_	,     lowercase_			) and len(lowercase_			) > 0:
        if not all(isinstance(lowercase_	,     lowercase_			) for d in table			):
         raise ValueError(
             f"""Keyword argument `table` should be a list of dict, but is {(type(lowercase_			) for d in table)}"""			)

        if table[0].get('query'			) is not None and table[0].get('table'			) is not None:
         UpperCAmelCase			:      Union[str, Any]		   =					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(lowercase_	,     lowercase_			) or isinstance(lowercase_	,     types.GeneratorType			):
        return table
       else:
        raise ValueError(
            'Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '
            f"""is {type(lowercase_			)})"""			)
      else:
       UpperCAmelCase			:      Union[str, Any]		   =					[{'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.'			)

        UpperCAmelCase			:      List[Any]		   =					pd.DataFrame(tqa_pipeline_input['table']			)

      return tqa_pipeline_inputs




@add_end_docstrings(_snake_case				)
class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      int    						=    """table,query"""


     def __init__(						self					:	int	,     lowercase_					:	str=TableQuestionAnsweringArgumentHandler()	,     *lowercase_					:	Optional[Any]	,     **lowercase_					:	Tuple			)		->      List[str]:
      super().__init__(*lowercase_	,     **lowercase_			)
      UpperCAmelCase			:      int		   =					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()			)			)

      UpperCAmelCase			:      List[str]		   =					bool(getattr(self.model.config	,     'aggregation_labels'	,     lowercase_			)			) and bool(
          getattr(self.model.config	,     'num_aggregation_labels'	,     lowercase_			)			)
      UpperCAmelCase			:      Tuple		   =					'tapas' if hasattr(self.model.config	,     'aggregation_labels'			) else None


     def   UpperCAmelCase_			(						self					:	Dict	,     **lowercase_					:	List[str]			)		->      Dict:
      return self.model(**lowercase_			)


     def   UpperCAmelCase_			(						self					:	Any	,     **lowercase_					:	Dict			)		->      List[str]:
      if self.framework == "pt":
       UpperCAmelCase			:      Dict		   =					[]
       UpperCAmelCase			:      Any		   =					[]
       UpperCAmelCase			:      List[Any]		   =					None
       UpperCAmelCase			:      int		   =					inputs['input_ids'].shape[0]

       UpperCAmelCase			:      Union[str, Any]		   =					inputs['input_ids'].to(self.device			)
       UpperCAmelCase			:      Optional[Any]		   =					inputs['attention_mask'].to(self.device			)
       UpperCAmelCase			:      int		   =					inputs['token_type_ids'].to(self.device			)
       UpperCAmelCase			:      Any		   =					None

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

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

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

         UpperCAmelCase			:      int		   =					torch.from_numpy(lowercase_			).type(torch.long			).to(self.device			)

        UpperCAmelCase			:      int		   =					input_ids[index]
        UpperCAmelCase			:      List[Any]		   =					attention_mask[index]  # shape (seq_len,)
        UpperCAmelCase			:      Any		   =					token_type_ids[index]  # shape (seq_len, 7)
        UpperCAmelCase			:      Union[str, Any]		   =					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			)	,     )
        UpperCAmelCase			:      Any		   =					outputs.logits

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

        all_logits.append(lowercase_			)

        UpperCAmelCase			:      str		   =					torch.distributions.Bernoulli(logits=lowercase_			)
        UpperCAmelCase			:      Optional[Any]		   =					dist_per_token.probs * attention_mask_example.type(torch.floataa			).to(
            dist_per_token.probs.device			)

        UpperCAmelCase			:      Optional[int]		   =					collections.defaultdict(lowercase_			)
        for i, p in enumerate(probabilities.squeeze().tolist()			):
         UpperCAmelCase			:      Tuple		   =					token_type_ids_example[:, 0].tolist()[i]
         UpperCAmelCase			:      int		   =					token_type_ids_example[:, 1].tolist()[i] - 1
         UpperCAmelCase			:      Union[str, Any]		   =					token_type_ids_example[:, 2].tolist()[i] - 1
         if col >= 0 and row >= 0 and segment_id == 1:
          coords_to_probs[(col, row)].append(lowercase_			)

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

       UpperCAmelCase			:      int		   =					torch.cat(tuple(lowercase_			)	,     0			)

       return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(lowercase_			)	,     0			))
      else:
       UpperCAmelCase			:      str		   =					[]
       UpperCAmelCase			:      List[Any]		   =					[]
       UpperCAmelCase			:      Union[str, Any]		   =					None
       UpperCAmelCase			:      Optional[int]		   =					inputs['input_ids'].shape[0]

       UpperCAmelCase			:      Optional[int]		   =					inputs['input_ids']
       UpperCAmelCase			:      Tuple		   =					inputs['attention_mask']
       UpperCAmelCase			:      List[str]		   =					inputs['token_type_ids'].numpy()
       UpperCAmelCase			:      Optional[Any]		   =					None

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

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

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

         UpperCAmelCase			:      Union[str, Any]		   =					model_labels

        UpperCAmelCase			:      List[str]		   =					input_ids[index]
        UpperCAmelCase			:      Optional[Any]		   =					attention_mask[index]  # shape (seq_len,)
        UpperCAmelCase			:      Optional[int]		   =					token_type_ids[index]  # shape (seq_len, 7)
        UpperCAmelCase			:      Any		   =					self.model(
            input_ids=np.expand_dims(lowercase_	,     axis=0			)	,     attention_mask=np.expand_dims(lowercase_	,     axis=0			)	,     token_type_ids=np.expand_dims(lowercase_	,     axis=0			)	,     )
        UpperCAmelCase			:      List[Any]		   =					outputs.logits

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

        all_logits.append(lowercase_			)

        UpperCAmelCase			:      Tuple		   =					tfp.distributions.Bernoulli(logits=lowercase_			)
        UpperCAmelCase			:      Any		   =					dist_per_token.probs_parameter() * tf.cast(lowercase_	,     tf.floataa			)

        UpperCAmelCase			:      Optional[Any]		   =					collections.defaultdict(lowercase_			)
        UpperCAmelCase			:      Union[str, Any]		   =					token_type_ids_example
        for i, p in enumerate(tf.squeeze(lowercase_			).numpy().tolist()			):
         UpperCAmelCase			:      Any		   =					token_type_ids_example[:, 0].tolist()[i]
         UpperCAmelCase			:      List[str]		   =					token_type_ids_example[:, 1].tolist()[i] - 1
         UpperCAmelCase			:      Optional[Any]		   =					token_type_ids_example[:, 2].tolist()[i] - 1
         if col >= 0 and row >= 0 and segment_id == 1:
          coords_to_probs[(col, row)].append(lowercase_			)

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

       UpperCAmelCase			:      Any		   =					tf.concat(tuple(lowercase_			)	,     0			)

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


     def __call__(						self					:	Union[str, Any]	,     *lowercase_					:	int	,     **lowercase_					:	Union[str, Any]			)		->      Optional[int]:
      UpperCAmelCase			:      str		   =					self._args_parser(*lowercase_	,     **lowercase_			)

      UpperCAmelCase			:      Tuple		   =					super().__call__(lowercase_	,     **lowercase_			)
      if len(lowercase_			) == 1:
       return results[0]
      return results


     def   UpperCAmelCase_			(						self					:	Optional[int]	,     lowercase_					:	List[Any]=None	,     lowercase_					:	Dict=None	,     lowercase_					:	str=None	,     **lowercase_					:	str			)		->      Any:
      UpperCAmelCase			:      Optional[Any]		   =					{}
      if padding is not None:
       UpperCAmelCase			:      Optional[int]		   =					padding
      if truncation is not None:
       UpperCAmelCase			:      int		   =					truncation

      UpperCAmelCase			:      Optional[Any]		   =					{}
      if sequential is not None:
       UpperCAmelCase			:      Dict		   =					sequential
      return preprocess_params, forward_params, {}


     def   UpperCAmelCase_			(						self					:	List[str]	,     lowercase_					:	Optional[int]	,     lowercase_					:	str=None	,     lowercase_					:	Tuple=True	,     lowercase_					:	Tuple=None			)		->      str:
      if truncation is None:
       if self.type == "tapas":
        UpperCAmelCase			:      List[Any]		   =					'drop_rows_to_fit'
       else:
        UpperCAmelCase			:      Dict		   =					'do_not_truncate'

      UpperCAmelCase   ,					UpperCAmelCase			:      Union[str, Any]		   =					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'			)
      UpperCAmelCase			:      Union[str, Any]		   =					self.tokenizer(lowercase_	,     lowercase_	,     return_tensors=self.framework	,     truncation=lowercase_	,     padding=lowercase_			)
      UpperCAmelCase			:      Union[str, Any]		   =					table
      return inputs


     def   UpperCAmelCase_			(						self					:	Tuple	,     lowercase_					:	Optional[int]	,     lowercase_					:	int=False			)		->      Optional[Any]:
      UpperCAmelCase			:      Dict		   =					model_inputs.pop('table'			)

      if self.type == "tapas":
       if sequential:
        UpperCAmelCase			:      List[Any]		   =					self.sequential_inference(**lowercase_			)
       else:
        UpperCAmelCase			:      Tuple		   =					self.batch_inference(**lowercase_			)
      else:
       UpperCAmelCase			:      Union[str, Any]		   =					self.model.generate(**lowercase_			)
      UpperCAmelCase			:      int		   =					{'model_inputs': model_inputs, 'table': table, 'outputs': outputs}
      return model_outputs




     def   UpperCAmelCase_			(						self					:	List[Any]	,     lowercase_					:	Tuple			)		->      Union[str, Any]:
      UpperCAmelCase			:      List[str]		   =					model_outputs['model_inputs']
      UpperCAmelCase			:      Optional[int]		   =					model_outputs['table']
      UpperCAmelCase			:      Optional[Any]		   =					model_outputs['outputs']
      if self.type == "tapas":
       if self.aggregate:
        UpperCAmelCase   ,					UpperCAmelCase			:      Union[str, Any]		   =					outputs[:2]
        UpperCAmelCase			:      str		   =					self.tokenizer.convert_logits_to_predictions(lowercase_	,     lowercase_	,     lowercase_			)
        UpperCAmelCase   ,					UpperCAmelCase			:      List[Any]		   =					predictions
        UpperCAmelCase			:      Union[str, Any]		   =					{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(lowercase_			)}

        UpperCAmelCase			:      List[Any]		   =					self.model.config.no_aggregation_label_index
        UpperCAmelCase			:      Tuple		   =					{
            i: aggregators[i] + ' > ' for i, pred in enumerate(lowercase_			) if pred != no_agg_label_index
        }
       else:
        UpperCAmelCase			:      List[str]		   =					outputs[0]
        UpperCAmelCase			:      str		   =					self.tokenizer.convert_logits_to_predictions(lowercase_	,     lowercase_			)
        UpperCAmelCase			:      Dict		   =					predictions[0]
        UpperCAmelCase			:      Optional[Any]		   =					{}
        UpperCAmelCase			:      Any		   =					{}
       UpperCAmelCase			:      Union[str, Any]		   =					[]
       for index, coordinates in enumerate(lowercase_			):
        UpperCAmelCase			:      str		   =					[table.iat[coordinate] for coordinate in coordinates]
        UpperCAmelCase			:      List[str]		   =					aggregators.get(lowercase_	,     ''			)
        UpperCAmelCase			:      List[Any]		   =					aggregators_prefix.get(lowercase_	,     ''			)
        UpperCAmelCase			:      Any		   =					{
            'answer': aggregator_prefix + ', '.join(lowercase_			),
            'coordinates': coordinates,
            'cells': [table.iat[coordinate] for coordinate in coordinates],
        }
        if aggregator:
         UpperCAmelCase			:      Any		   =					aggregator

        answers.append(lowercase_			)
       if len(lowercase_			) == 0:
        raise PipelineException('Empty answer'			)
      else:
       UpperCAmelCase			:      List[str]		   =					[{'answer': answer} for answer in self.tokenizer.batch_decode(lowercase_	,     skip_special_tokens=lowercase_			)]

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







'''simple docstring'''

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowercase__            =     "pcuenq/k-upscaler"




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			*, UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      Tuple		   =					{
     # norm1
     F"""{diffusers_resnet_prefix}.norm1.linear.weight""": checkpoint[F"""{resnet_prefix}.main.0.mapper.weight"""],
     F"""{diffusers_resnet_prefix}.norm1.linear.bias""": checkpoint[F"""{resnet_prefix}.main.0.mapper.bias"""],
     # conv1
     F"""{diffusers_resnet_prefix}.conv1.weight""": checkpoint[F"""{resnet_prefix}.main.2.weight"""],
     F"""{diffusers_resnet_prefix}.conv1.bias""": checkpoint[F"""{resnet_prefix}.main.2.bias"""],
     # norm2
     F"""{diffusers_resnet_prefix}.norm2.linear.weight""": checkpoint[F"""{resnet_prefix}.main.4.mapper.weight"""],
     F"""{diffusers_resnet_prefix}.norm2.linear.bias""": checkpoint[F"""{resnet_prefix}.main.4.mapper.bias"""],
     # conv2
     F"""{diffusers_resnet_prefix}.conv2.weight""": checkpoint[F"""{resnet_prefix}.main.6.weight"""],
     F"""{diffusers_resnet_prefix}.conv2.bias""": checkpoint[F"""{resnet_prefix}.main.6.bias"""],
 }

 if resnet.conv_shortcut is not None:
  rv.update(
      {
          F"""{diffusers_resnet_prefix}.conv_shortcut.weight""": checkpoint[F"""{resnet_prefix}.skip.weight"""],
      }      )

 return rv




def  UpperCamelCase(	UpperCAmelCase_    ,			*, UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      List[str]		   =					checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3    ,			dim=0      )
 UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      List[Any]		   =					checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3    ,			dim=0      )
 UpperCAmelCase			:      Optional[Any]		   =					{
     # norm
     F"""{diffusers_attention_prefix}.norm1.linear.weight""": checkpoint[F"""{attention_prefix}.norm_in.mapper.weight"""],
     F"""{diffusers_attention_prefix}.norm1.linear.bias""": checkpoint[F"""{attention_prefix}.norm_in.mapper.bias"""],
     # to_q
     F"""{diffusers_attention_prefix}.attn1.to_q.weight""": weight_q.squeeze(-1      ).squeeze(-1      ),
     F"""{diffusers_attention_prefix}.attn1.to_q.bias""": bias_q,
     # to_k
     F"""{diffusers_attention_prefix}.attn1.to_k.weight""": weight_k.squeeze(-1      ).squeeze(-1      ),
     F"""{diffusers_attention_prefix}.attn1.to_k.bias""": bias_k,
     # to_v
     F"""{diffusers_attention_prefix}.attn1.to_v.weight""": weight_v.squeeze(-1      ).squeeze(-1      ),
     F"""{diffusers_attention_prefix}.attn1.to_v.bias""": bias_v,
     # to_out
     F"""{diffusers_attention_prefix}.attn1.to_out.0.weight""": checkpoint[F"""{attention_prefix}.out_proj.weight"""]
     .squeeze(-1      )
     .squeeze(-1      ),
     F"""{diffusers_attention_prefix}.attn1.to_out.0.bias""": checkpoint[F"""{attention_prefix}.out_proj.bias"""],
 }

 return rv




def  UpperCamelCase(	UpperCAmelCase_    ,			*, UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase   ,					UpperCAmelCase			:      Union[str, Any]		   =					checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2    ,			dim=0      )
 UpperCAmelCase   ,					UpperCAmelCase			:      Any		   =					checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2    ,			dim=0      )

 UpperCAmelCase			:      Optional[int]		   =					{
     # norm2 (ada groupnorm)
     F"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight""": checkpoint[
         F"""{attention_prefix}.norm_dec.mapper.weight"""
     ],
     F"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias""": checkpoint[
         F"""{attention_prefix}.norm_dec.mapper.bias"""
     ],
     # layernorm on encoder_hidden_state
     F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight""": checkpoint[
         F"""{attention_prefix}.norm_enc.weight"""
     ],
     F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias""": checkpoint[
         F"""{attention_prefix}.norm_enc.bias"""
     ],
     # to_q
     F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight""": checkpoint[
         F"""{attention_prefix}.q_proj.weight"""
     ]
     .squeeze(-1      )
     .squeeze(-1      ),
     F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias""": checkpoint[
         F"""{attention_prefix}.q_proj.bias"""
     ],
     # to_k
     F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight""": weight_k.squeeze(-1      ).squeeze(-1      ),
     F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias""": bias_k,
     # to_v
     F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight""": weight_v.squeeze(-1      ).squeeze(-1      ),
     F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias""": bias_v,
     # to_out
     F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight""": checkpoint[
         F"""{attention_prefix}.out_proj.weight"""
     ]
     .squeeze(-1      )
     .squeeze(-1      ),
     F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias""": checkpoint[
         F"""{attention_prefix}.out_proj.bias"""
     ],
 }

 return rv




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      str		   =					'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
 UpperCAmelCase			:      Tuple		   =					F"""{block_prefix}.{block_idx}"""

 UpperCAmelCase			:      Union[str, Any]		   =					{}

 if not hasattr(UpperCAmelCase_    ,			'attentions'      ):
  UpperCAmelCase			:      List[Any]		   =					1  # resnet only
 elif not block.attentions[0].add_self_attention:
  UpperCAmelCase			:      str		   =					2  # resnet -> cross-attention
 else:
  UpperCAmelCase			:      List[str]		   =					3  # resnet -> self-attention -> cross-attention)

 for resnet_idx, resnet in enumerate(block.resnets      ):
  # diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
  UpperCAmelCase			:      Optional[Any]		   =					F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
  UpperCAmelCase			:      List[Any]		   =					n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
  UpperCAmelCase			:      Optional[Any]		   =					F"""{block_prefix}.{idx}""" if block_type == 'up' else F"""{block_prefix}.{idx}"""

  diffusers_checkpoint.update(
      resnet_to_diffusers_checkpoint(
          UpperCAmelCase_    ,			UpperCAmelCase_    ,			diffusers_resnet_prefix=UpperCAmelCase_    ,			resnet_prefix=UpperCAmelCase_      )      )

 if hasattr(UpperCAmelCase_    ,			'attentions'      ):
  for attention_idx, attention in enumerate(block.attentions      ):
   UpperCAmelCase			:      str		   =					F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
   UpperCAmelCase			:      Optional[int]		   =					n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
   UpperCAmelCase			:      int		   =					F"""{block_prefix}.{idx}"""
   UpperCAmelCase			:      Optional[int]		   =					F"""{block_prefix}.{idx }"""
   UpperCAmelCase			:      int		   =					1 if not attention.add_self_attention else 2
   UpperCAmelCase			:      Optional[Any]		   =					(
       n * attention_idx + cross_attention_index
       if block_type == 'up'
       else n * attention_idx + cross_attention_index + 1
   )
   UpperCAmelCase			:      List[Any]		   =					F"""{block_prefix}.{idx }"""

   diffusers_checkpoint.update(
       cross_attn_to_diffusers_checkpoint(
           UpperCAmelCase_    ,			diffusers_attention_prefix=UpperCAmelCase_    ,			diffusers_attention_index=2    ,			attention_prefix=UpperCAmelCase_    ,			)      )

   if attention.add_self_attention is True:
    diffusers_checkpoint.update(
        self_attn_to_diffusers_checkpoint(
            UpperCAmelCase_    ,			diffusers_attention_prefix=UpperCAmelCase_    ,			attention_prefix=UpperCAmelCase_    ,			)      )

 return diffusers_checkpoint




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      Tuple		   =					{}

 # pre-processing
 diffusers_checkpoint.update(
     {
         'conv_in.weight': checkpoint['inner_model.proj_in.weight'],
         'conv_in.bias': checkpoint['inner_model.proj_in.bias'],
     }      )

 # timestep and class embedding
 diffusers_checkpoint.update(
     {
         'time_proj.weight': checkpoint['inner_model.timestep_embed.weight'].squeeze(-1      ),
         'time_embedding.linear_1.weight': checkpoint['inner_model.mapping.0.weight'],
         'time_embedding.linear_1.bias': checkpoint['inner_model.mapping.0.bias'],
         'time_embedding.linear_2.weight': checkpoint['inner_model.mapping.2.weight'],
         'time_embedding.linear_2.bias': checkpoint['inner_model.mapping.2.bias'],
         'time_embedding.cond_proj.weight': checkpoint['inner_model.mapping_cond.weight'],
     }      )

 # down_blocks
 for down_block_idx, down_block in enumerate(model.down_blocks      ):
  diffusers_checkpoint.update(block_to_diffusers_checkpoint(UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			'down'      )      )

 # up_blocks
 for up_block_idx, up_block in enumerate(model.up_blocks      ):
  diffusers_checkpoint.update(block_to_diffusers_checkpoint(UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			'up'      )      )

 # post-processing
 diffusers_checkpoint.update(
     {
         'conv_out.weight': checkpoint['inner_model.proj_out.weight'],
         'conv_out.bias': checkpoint['inner_model.proj_out.bias'],
     }      )

 return diffusers_checkpoint




def  UpperCamelCase(	UpperCAmelCase_      ):
 UpperCAmelCase			:      List[Any]		   =					original_config['input_channels'] + original_config['unet_cond_dim']
 UpperCAmelCase			:      Any		   =					original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

 UpperCAmelCase			:      Tuple		   =					original_config['channels']

 assert (
     len(set(original_config['depths']      )      ) == 1
 ), "UNet2DConditionModel currently do not support blocks with different number of layers"
 UpperCAmelCase			:      str		   =					original_config['depths'][0]

 UpperCAmelCase			:      Tuple		   =					original_config['mapping_cond_dim']
 UpperCAmelCase			:      Any		   =					original_config['cross_cond_dim']

 UpperCAmelCase			:      Union[str, Any]		   =					[]
 UpperCAmelCase			:      List[str]		   =					[]
 for s, c in zip(original_config['self_attn_depths']    ,			original_config['cross_attn_depths']      ):
  if s:
   UpperCAmelCase			:      int		   =					'self'
   UpperCAmelCase			:      Any		   =					'cross' if c else None
  elif c:
   UpperCAmelCase			:      int		   =					'cross'
   UpperCAmelCase			:      str		   =					None
  else:
   UpperCAmelCase			:      Optional[int]		   =					None
   UpperCAmelCase			:      Any		   =					None
  attna_types.append(UpperCAmelCase_      )
  attna_types.append(UpperCAmelCase_      )

 UpperCAmelCase			:      List[Any]		   =					UNetaDConditionModel(
     in_channels=UpperCAmelCase_    ,			out_channels=UpperCAmelCase_    ,			down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')    ,			mid_block_type=UpperCAmelCase_    ,			up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')    ,			block_out_channels=UpperCAmelCase_    ,			layers_per_block=UpperCAmelCase_    ,			act_fn='gelu'    ,			norm_num_groups=UpperCAmelCase_    ,			cross_attention_dim=UpperCAmelCase_    ,			attention_head_dim=64    ,			time_cond_proj_dim=UpperCAmelCase_    ,			resnet_time_scale_shift='scale_shift'    ,			time_embedding_type='fourier'    ,			timestep_post_act='gelu'    ,			conv_in_kernel=1    ,			conv_out_kernel=1    ,			)

 return unet




def  UpperCamelCase(	UpperCAmelCase_      ):
 UpperCAmelCase			:      Optional[Any]		   =					torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )

 UpperCAmelCase			:      str		   =					huggingface_hub.hf_hub_download(UpperCAmelCase_    ,			'config_laion_text_cond_latent_upscaler_2.json'      )
 UpperCAmelCase			:      Optional[int]		   =					huggingface_hub.hf_hub_download(
     UpperCAmelCase_    ,			'laion_text_cond_latent_upscaler_2_1_00470000_slim.pth'      )
 print(F"""loading original model configuration from {orig_config_path}"""      )
 print(F"""loading original model checkpoint from {orig_weights_path}"""      )

 print('converting to diffusers unet'      )
 UpperCAmelCase			:      List[Any]		   =					K.config.load_config(open(UpperCAmelCase_      )      )['model']
 UpperCAmelCase			:      int		   =					unet_model_from_original_config(UpperCAmelCase_      )

 UpperCAmelCase			:      List[Any]		   =					torch.load(UpperCAmelCase_    ,			map_location=UpperCAmelCase_      )['model_ema']
 UpperCAmelCase			:      Optional[Any]		   =					unet_to_diffusers_checkpoint(UpperCAmelCase_    ,			UpperCAmelCase_      )

 model.load_state_dict(UpperCAmelCase_    ,			strict=UpperCAmelCase_      )
 model.save_pretrained(args.dump_path      )
 print(F"""saving converted unet model in {args.dump_path}"""      )


if __name__ == "__main__":
 lowercase__            =     argparse.ArgumentParser()

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

 main(args)




