





'''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 	_lowercase    (				_snake_case					):


   def __call__(			self     , _UpperCAmelCase=None     , _UpperCAmelCase=None     , **_UpperCAmelCase       ):
       # 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:
               A							:							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:
                   A							:							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:
           A							:							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.'''       )

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

       return tqa_pipeline_inputs







@add_end_docstrings(_snake_case					)
class 	_lowercase    (				_snake_case					):
   _UpperCamelCase									= """table,query"""


   def __init__(			self     , _UpperCAmelCase=TableQuestionAnsweringArgumentHandler()     , *_UpperCAmelCase     , **_UpperCAmelCase       ):
       super().__init__(*lowercase_     , **lowercase_       )
       A							:							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()       )       )

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


   def        snake_case					(			self     , **_UpperCAmelCase       ):
       return self.model(**lowercase_       )


   def        snake_case					(			self     , **_UpperCAmelCase       ):
       if self.framework == "pt":
           A							:							Dict				=    []
           A							:							Any				=    []
           A							:							List[Any]				=    None
           A							:							int				=    inputs['input_ids'].shape[0]

           A							:							Union[str, Any]				=    inputs['input_ids'].to(self.device       )
           A							:							Optional[Any]				=    inputs['attention_mask'].to(self.device       )
           A							:							int				=    inputs['token_type_ids'].to(self.device       )
           A							:							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:
                   A							:							Dict				=    token_type_ids_example[:, 3]  # shape (seq_len,)
                   A							:							List[str]				=    np.zeros_like(prev_labels_example.cpu().numpy()       )  # shape (seq_len,)

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

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

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

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

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

               all_logits.append(lowercase_       )

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

               A							:							Optional[int]				=    collections.defaultdict(lowercase_       )
               for i, p in enumerate(probabilities.squeeze().tolist()       ):
                   A							:							Tuple				=    token_type_ids_example[:, 0].tolist()[i]
                   A							:							int				=    token_type_ids_example[:, 1].tolist()[i] - 1
                   A							:							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_       )

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

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

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

           A							:							Optional[int]				=    inputs['input_ids']
           A							:							Tuple				=    inputs['attention_mask']
           A							:							List[str]				=    inputs['token_type_ids'].numpy()
           A							:							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:
                   A							:							List[Any]				=    token_type_ids_example[:, 3]  # shape (seq_len,)
                   A							:							Dict				=    np.zeros_like(lowercase_     , dtype=np.intaa       )  # shape (seq_len,)

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

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

                   A							:							Union[str, Any]				=    model_labels

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

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

               all_logits.append(lowercase_       )

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

               A							:							Optional[Any]				=    collections.defaultdict(lowercase_       )
               A							:							Union[str, Any]				=    token_type_ids_example
               for i, p in enumerate(tf.squeeze(lowercase_       ).numpy().tolist()       ):
                   A							:							Any				=    token_type_ids_example[:, 0].tolist()[i]
                   A							:							List[str]				=    token_type_ids_example[:, 1].tolist()[i] - 1
                   A							:							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_       )

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

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

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


   def __call__(			self     , *_UpperCAmelCase     , **_UpperCAmelCase       ):
       A							:							str				=    self._args_parser(*lowercase_     , **lowercase_       )

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


   def        snake_case					(			self     , _UpperCAmelCase=None     , _UpperCAmelCase=None     , _UpperCAmelCase=None     , **_UpperCAmelCase       ):
       A							:							Optional[Any]				=    {}
       if padding is not None:
           A							:							Optional[int]				=    padding
       if truncation is not None:
           A							:							int				=    truncation

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


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=None     , _UpperCAmelCase=True     , _UpperCAmelCase=None       ):
       if truncation is None:
           if self.type == "tapas":
               A							:							List[Any]				=    'drop_rows_to_fit'
           else:
               A							:							Dict				=    'do_not_truncate'

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


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=False       ):
       A							:							Dict				=    model_inputs.pop('''table'''       )

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


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

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

               answers.append(lowercase_       )
           if len(lowercase_       ) == 0:
               raise PipelineException('''Empty answer'''       )
       else:
           A							:							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)




