
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, 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'''
					lowercase						     =						[]
					for old_item in old_list:
										lowercase						     =						old_item.replace('''in_layers.0'''			,      '''norm1'''		)
										lowercase						     =						new_item.replace('''in_layers.2'''			,      '''conv1'''		)

										lowercase						     =						new_item.replace('''out_layers.0'''			,      '''norm2'''		)
										lowercase						     =						new_item.replace('''out_layers.3'''			,      '''conv2'''		)

										lowercase						     =						new_item.replace('''emb_layers.1'''			,      '''time_emb_proj'''		)
										lowercase						     =						new_item.replace('''skip_connection'''			,      '''conv_shortcut'''		)

										lowercase						     =						shave_segments(lowerCAmelCase__			,      n_shave_prefix_segments=lowerCAmelCase__		)

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

					return mapping



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__=0		):
					'''simple docstring'''
					lowercase						     =						[]
					for old_item in old_list:
										lowercase						     =						old_item

										lowercase						     =						new_item.replace('''norm.weight'''			,      '''group_norm.weight'''		)
										lowercase						     =						new_item.replace('''norm.bias'''			,      '''group_norm.bias'''		)

										lowercase						     =						new_item.replace('''proj_out.weight'''			,      '''proj_attn.weight'''		)
										lowercase						     =						new_item.replace('''proj_out.bias'''			,      '''proj_attn.bias'''		)

										lowercase						     =						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."

					# Splits the attention layers into three variables.
					if attention_paths_to_split is not None:
										for path, path_map in attention_paths_to_split.items():
															lowercase						     =						old_checkpoint[path]
															lowercase						     =						old_tensor.shape[0] // 3

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

															lowercase						     =						old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

															lowercase						     =						query.reshape(lowerCAmelCase__		)
															lowercase						     =						key.reshape(lowerCAmelCase__		)
															lowercase						     =						value.reshape(lowerCAmelCase__		)

					for path in paths:
										lowercase						     =						path['''new''']

										# These have already been assigned
										if attention_paths_to_split is not None and new_path in attention_paths_to_split:
															continue

										# Global renaming happens here
										lowercase						     =						new_path.replace('''middle_block.0'''			,      '''mid_block.resnets.0'''		)
										lowercase						     =						new_path.replace('''middle_block.1'''			,      '''mid_block.attentions.0'''		)
										lowercase						     =						new_path.replace('''middle_block.2'''			,      '''mid_block.resnets.1'''		)

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

        # proj_attn.weight has to be converted from conv 1D to linear
										if "proj_attn.weight" in new_path:
															lowercase						     =						old_checkpoint[path['''old''']][:, :, 0]
										else:
															lowercase						     =						old_checkpoint[path['''old''']]



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						{}

					lowercase						     =						checkpoint['''time_embed.0.weight''']
					lowercase						     =						checkpoint['''time_embed.0.bias''']
					lowercase						     =						checkpoint['''time_embed.2.weight''']
					lowercase						     =						checkpoint['''time_embed.2.bias''']

					lowercase						     =						checkpoint['''input_blocks.0.0.weight''']
					lowercase						     =						checkpoint['''input_blocks.0.0.bias''']

					lowercase						     =						checkpoint['''out.0.weight''']
					lowercase						     =						checkpoint['''out.0.bias''']
					lowercase						     =						checkpoint['''out.2.weight''']
					lowercase						     =						checkpoint['''out.2.bias''']

					# Retrieves the keys for the input blocks only
					lowercase						     =						len({'''.'''.join(layer.split('''.'''		)[:2]		) for layer in checkpoint if '''input_blocks''' in layer}		)
					lowercase						     =						{
					    layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
					    for layer_id in range(lowerCAmelCase__		)
					}

					# Retrieves the keys for the middle blocks only
					lowercase						     =						len({'''.'''.join(layer.split('''.'''		)[:2]		) for layer in checkpoint if '''middle_block''' in layer}		)
					lowercase						     =						{
					    layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
					    for layer_id in range(lowerCAmelCase__		)
					}

					# Retrieves the keys for the output blocks only
					lowercase						     =						len({'''.'''.join(layer.split('''.'''		)[:2]		) for layer in checkpoint if '''output_blocks''' in layer}		)
					lowercase						     =						{
					    layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
					    for layer_id in range(lowerCAmelCase__		)
					}

					for i in range(1			,      lowerCAmelCase__		):
										lowercase						     =						(i - 1) // (config['''num_res_blocks'''] + 1)
										lowercase						     =						(i - 1) % (config['''num_res_blocks'''] + 1)

										lowercase						     =						[key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
										lowercase						     =						[key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]

										if f'input_blocks.{i}.0.op.weight' in checkpoint:
															lowercase						     =						checkpoint[
															    f'input_blocks.{i}.0.op.weight'
															]
															lowercase						     =						checkpoint[
															    f'input_blocks.{i}.0.op.bias'
															]
															continue

										lowercase						     =						renew_resnet_paths(lowerCAmelCase__		)
										lowercase						     =						{'''old''': f'input_blocks.{i}.0', '''new''': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
										lowercase						     =						{'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
										assign_to_checkpoint(
										    lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      additional_replacements=[meta_path, resnet_op]			,      config=lowerCAmelCase__		)

										if len(lowerCAmelCase__		):
															lowercase						     =						renew_attention_paths(lowerCAmelCase__		)
															lowercase						     =						{
															    '''old''': f'input_blocks.{i}.1',
															    '''new''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
															}
															lowercase						     =						{
															    f'input_blocks.{i}.1.qkv.bias': {
															        '''key''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
															        '''query''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
															        '''value''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
															    },
															    f'input_blocks.{i}.1.qkv.weight': {
															        '''key''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
															        '''query''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
															        '''value''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
															    },
															}
															assign_to_checkpoint(
															    lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      additional_replacements=[meta_path]			,      attention_paths_to_split=lowerCAmelCase__			,      config=lowerCAmelCase__			,      )

					lowercase						     =						middle_blocks[0]
					lowercase						     =						middle_blocks[1]
					lowercase						     =						middle_blocks[2]

					lowercase						     =						renew_resnet_paths(lowerCAmelCase__		)
					assign_to_checkpoint(lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      config=lowerCAmelCase__		)

					lowercase						     =						renew_resnet_paths(lowerCAmelCase__		)
					assign_to_checkpoint(lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      config=lowerCAmelCase__		)

					lowercase						     =						renew_attention_paths(lowerCAmelCase__		)
					lowercase						     =						{
					    '''middle_block.1.qkv.bias''': {
					        '''key''': '''mid_block.attentions.0.key.bias''',
					        '''query''': '''mid_block.attentions.0.query.bias''',
					        '''value''': '''mid_block.attentions.0.value.bias''',
					    },
					    '''middle_block.1.qkv.weight''': {
					        '''key''': '''mid_block.attentions.0.key.weight''',
					        '''query''': '''mid_block.attentions.0.query.weight''',
					        '''value''': '''mid_block.attentions.0.value.weight''',
					    },
					}
					assign_to_checkpoint(
					    lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      attention_paths_to_split=lowerCAmelCase__			,      config=lowerCAmelCase__		)

					for i in range(lowerCAmelCase__		):
										lowercase						     =						i // (config['''num_res_blocks'''] + 1)
										lowercase						     =						i % (config['''num_res_blocks'''] + 1)
										lowercase						     =						[shave_segments(lowerCAmelCase__			,      2		) for name in output_blocks[i]]
										lowercase						     =						{}

										for layer in output_block_layers:
															lowercase     ,						lowercase						     =						layer.split('''.'''		)[0], shave_segments(lowerCAmelCase__			,      1		)
															if layer_id in output_block_list:
																				output_block_list[layer_id].append(lowerCAmelCase__		)
															else:
																				lowercase						     =						[layer_name]

										if len(lowerCAmelCase__		) > 1:
															lowercase						     =						[key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
															lowercase						     =						[key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

															lowercase						     =						renew_resnet_paths(lowerCAmelCase__		)
															lowercase						     =						renew_resnet_paths(lowerCAmelCase__		)

															lowercase						     =						{'''old''': f'output_blocks.{i}.0', '''new''': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
															assign_to_checkpoint(lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      additional_replacements=[meta_path]			,      config=lowerCAmelCase__		)

															if ["conv.weight", "conv.bias"] in output_block_list.values():
																				lowercase						     =						list(output_block_list.values()		).index(['''conv.weight''', '''conv.bias''']		)
																				lowercase						     =						checkpoint[
																				    f'output_blocks.{i}.{index}.conv.weight'
																				]
																				lowercase						     =						checkpoint[
																				    f'output_blocks.{i}.{index}.conv.bias'
																				]

																				# Clear attentions as they have been attributed above.
																				if len(lowerCAmelCase__		) == 2:
																									lowercase						     =						[]

															if len(lowerCAmelCase__		):
																				lowercase						     =						renew_attention_paths(lowerCAmelCase__		)
																				lowercase						     =						{
																				    '''old''': f'output_blocks.{i}.1',
																				    '''new''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
																				}
																				lowercase						     =						{
																				    f'output_blocks.{i}.1.qkv.bias': {
																				        '''key''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
																				        '''query''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
																				        '''value''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
																				    },
																				    f'output_blocks.{i}.1.qkv.weight': {
																				        '''key''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
																				        '''query''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
																				        '''value''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
																				    },
																				}
																				assign_to_checkpoint(
																				    lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      additional_replacements=[meta_path]			,      attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions		) else None			,      config=lowerCAmelCase__			,      )
										else:
															lowercase						     =						renew_resnet_paths(lowerCAmelCase__			,      n_shave_prefix_segments=1		)
															for path in resnet_0_paths:
																				lowercase						     =						'''.'''.join(['''output_blocks''', str(lowerCAmelCase__		), path['''old''']]		)
																				lowercase						     =						'''.'''.join(['''up_blocks''', str(lowerCAmelCase__		), '''resnets''', str(lowerCAmelCase__		), path['''new''']]		)

																				lowercase						     =						checkpoint[old_path]

					return new_checkpoint


if __name__ == "__main__":
							lowercase__		:str			  =   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.")

							lowercase__		:Optional[Any]			  =   parser.parse_args()

							lowercase__		:str			  =   torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														lowercase__		:Optional[Any]			  =   json.loads(f.read())

							lowercase__		:str			  =   convert_ldm_checkpoint(checkpoint, config)

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

							lowercase__		:List[Any]			  =   UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														lowercase__		:Tuple			  =   DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
														lowercase__		:List[Any]			  =   VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

														lowercase__		:Any			  =   LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
							import numpy as np
							import tensorflow as tf

							from transformers import TFXLMRobertaModel







@require_tf
@require_sentencepiece
@require_tokenizers
class     lowercase		( unittest.TestCase		):




							@slow
							def 			A__				(    self):
												lowercase						     =						TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base''')

												lowercase						     =						{
												    '''input_ids''': tf.convert_to_tensor([[0, 2_6_4_6, 1_0_2_6_9, 8_3, 9_9_9_4_2, 2]]       ,dtype=tf.intaa),  # "My dog is cute"
												    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]       ,dtype=tf.intaa),
												}

												lowercase						     =						model(A__)['''last_hidden_state''']
												lowercase						     =						tf.TensorShape((1, 6, 7_6_8))
												self.assertEqual(output.shape       ,A__)
												# compare the actual values for a slice.
												lowercase						     =						tf.convert_to_tensor(
												    [
												        [
												            [0.0681762, 0.10894451, 0.06772504],
												            [-0.06423668, 0.02366615, 0.04329344],
												            [-0.06057295, 0.09974135, -0.00070584],
												        ]
												    ]       ,dtype=tf.floataa       ,)

												self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,expected_slice.numpy()       ,atol=1E-4))




