


import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


__lowerCamelCase   	=  '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''


class 	a__						(    unittest.TestCase    ,							lowerCAmelCase_     ):

       def     UpperCAmelCase( self   :  Optional[int]     ):
            a_     :  List[Any]				      =     load_tool("""summarization"""     )
            self.tool.setup()
            a_     :  Any				      =     load_tool("""summarization"""   ,				remote=lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Any     ):
            a_     :  int				      =     self.tool(lowerCamelCase_     )
            self.assertEqual(
                lowerCamelCase_   ,				"""Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""   ,				)

       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            a_     :  str				      =     self.remote_tool(lowerCamelCase_     )
            self.assertEqual(
                lowerCamelCase_   ,				"""Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""   ,				)

       def     UpperCAmelCase( self   :  Tuple     ):
            a_     :  Any				      =     self.tool(text=lowerCamelCase_     )
            self.assertEqual(
                lowerCamelCase_   ,				"""Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""   ,				)

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            a_     :  Optional[Any]				      =     self.remote_tool(text=lowerCamelCase_     )
            self.assertEqual(
                lowerCamelCase_   ,				"""Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""   ,				)










import argparse

import torch

from diffusers import UNetaDConditionModel

def      a    (   _lowercase :  Dict			,    _lowercase :  Dict			,    _lowercase :  Any			,    _lowercase :  Tuple=None			,    _lowercase :  List[Any]=None			,    _lowercase :  Optional[Any]=None							)  ->Any:
		assert isinstance(_lowercase			,    _lowercase							), "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():
						UpperCamelCase__								=						old_checkpoint[path]
						UpperCamelCase__								=						old_tensor.shape[0] // 3

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

						UpperCamelCase__								=						old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

						UpperCamelCase__								=						query.reshape(_lowercase							)
						UpperCamelCase__								=						key.reshape(_lowercase							)
						UpperCamelCase__								=						value.reshape(_lowercase							)

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

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

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

        # proj_attn.weight has to be converted from conv 1D to linear
				UpperCamelCase__								=						old_checkpoint[path["""old"""]]
				UpperCamelCase__								=						["""proj_attn.weight"""]
				UpperCamelCase__								=						["""proj_out.weight""", """proj_in.weight"""]
				if any(k in new_path for k in names							):
						UpperCamelCase__								=						weight[:, :, 0]
				elif any(k in new_path for k in names_a							) and len(weight.shape							) > 2 and ".attentions." not in new_path:
						UpperCamelCase__								=						weight[:, :, 0]
				else:
						UpperCamelCase__								=						weight

def      a    (   _lowercase :  Tuple			,    _lowercase :  List[Any]=0							)  ->int:
		UpperCamelCase__								=						[]
		for old_item in old_list:
				UpperCamelCase__								=						old_item

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

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

				#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

		return mapping

def      a    (   _lowercase :  Union[str, Any]			,    _lowercase :  int=1							)  ->str:
		if n_shave_prefix_segments >= 0:
				return ".".join(path.split("""."""							)[n_shave_prefix_segments:]							)
		else:
				return ".".join(path.split("""."""							)[:n_shave_prefix_segments]							)

def      a    (   _lowercase :  List[str]			,    _lowercase :  Any=0							)  ->Any:
		UpperCamelCase__								=						[]
		for old_item in old_list:
				mapping.append({"""old""": old_item, """new""": old_item}							)

		return mapping

def      a    (   _lowercase :  List[str]			,    _lowercase :  int=0							)  ->Tuple:
		UpperCamelCase__								=						[]
		for old_item in old_list:
				UpperCamelCase__								=						old_item.replace("""in_layers.0"""			,    """norm1"""							)
				UpperCamelCase__								=						new_item.replace("""in_layers.2"""			,    """conv1"""							)

				UpperCamelCase__								=						new_item.replace("""out_layers.0"""			,    """norm2"""							)
				UpperCamelCase__								=						new_item.replace("""out_layers.3"""			,    """conv2"""							)

				UpperCamelCase__								=						new_item.replace("""emb_layers.1"""			,    """time_emb_proj"""							)
				UpperCamelCase__								=						new_item.replace("""skip_connection"""			,    """conv_shortcut"""							)

				UpperCamelCase__								=						shave_segments(_lowercase			,    n_shave_prefix_segments=_lowercase							)

				if "temopral_conv" not in old_item:
						mapping.append({"""old""": old_item, """new""": new_item}							)

		return mapping

def      a    (   _lowercase :  List[Any]			,    _lowercase :  List[Any]			,    _lowercase :  int=None			,    _lowercase :  Optional[Any]=False							)  ->List[str]:
		UpperCamelCase__								=						{}
		UpperCamelCase__								=						list(checkpoint.keys()							)

		UpperCamelCase__								=						"""model.diffusion_model."""

		# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
		if sum(k.startswith("""model_ema"""							) for k in keys							) > 1_0_0 and extract_ema:
				print(F"""Checkpoint {path} has both EMA and non-EMA weights."""							)
				print(
				    """In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"""
				    """ weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."""							)
				for key in keys:
						if key.startswith("""model.diffusion_model"""							):
								UpperCamelCase__								=						"""model_ema.""" + """""".join(key.split("""."""							)[1:]							)
								UpperCamelCase__								=						checkpoint.pop(_lowercase							)
		else:
				if sum(k.startswith("""model_ema"""							) for k in keys							) > 1_0_0:
						print(
						    """In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"""
						    """ weights (usually better for inference), please make sure to add the `--extract_ema` flag."""							)

				for key in keys:
						UpperCamelCase__								=						checkpoint.pop(_lowercase							)

		UpperCamelCase__								=						{}

		UpperCamelCase__								=						unet_state_dict["""time_embed.0.weight"""]
		UpperCamelCase__								=						unet_state_dict["""time_embed.0.bias"""]
		UpperCamelCase__								=						unet_state_dict["""time_embed.2.weight"""]
		UpperCamelCase__								=						unet_state_dict["""time_embed.2.bias"""]

		if config["class_embed_type"] is None:
				# No parameters to port
				...
		elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
				UpperCamelCase__								=						unet_state_dict["""label_emb.0.0.weight"""]
				UpperCamelCase__								=						unet_state_dict["""label_emb.0.0.bias"""]
				UpperCamelCase__								=						unet_state_dict["""label_emb.0.2.weight"""]
				UpperCamelCase__								=						unet_state_dict["""label_emb.0.2.bias"""]
		else:
				raise NotImplementedError(F"""Not implemented `class_embed_type`: {config['class_embed_type']}"""							)

		UpperCamelCase__								=						unet_state_dict["""input_blocks.0.0.weight"""]
		UpperCamelCase__								=						unet_state_dict["""input_blocks.0.0.bias"""]

		UpperCamelCase__								=						[v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""							)]
		UpperCamelCase__								=						renew_attention_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
		assign_to_checkpoint(_lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

		UpperCamelCase__								=						unet_state_dict["""out.0.weight"""]
		UpperCamelCase__								=						unet_state_dict["""out.0.bias"""]
		UpperCamelCase__								=						unet_state_dict["""out.2.weight"""]
		UpperCamelCase__								=						unet_state_dict["""out.2.bias"""]

		# Retrieves the keys for the input blocks only
		UpperCamelCase__								=						len({""".""".join(layer.split("""."""							)[:2]							) for layer in unet_state_dict if """input_blocks""" in layer}							)
		UpperCamelCase__								=						{
		    layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
		    for layer_id in range(_lowercase							)
		}

		# Retrieves the keys for the middle blocks only
		UpperCamelCase__								=						len({""".""".join(layer.split("""."""							)[:2]							) for layer in unet_state_dict if """middle_block""" in layer}							)
		UpperCamelCase__								=						{
		    layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
		    for layer_id in range(_lowercase							)
		}

		# Retrieves the keys for the output blocks only
		UpperCamelCase__								=						len({""".""".join(layer.split("""."""							)[:2]							) for layer in unet_state_dict if """output_blocks""" in layer}							)
		UpperCamelCase__								=						{
		    layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
		    for layer_id in range(_lowercase							)
		}

		for i in range(1			,    _lowercase							):
				UpperCamelCase__								=						(i - 1) // (config["""layers_per_block"""] + 1)
				UpperCamelCase__								=						(i - 1) % (config["""layers_per_block"""] + 1)

				UpperCamelCase__								=						[
				    key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
				]
				UpperCamelCase__								=						[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
				UpperCamelCase__								=						[key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]

				if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
						UpperCamelCase__								=						unet_state_dict.pop(
						    F"""input_blocks.{i}.op.weight"""							)
						UpperCamelCase__								=						unet_state_dict.pop(
						    F"""input_blocks.{i}.op.bias"""							)

				UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
				UpperCamelCase__								=						{"""old""": F"""input_blocks.{i}.0""", """new""": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
				assign_to_checkpoint(
				    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

				UpperCamelCase__								=						[key for key in resnets if """temopral_conv""" in key]
				UpperCamelCase__								=						renew_temp_conv_paths(_lowercase							)
				UpperCamelCase__								=						{
				    """old""": F"""input_blocks.{i}.0.temopral_conv""",
				    """new""": F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
				}
				assign_to_checkpoint(
				    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

				if len(_lowercase							):
						UpperCamelCase__								=						renew_attention_paths(_lowercase							)
						UpperCamelCase__								=						{"""old""": F"""input_blocks.{i}.1""", """new""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
						assign_to_checkpoint(
						    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

				if len(_lowercase							):
						UpperCamelCase__								=						renew_attention_paths(_lowercase							)
						UpperCamelCase__								=						{
						    """old""": F"""input_blocks.{i}.2""",
						    """new""": F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
						}
						assign_to_checkpoint(
						    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

		UpperCamelCase__								=						middle_blocks[0]
		UpperCamelCase__								=						[key for key in resnet_a if """temopral_conv""" in key]
		UpperCamelCase__								=						middle_blocks[1]
		UpperCamelCase__								=						middle_blocks[2]
		UpperCamelCase__								=						middle_blocks[3]
		UpperCamelCase__								=						[key for key in resnet_a if """temopral_conv""" in key]

		UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    config=_lowercase			,    additional_replacements=[meta_path]							)

		UpperCamelCase__								=						renew_temp_conv_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    config=_lowercase			,    additional_replacements=[meta_path]							)

		UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    config=_lowercase			,    additional_replacements=[meta_path]							)

		UpperCamelCase__								=						renew_temp_conv_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    config=_lowercase			,    additional_replacements=[meta_path]							)

		UpperCamelCase__								=						renew_attention_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

		UpperCamelCase__								=						renew_attention_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

		for i in range(_lowercase							):
				UpperCamelCase__								=						i // (config["""layers_per_block"""] + 1)
				UpperCamelCase__								=						i % (config["""layers_per_block"""] + 1)
				UpperCamelCase__								=						[shave_segments(_lowercase			,    2							) for name in output_blocks[i]]
				UpperCamelCase__								=						{}

				for layer in output_block_layers:
						UpperCamelCase__	,      UpperCamelCase__								=						layer.split("""."""							)[0], shave_segments(_lowercase			,    1							)
						if layer_id in output_block_list:
								output_block_list[layer_id].append(_lowercase							)
						else:
								UpperCamelCase__								=						[layer_name]

				if len(_lowercase							) > 1:
						UpperCamelCase__								=						[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
						UpperCamelCase__								=						[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
						UpperCamelCase__								=						[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

						UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
						UpperCamelCase__								=						renew_resnet_paths(_lowercase							)

						UpperCamelCase__								=						{"""old""": F"""output_blocks.{i}.0""", """new""": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
						assign_to_checkpoint(
						    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

						UpperCamelCase__								=						[key for key in resnets if """temopral_conv""" in key]
						UpperCamelCase__								=						renew_temp_conv_paths(_lowercase							)
						UpperCamelCase__								=						{
						    """old""": F"""output_blocks.{i}.0.temopral_conv""",
						    """new""": F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
						}
						assign_to_checkpoint(
						    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

						UpperCamelCase__								=						{k: sorted(_lowercase							) for k, v in output_block_list.items()}
						if ["conv.bias", "conv.weight"] in output_block_list.values():
								UpperCamelCase__								=						list(output_block_list.values()							).index(["""conv.bias""", """conv.weight"""]							)
								UpperCamelCase__								=						unet_state_dict[
								    F"""output_blocks.{i}.{index}.conv.weight"""
								]
								UpperCamelCase__								=						unet_state_dict[
								    F"""output_blocks.{i}.{index}.conv.bias"""
								]

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

						if len(_lowercase							):
								UpperCamelCase__								=						renew_attention_paths(_lowercase							)
								UpperCamelCase__								=						{
								    """old""": F"""output_blocks.{i}.1""",
								    """new""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
								}
								assign_to_checkpoint(
								    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

						if len(_lowercase							):
								UpperCamelCase__								=						renew_attention_paths(_lowercase							)
								UpperCamelCase__								=						{
								    """old""": F"""output_blocks.{i}.2""",
								    """new""": F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
								}
								assign_to_checkpoint(
								    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)
				else:
						UpperCamelCase__								=						renew_resnet_paths(_lowercase			,    n_shave_prefix_segments=1							)
						for path in resnet_0_paths:
								UpperCamelCase__								=						""".""".join(["""output_blocks""", str(_lowercase							), path["""old"""]]							)
								UpperCamelCase__								=						""".""".join(["""up_blocks""", str(_lowercase							), """resnets""", str(_lowercase							), path["""new"""]]							)
								UpperCamelCase__								=						unet_state_dict[old_path]

						UpperCamelCase__								=						[l for l in output_block_layers if """temopral_conv""" in l]
						for path in temopral_conv_paths:
								UpperCamelCase__								=						path.split("""temopral_conv."""							)[-1]
								UpperCamelCase__								=						""".""".join(["""output_blocks""", str(_lowercase							), str(_lowercase							), """temopral_conv""", pruned_path]							)
								UpperCamelCase__								=						""".""".join(["""up_blocks""", str(_lowercase							), """temp_convs""", str(_lowercase							), pruned_path]							)
								UpperCamelCase__								=						unet_state_dict[old_path]

		return new_checkpoint


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

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

				__lowerCAmelCase 	=      torch.load(args.checkpoint_path, map_location="cpu")
				__lowerCAmelCase 	=      UNetaDConditionModel()

				__lowerCAmelCase 	=      convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

				__lowerCAmelCase 	=      set(unet.state_dict().keys()) - set(converted_ckpt.keys())
				__lowerCAmelCase 	=      set(converted_ckpt.keys()) - set(unet.state_dict().keys())

				assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

				# load state_dict
				unet.load_state_dict(converted_ckpt)

				unet.save_pretrained(args.dump_path)

				# -- finish converting the unet --





