


import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCamelCase : Tuple						   =       [
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

lowerCamelCase : Optional[Any]						   =       [
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

lowerCamelCase : Tuple						   =       []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
	# loop over downblocks/upblocks

	for j in range(2):
		# loop over resnets/attentions for downblocks
		lowerCamelCase : str						   =       f'''down_blocks.{i}.resnets.{j}.'''
		lowerCamelCase : Optional[Any]						   =       f'''input_blocks.{3*i + j + 1}.0.'''
		unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

		if i < 3:
			# no attention layers in down_blocks.3
			lowerCamelCase : Optional[Any]						   =       f'''down_blocks.{i}.attentions.{j}.'''
			lowerCamelCase : str						   =       f'''input_blocks.{3*i + j + 1}.1.'''
			unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

	for j in range(3):
		# loop over resnets/attentions for upblocks
		lowerCamelCase : Union[str, Any]						   =       f'''up_blocks.{i}.resnets.{j}.'''
		lowerCamelCase : Optional[Any]						   =       f'''output_blocks.{3*i + j}.0.'''
		unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

		if i > 0:
			# no attention layers in up_blocks.0
			lowerCamelCase : List[Any]						   =       f'''up_blocks.{i}.attentions.{j}.'''
			lowerCamelCase : Optional[int]						   =       f'''output_blocks.{3*i + j}.1.'''
			unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

	if i < 3:
		# no downsample in down_blocks.3
		lowerCamelCase : List[str]						   =       f'''down_blocks.{i}.downsamplers.0.conv.'''
		lowerCamelCase : List[str]						   =       f'''input_blocks.{3*(i+1)}.0.op.'''
		unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

		# no upsample in up_blocks.3
		lowerCamelCase : Optional[int]						   =       f'''up_blocks.{i}.upsamplers.0.'''
		lowerCamelCase : Optional[int]						   =       f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
		unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCamelCase : Optional[Any]						   =       '''mid_block.attentions.0.'''
lowerCamelCase : Tuple						   =       '''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
	lowerCamelCase : Optional[Any]						   =       f'''mid_block.resnets.{j}.'''
	lowerCamelCase : int						   =       f'''middle_block.{2*j}.'''
	unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def       snake_case_		(     lowerCAmelCase_       :						Optional[int]      ):
							# buyer beware: this is a *brittle* function,
							# and correct output requires that all of these pieces interact in
							# the exact order in which I have arranged them.
							__lowercase      :							List[str]				=     {k: k for k in unet_state_dict.keys()}
							for sd_name, hf_name in unet_conversion_map:
														__lowercase      :							Union[str, Any]				=     sd_name
							for k, v in mapping.items():
														if "resnets" in k:
																					for sd_part, hf_part in unet_conversion_map_resnet:
																												__lowercase      :							List[str]				=     v.replace(lowerCAmelCase_       ,       lowerCAmelCase_      )
																					__lowercase      :							str				=     v
							for k, v in mapping.items():
														for sd_part, hf_part in unet_conversion_map_layer:
																					__lowercase      :							int				=     v.replace(lowerCAmelCase_       ,       lowerCAmelCase_      )
														__lowercase      :							List[Any]				=     v
							__lowercase      :							List[str]				=     {v: unet_state_dict[k] for k, v in mapping.items()}
							return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCamelCase : List[str]						   =       [
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
	# down_blocks have two resnets
	for j in range(2):
		lowerCamelCase : str						   =       f'''encoder.down_blocks.{i}.resnets.{j}.'''
		lowerCamelCase : Dict						   =       f'''encoder.down.{i}.block.{j}.'''
		vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

	if i < 3:
		lowerCamelCase : List[str]						   =       f'''down_blocks.{i}.downsamplers.0.'''
		lowerCamelCase : Tuple						   =       f'''down.{i}.downsample.'''
		vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

		lowerCamelCase : Union[str, Any]						   =       f'''up_blocks.{i}.upsamplers.0.'''
		lowerCamelCase : List[str]						   =       f'''up.{3-i}.upsample.'''
		vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

	# up_blocks have three resnets
	# also, up blocks in hf are numbered in reverse from sd
	for j in range(3):
		lowerCamelCase : Dict						   =       f'''decoder.up_blocks.{i}.resnets.{j}.'''
		lowerCamelCase : Any						   =       f'''decoder.up.{3-i}.block.{j}.'''
		vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
	lowerCamelCase : Optional[Any]						   =       f'''mid_block.resnets.{i}.'''
	lowerCamelCase : Dict						   =       f'''mid.block_{i+1}.'''
	vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCamelCase : str						   =       [
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]
def       snake_case_		(     lowerCAmelCase_       :						Tuple      ):
							# convert HF linear weights to SD conv2d weights
							return w.reshape(*w.shape       ,       1       ,       1      )
def       snake_case_		(     lowerCAmelCase_       :						Union[str, Any]      ):
							__lowercase      :							str				=     {k: k for k in vae_state_dict.keys()}
							for k, v in mapping.items():
														for sd_part, hf_part in vae_conversion_map:
																					__lowercase      :							Optional[int]				=     v.replace(lowerCAmelCase_       ,       lowerCAmelCase_      )
														__lowercase      :							Optional[int]				=     v
							for k, v in mapping.items():
														if "attentions" in k:
																					for sd_part, hf_part in vae_conversion_map_attn:
																												__lowercase      :							Any				=     v.replace(lowerCAmelCase_       ,       lowerCAmelCase_      )
																					__lowercase      :							Optional[Any]				=     v
							__lowercase      :							Any				=     {v: vae_state_dict[k] for k, v in mapping.items()}
							__lowercase      :							str				=     ["""q""", """k""", """v""", """proj_out"""]
							for k, v in new_state_dict.items():
														for weight_name in weights_to_convert:
																					if F"mid.attn_1.{weight_name}.weight" in k:
																												print(F"Reshaping {k} for SD format"      )
																												__lowercase      :							Dict				=     reshape_weight_for_sd(lowerCAmelCase_      )
							return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCamelCase : Tuple						   =       [
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
lowerCamelCase : Union[str, Any]						   =       {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCamelCase : Any						   =       re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCamelCase : Dict						   =       {'''q''': 0, '''k''': 1, '''v''': 2}
def       snake_case_		(     lowerCAmelCase_       :						str      ):
							__lowercase      :							List[Any]				=     {}
							__lowercase      :							Tuple				=     {}
							__lowercase      :							Dict				=     {}
							for k, v in text_enc_dict.items():
														if (
														    k.endswith(""".self_attn.q_proj.weight"""      )
														    or k.endswith(""".self_attn.k_proj.weight"""      )
														    or k.endswith(""".self_attn.v_proj.weight"""      )
														):
																					__lowercase      :							List[str]				=     k[: -len(""".q_proj.weight"""      )]
																					__lowercase      :							Any				=     k[-len("""q_proj.weight"""      )]
																					if k_pre not in capture_qkv_weight:
																												__lowercase      :							Any				=     [None, None, None]
																					__lowercase      :							List[Any]				=     v
																					continue

														if (
														    k.endswith(""".self_attn.q_proj.bias"""      )
														    or k.endswith(""".self_attn.k_proj.bias"""      )
														    or k.endswith(""".self_attn.v_proj.bias"""      )
														):
																					__lowercase      :							Optional[Any]				=     k[: -len(""".q_proj.bias"""      )]
																					__lowercase      :							Optional[Any]				=     k[-len("""q_proj.bias"""      )]
																					if k_pre not in capture_qkv_bias:
																												__lowercase      :							Any				=     [None, None, None]
																					__lowercase      :							List[str]				=     v
																					continue

														__lowercase      :							List[Any]				=     textenc_pattern.sub(lambda lowerCAmelCase_      : protected[re.escape(m.group(0      )      )]       ,       lowerCAmelCase_      )
														__lowercase      :							Union[str, Any]				=     v

							for k_pre, tensors in capture_qkv_weight.items():
														if None in tensors:
																					raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""      )
														__lowercase      :							Any				=     textenc_pattern.sub(lambda lowerCAmelCase_      : protected[re.escape(m.group(0      )      )]       ,       lowerCAmelCase_      )
														__lowercase      :							List[Any]				=     torch.cat(lowerCAmelCase_      )

							for k_pre, tensors in capture_qkv_bias.items():
														if None in tensors:
																					raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""      )
														__lowercase      :							Union[str, Any]				=     textenc_pattern.sub(lambda lowerCAmelCase_      : protected[re.escape(m.group(0      )      )]       ,       lowerCAmelCase_      )
														__lowercase      :							str				=     torch.cat(lowerCAmelCase_      )

							return new_state_dict
def       snake_case_		(     lowerCAmelCase_       :						Union[str, Any]      ):
							return text_enc_dict


if __name__ == "__main__":
	lowerCamelCase : Union[str, Any]						   =       argparse.ArgumentParser()

	parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
	parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
	parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
	parser.add_argument(
	    '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
	)

	lowerCamelCase : List[Any]						   =       parser.parse_args()

	assert args.model_path is not None, "Must provide a model path!"

	assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

	# Path for safetensors
	lowerCamelCase : Dict						   =       osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
	lowerCamelCase : Tuple						   =       osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
	lowerCamelCase : List[str]						   =       osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

	# Load models from safetensors if it exists, if it doesn't pytorch
	if osp.exists(unet_path):
		lowerCamelCase : Any						   =       load_file(unet_path, device='''cpu''')
	else:
		lowerCamelCase : Optional[Any]						   =       osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
		lowerCamelCase : List[Any]						   =       torch.load(unet_path, map_location='''cpu''')

	if osp.exists(vae_path):
		lowerCamelCase : List[str]						   =       load_file(vae_path, device='''cpu''')
	else:
		lowerCamelCase : Tuple						   =       osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
		lowerCamelCase : str						   =       torch.load(vae_path, map_location='''cpu''')

	if osp.exists(text_enc_path):
		lowerCamelCase : Optional[Any]						   =       load_file(text_enc_path, device='''cpu''')
	else:
		lowerCamelCase : Any						   =       osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
		lowerCamelCase : Dict						   =       torch.load(text_enc_path, map_location='''cpu''')

	# Convert the UNet model
	lowerCamelCase : Dict						   =       convert_unet_state_dict(unet_state_dict)
	lowerCamelCase : Optional[int]						   =       {'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

	# Convert the VAE model
	lowerCamelCase : Optional[int]						   =       convert_vae_state_dict(vae_state_dict)
	lowerCamelCase : Optional[Any]						   =       {'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

	# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
	lowerCamelCase : Union[str, Any]						   =       '''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

	if is_vaa_model:
		# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
		lowerCamelCase : int						   =       {'''transformer.''' + k: v for k, v in text_enc_dict.items()}
		lowerCamelCase : Tuple						   =       convert_text_enc_state_dict_vaa(text_enc_dict)
		lowerCamelCase : str						   =       {'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
	else:
		lowerCamelCase : Union[str, Any]						   =       convert_text_enc_state_dict(text_enc_dict)
		lowerCamelCase : Optional[int]						   =       {'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

	# Put together new checkpoint
	lowerCamelCase : List[Any]						   =       {**unet_state_dict, **vae_state_dict, **text_enc_dict}
	if args.half:
		lowerCamelCase : Union[str, Any]						   =       {k: v.half() for k, v in state_dict.items()}

	if args.use_safetensors:
		save_file(state_dict, args.checkpoint_path)
	else:
		lowerCamelCase : List[str]						   =       {'''state_dict''': state_dict}
		torch.save(state_dict, args.checkpoint_path)







import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
	import tensorflow as tf

if is_torch_available():
	import torch


lowerCamelCase : List[Any]						   =       logging.get_logger(__name__)

class 		lowerCAmelCase     :

					'''simple docstring'''

					def __init__(  self				:	List[str]		,     __a				:	str = None		,     __a				:	uuid.UUID = None		,     __a				:	Any=None		,     __a				:	List[Any]=None							)			->  List[Any]:


												"""simple docstring"""

												if not conversation_id:
																			__lowercase      :							Any				=     uuid.uuida()
												if past_user_inputs is None:
																			__lowercase      :							Dict				=     []
												if generated_responses is None:
																			__lowercase      :							Dict				=     []

												__lowercase      :							uuid.UUID				=     conversation_id
												__lowercase      :							List[str]				=     past_user_inputs
												__lowercase      :							List[str]				=     generated_responses
												__lowercase      :							Optional[str]				=     text



					def __eq__(  self				:	Dict		,     __a				:	Dict							)			->  Any:


												"""simple docstring"""

												if not isinstance(__a		,     __a							):
																			return False
												if self.uuid == other.uuid:
																			return True
												return (
												    self.new_user_input == other.new_user_input
												    and self.past_user_inputs == other.past_user_inputs
												    and self.generated_responses == other.generated_responses
												)



					def       lowerCAmelCase				(  self				:	List[str]		,     __a				:	str		,     __a				:	bool = False							)			->  Dict:


												"""simple docstring"""

												if self.new_user_input:
																			if overwrite:
																										logger.warning(
																										    F"User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten "
																										    F"with: \"{text}\"."							)
																										__lowercase      :							Optional[int]				=     text
																			else:
																										logger.warning(
																										    F"User input added while unprocessed input was existing: \"{self.new_user_input}\" new input "
																										    F"ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"							)
												else:
																			__lowercase      :							Dict				=     text



					def       lowerCAmelCase				(  self				:	int							)			->  Optional[Any]:


												"""simple docstring"""

												if self.new_user_input:
																			self.past_user_inputs.append(self.new_user_input							)
												__lowercase      :							Dict				=     None



					def       lowerCAmelCase				(  self				:	Optional[int]		,     __a				:	str							)			->  List[Any]:


												"""simple docstring"""

												self.generated_responses.append(__a							)



					def       lowerCAmelCase				(  self				:	Any							)			->  Optional[Any]:


												"""simple docstring"""

												for user_input, generated_response in zip(self.past_user_inputs		,     self.generated_responses							):
																			yield True, user_input
																			yield False, generated_response
												if self.new_user_input:
																			yield True, self.new_user_input



					def __repr__(  self				:	int							)			->  str:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     F"Conversation id: {self.uuid} \n"
												for is_user, text in self.iter_texts():
																			__lowercase      :							Optional[Any]				=     """user""" if is_user else """bot"""
																			output += F"{name} >> {text} \n"
												return output







@add_end_docstrings(
    __a      ,       r'''
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    '''      ,       )
class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	Any		,     *__a				:	int		,     **__a				:	str							)			->  Union[str, Any]:


												"""simple docstring"""

												super().__init__(*__a		,     **__a							)
												if self.tokenizer.pad_token_id is None:
																			__lowercase      :							List[Any]				=     self.tokenizer.eos_token



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	int=None		,     __a				:	Tuple=None		,     __a				:	Any=None		,     **__a				:	List[str]							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     {}
												__lowercase      :							Tuple				=     {}
												__lowercase      :							List[str]				=     {}

												if min_length_for_response is not None:
																			__lowercase      :							Dict				=     min_length_for_response
												if minimum_tokens is not None:
																			__lowercase      :							Union[str, Any]				=     minimum_tokens

												if "max_length" in generate_kwargs:
																			__lowercase      :							Union[str, Any]				=     generate_kwargs["""max_length"""]
																			# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
												if clean_up_tokenization_spaces is not None:
																			__lowercase      :							Union[str, Any]				=     clean_up_tokenization_spaces

												if generate_kwargs:
																			forward_params.update(__a							)
												return preprocess_params, forward_params, postprocess_params



					def __call__(  self				:	Optional[int]		,     __a				:	Union[Conversation, List[Conversation]]		,     __a				:	Dict=0		,     **__a				:	List[Any]							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     super().__call__(__a		,     num_workers=__a		,     **__a							)
												if isinstance(__a		,     __a							) and len(__a							) == 1:
																			return outputs[0]
												return outputs



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	Conversation		,     __a				:	Tuple=32							)			->  Dict[str, Any]:


												"""simple docstring"""

												if not isinstance(__a		,     __a							):
																			raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""							)
												if conversation.new_user_input is None:
																			raise ValueError(
																			    F"Conversation with UUID {type(conversation.uuid							)} does not contain new user input to process. "
																			    """Add user inputs with the conversation's `add_user_input` method"""							)
												if hasattr(self.tokenizer		,     """_build_conversation_input_ids"""							):
																			__lowercase      :							List[Any]				=     self.tokenizer._build_conversation_input_ids(__a							)
												else:
																			# If the tokenizer cannot handle conversations, we default to only the old version
																			__lowercase      :							Tuple				=     self._legacy_parse_and_tokenize(__a							)

												if self.framework == "pt":
																			__lowercase      :							List[Any]				=     torch.LongTensor([input_ids]							)
												elif self.framework == "tf":
																			__lowercase      :							List[str]				=     tf.constant([input_ids]							)
												return {"input_ids": input_ids, "conversation": conversation}



					def       lowerCAmelCase				(  self				:	Any		,     __a				:	Dict		,     __a				:	Any=10		,     **__a				:	Dict							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     generate_kwargs.get("""max_length"""		,     self.model.config.max_length							)

												__lowercase      :							List[Any]				=     model_inputs["""input_ids"""].shape[1]
												if max_length - minimum_tokens < n:
																			logger.warning(F"Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"							)
																			__lowercase      :							Any				=     max_length - minimum_tokens
																			__lowercase      :							int				=     model_inputs["""input_ids"""][:, -trim:]
																			if "attention_mask" in model_inputs:
																										__lowercase      :							Dict				=     model_inputs["""attention_mask"""][:, -trim:]
												__lowercase      :							Union[str, Any]				=     model_inputs.pop("""conversation"""							)
												__lowercase      :							Tuple				=     max_length
												__lowercase      :							int				=     self.model.generate(**__a		,     **__a							)
												if self.model.config.is_encoder_decoder:
																			__lowercase      :							Optional[int]				=     1
												else:
																			__lowercase      :							str				=     n
												return {"output_ids": output_ids[:, start_position:], "conversation": conversation}



					def       lowerCAmelCase				(  self				:	int		,     __a				:	Tuple		,     __a				:	List[Any]=True							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							int				=     model_outputs["""output_ids"""]
												__lowercase      :							Union[str, Any]				=     self.tokenizer.decode(
												    output_ids[0]		,     skip_special_tokens=__a		,     clean_up_tokenization_spaces=__a		,     )
												__lowercase      :							List[str]				=     model_outputs["""conversation"""]
												conversation.mark_processed()
												conversation.append_response(__a							)
												return conversation



					def       lowerCAmelCase				(  self				:	int		,     __a				:	Conversation							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     self.tokenizer.eos_token_id
												__lowercase      :							Optional[Any]				=     []
												for is_user, text in conversation.iter_texts():
																			if eos_token_id is not None:
																										input_ids.extend(self.tokenizer.encode(__a		,     add_special_tokens=__a							) + [eos_token_id]							)
																			else:
																										input_ids.extend(self.tokenizer.encode(__a		,     add_special_tokens=__a							)							)

												if len(__a							) > self.tokenizer.model_max_length:
																			__lowercase      :							List[Any]				=     input_ids[-self.tokenizer.model_max_length :]
												return input_ids