
'''simple docstring'''
import enum
import warnings

from ..tokenization_utils import TruncationStrategy
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

		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

lowercase__											=logging.get_logger(__name__)







class   a_ (      enum.Enum					):
			lowerCamelCase__	:			str       =    0
			lowerCamelCase__	:			str       =    1







@add_end_docstrings(UpperCamelCase__					)
class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			Any       =    'generated'




			def __init__(       self					,   *UpperCAmelCase					,   **UpperCAmelCase	):
				super().__init__(*UpperCAmelCase					,   **UpperCAmelCase	)

				self.check_model_type(
				    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				    if self.framework == """tf"""
				    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=None					,   UpperCAmelCase=None					,   UpperCAmelCase=None					,   UpperCAmelCase=None					,   UpperCAmelCase=None					,   UpperCAmelCase=None					,   **UpperCAmelCase					,   ):
				a_      				=						{}
				if truncation is not None:
					a_      				=						truncation

				a_      				=						generate_kwargs

				a_      				=						{}
				if return_tensors is not None and return_type is None:
					a_      				=						ReturnType.TENSORS if return_tensors else ReturnType.TEXT
				if return_type is not None:
					a_      				=						return_type

				if clean_up_tokenization_spaces is not None:
					a_      				=						clean_up_tokenization_spaces

				if stop_sequence is not None:
					a_      				=						self.tokenizer.encode(UpperCAmelCase					,   add_special_tokens=UpperCAmelCase	)
					if len(UpperCAmelCase	) > 1:
						warnings.warn(
						    """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
						    """ the stop sequence will be used as the stop sequence string in the interim."""	)
					a_      				=						stop_sequence_ids[0]

				return preprocess_params, forward_params, postprocess_params




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				return True




			def 					lowerCAmelCase__					(       self					,   *UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						self.model.config.prefix if self.model.config.prefix is not None else """"""
				if isinstance(args[0]					,   UpperCAmelCase	):
					if self.tokenizer.pad_token_id is None:
						raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input"""	)
					a_      				=						([prefix + arg for arg in args[0]],)
					a_      				=						True

				elif isinstance(args[0]					,   UpperCAmelCase	):
					a_      				=						(prefix + args[0],)
					a_      				=						False
				else:
					raise ValueError(
					    f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''	)
				a_      				=						self.tokenizer(*UpperCAmelCase					,   padding=UpperCAmelCase					,   truncation=UpperCAmelCase					,   return_tensors=self.framework	)
				# This is produced by tokenizers but is an invalid generate kwargs
				if "token_type_ids" in inputs:
					del inputs["token_type_ids"]
				return inputs




			def __call__(       self					,   *UpperCAmelCase					,   **UpperCAmelCase	):
				a_      				=						super().__call__(*UpperCAmelCase					,   **UpperCAmelCase	)
				if (
				    isinstance(args[0]					,   UpperCAmelCase	)
				    and all(isinstance(UpperCAmelCase					,   UpperCAmelCase	) for el in args[0]	)
				    and all(len(UpperCAmelCase	) == 1 for res in result	)
				):
					return [res[0] for res in result]
				return result




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase=TruncationStrategy.DO_NOT_TRUNCATE					,   **UpperCAmelCase	):
				a_      				=						self._parse_and_tokenize(UpperCAmelCase					,   truncation=UpperCAmelCase					,   **UpperCAmelCase	)
				return inputs




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   **UpperCAmelCase	):
				if self.framework == "pt":
					a_				,  a_      				=						model_inputs["""input_ids"""].shape
				elif self.framework == "tf":
					a_				,  a_      				=						tf.shape(model_inputs["""input_ids"""]	).numpy()

				a_      				=						generate_kwargs.get("""min_length"""					,   self.model.config.min_length	)
				a_      				=						generate_kwargs.get("""max_length"""					,   self.model.config.max_length	)
				self.check_inputs(UpperCAmelCase					,   generate_kwargs["""min_length"""]					,   generate_kwargs["""max_length"""]	)
				a_      				=						self.model.generate(**UpperCAmelCase					,   **UpperCAmelCase	)
				a_      				=						output_ids.shape[0]
				if self.framework == "pt":
					a_      				=						output_ids.reshape(UpperCAmelCase					,   out_b // in_b					,   *output_ids.shape[1:]	)
				elif self.framework == "tf":
					a_      				=						tf.reshape(UpperCAmelCase					,   (in_b, out_b // in_b, *output_ids.shape[1:])	)
				return {"output_ids": output_ids}






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase=ReturnType.TEXT					,   UpperCAmelCase=False	):
				a_      				=						[]
				for output_ids in model_outputs["output_ids"][0]:
					if return_type == ReturnType.TENSORS:
						a_      				=						{f'''{self.return_name}_token_ids''': output_ids}
					elif return_type == ReturnType.TEXT:
						a_      				=						{
						    f'''{self.return_name}_text''': self.tokenizer.decode(
						        UpperCAmelCase					,   skip_special_tokens=UpperCAmelCase					,   clean_up_tokenization_spaces=UpperCAmelCase					,   )
						}
					records.append(UpperCAmelCase	)
				return records







@add_end_docstrings(UpperCamelCase__					)
class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			Optional[int]       =    'summary'




			def __call__(       self					,   *UpperCAmelCase					,   **UpperCAmelCase	):
				return super().__call__(*UpperCAmelCase					,   **UpperCAmelCase	)






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				if max_length < min_length:
					logger.warning(f'''Your min_length={min_length} must be inferior than your max_length={max_length}.'''	)

				if input_length < max_length:
					logger.warning(
					    f'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
					    """a summarization task, where outputs shorter than the input are typically wanted, you might """
					    f'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'''	)







@add_end_docstrings(UpperCamelCase__					)
class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			Dict       =    'translation'




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				if input_length > 0.9 * max_length:
					logger.warning(
					    f'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
					    """increasing your max_length manually, e.g. translator('...', max_length=400)"""	)
				return True




			def 					lowerCAmelCase__					(       self					,   *UpperCAmelCase					,   UpperCAmelCase=TruncationStrategy.DO_NOT_TRUNCATE					,   UpperCAmelCase=None					,   UpperCAmelCase=None	):
				if getattr(self.tokenizer					,   """_build_translation_inputs"""					,   UpperCAmelCase	):
					return self.tokenizer._build_translation_inputs(
					    *UpperCAmelCase					,   return_tensors=self.framework					,   truncation=UpperCAmelCase					,   src_lang=UpperCAmelCase					,   tgt_lang=UpperCAmelCase	)
				else:
					return super()._parse_and_tokenize(*UpperCAmelCase					,   truncation=UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=None					,   UpperCAmelCase=None					,   **UpperCAmelCase	):
				a_				,  a_				,  a_      				=						super()._sanitize_parameters(**UpperCAmelCase	)
				if src_lang is not None:
					a_      				=						src_lang
				if tgt_lang is not None:
					a_      				=						tgt_lang
				if src_lang is None and tgt_lang is None:
					# Backward compatibility, direct arguments use is preferred.
					a_      				=						kwargs.get("""task"""					,   self.task	)
					a_      				=						task.split("""_"""	)
					if task and len(UpperCAmelCase	) == 4:
						# translation, XX, to YY
						a_      				=						items[1]
						a_      				=						items[3]
				return preprocess_params, forward_params, postprocess_params






			def __call__(       self					,   *UpperCAmelCase					,   **UpperCAmelCase	):
				return super().__call__(*UpperCAmelCase					,   **UpperCAmelCase	)


'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

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

		parser.add_argument(
		    '--txt2img_unclip',
		    default='kakaobrain/karlo-v1-alpha',
		    type=str,
		    required=False,
		    help='The pretrained txt2img unclip.',
		)

		lowercase__											=parser.parse_args()

		lowercase__											=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase__											=CLIPImageProcessor()
		lowercase__											=CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase__											=UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)
