



'''simple docstring'''

import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible

class      __magic_name__						(   unittest.TestCase       ):







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Tuple				=							[
									    'safety_checker/pytorch_model.bin',
									    'safety_checker/model.safetensors',
									    'vae/diffusion_pytorch_model.bin',
									    'vae/diffusion_pytorch_model.safetensors',
									    'text_encoder/pytorch_model.bin',
									    'text_encoder/model.safetensors',
									    'unet/diffusion_pytorch_model.bin',
									    'unet/diffusion_pytorch_model.safetensors',
									]
									self.assertTrue(is_safetensors_compatible(_UpperCAmelCase		)		)







							def 							__lowercase      (							self :	Any		):
									_a					:				Any				=							[
									    'unet/diffusion_pytorch_model.bin',
									    'unet/diffusion_pytorch_model.safetensors',
									]
									self.assertTrue(is_safetensors_compatible(_UpperCAmelCase		)		)







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				List[Any]				=							[
									    'safety_checker/pytorch_model.bin',
									    'safety_checker/model.safetensors',
									    'vae/diffusion_pytorch_model.bin',
									    'vae/diffusion_pytorch_model.safetensors',
									    'text_encoder/pytorch_model.bin',
									    'text_encoder/model.safetensors',
									    'unet/diffusion_pytorch_model.bin',
									    # Removed: 'unet/diffusion_pytorch_model.safetensors',
									]
									self.assertFalse(is_safetensors_compatible(_UpperCAmelCase		)		)







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Dict				=							[
									    'text_encoder/pytorch_model.bin',
									    'text_encoder/model.safetensors',
									]
									self.assertTrue(is_safetensors_compatible(_UpperCAmelCase		)		)







							def 							__lowercase      (							self :	Dict		):
									_a					:				Tuple				=							[
									    'safety_checker/pytorch_model.bin',
									    'safety_checker/model.safetensors',
									    'vae/diffusion_pytorch_model.bin',
									    'vae/diffusion_pytorch_model.safetensors',
									    'text_encoder/pytorch_model.bin',
									    # Removed: 'text_encoder/model.safetensors',
									    'unet/diffusion_pytorch_model.bin',
									    'unet/diffusion_pytorch_model.safetensors',
									]
									self.assertFalse(is_safetensors_compatible(_UpperCAmelCase		)		)







							def 							__lowercase      (							self :	Tuple		):
									_a					:				Union[str, Any]				=							[
									    'safety_checker/pytorch_model.fp16.bin',
									    'safety_checker/model.fp16.safetensors',
									    'vae/diffusion_pytorch_model.fp16.bin',
									    'vae/diffusion_pytorch_model.fp16.safetensors',
									    'text_encoder/pytorch_model.fp16.bin',
									    'text_encoder/model.fp16.safetensors',
									    'unet/diffusion_pytorch_model.fp16.bin',
									    'unet/diffusion_pytorch_model.fp16.safetensors',
									]
									_a					:				int				=							'fp16'
									self.assertTrue(is_safetensors_compatible(_UpperCAmelCase     ,variant=_UpperCAmelCase		)		)







							def 							__lowercase      (							self :	Tuple		):
									_a					:				Optional[Any]				=							[
									    'unet/diffusion_pytorch_model.fp16.bin',
									    'unet/diffusion_pytorch_model.fp16.safetensors',
									]
									_a					:				Optional[int]				=							'fp16'
									self.assertTrue(is_safetensors_compatible(_UpperCAmelCase     ,variant=_UpperCAmelCase		)		)







							def 							__lowercase      (							self :	str		):
									# pass variant but use the non-variant filenames
									_a					:				Tuple				=							[
									    'unet/diffusion_pytorch_model.bin',
									    'unet/diffusion_pytorch_model.safetensors',
									]
									_a					:				Union[str, Any]				=							'fp16'
									self.assertTrue(is_safetensors_compatible(_UpperCAmelCase     ,variant=_UpperCAmelCase		)		)







							def 							__lowercase      (							self :	Tuple		):
									_a					:				List[str]				=							[
									    'safety_checker/pytorch_model.fp16.bin',
									    'safety_checker/model.fp16.safetensors',
									    'vae/diffusion_pytorch_model.fp16.bin',
									    'vae/diffusion_pytorch_model.fp16.safetensors',
									    'text_encoder/pytorch_model.fp16.bin',
									    'text_encoder/model.fp16.safetensors',
									    'unet/diffusion_pytorch_model.fp16.bin',
									    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
									]
									_a					:				List[Any]				=							'fp16'
									self.assertFalse(is_safetensors_compatible(_UpperCAmelCase     ,variant=_UpperCAmelCase		)		)







							def 							__lowercase      (							self :	str		):
									_a					:				int				=							[
									    'text_encoder/pytorch_model.fp16.bin',
									    'text_encoder/model.fp16.safetensors',
									]
									_a					:				Dict				=							'fp16'
									self.assertTrue(is_safetensors_compatible(_UpperCAmelCase     ,variant=_UpperCAmelCase		)		)







							def 							__lowercase      (							self :	str		):
									# pass variant but use the non-variant filenames
									_a					:				List[Any]				=							[
									    'text_encoder/pytorch_model.bin',
									    'text_encoder/model.safetensors',
									]
									_a					:				Optional[int]				=							'fp16'
									self.assertTrue(is_safetensors_compatible(_UpperCAmelCase     ,variant=_UpperCAmelCase		)		)







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Optional[Any]				=							[
									    'safety_checker/pytorch_model.fp16.bin',
									    'safety_checker/model.fp16.safetensors',
									    'vae/diffusion_pytorch_model.fp16.bin',
									    'vae/diffusion_pytorch_model.fp16.safetensors',
									    'text_encoder/pytorch_model.fp16.bin',
									    # 'text_encoder/model.fp16.safetensors',
									    'unet/diffusion_pytorch_model.fp16.bin',
									    'unet/diffusion_pytorch_model.fp16.safetensors',
									]
									_a					:				Optional[int]				=							'fp16'
									self.assertFalse(is_safetensors_compatible(_UpperCAmelCase     ,variant=_UpperCAmelCase		)		)









'''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

__lowerCAmelCase						      =       logging.get_logger(__name__)

class      __magic_name__						(   enum.Enum       ):
							lowerCAmelCase	:   Union[str, Any]           = 0
							lowerCAmelCase	:   Dict           = 1

@add_end_docstrings(_UpperCamelCase       )
class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   int           = 'generated'







							def __init__(							self :	List[Any]     ,*_UpperCAmelCase :	Any     ,**_UpperCAmelCase :	Optional[int]		):
									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 							__lowercase      (							self :	Optional[int]     ,_UpperCAmelCase :	List[Any]=None     ,_UpperCAmelCase :	Tuple=None     ,_UpperCAmelCase :	List[Any]=None     ,_UpperCAmelCase :	Union[str, Any]=None     ,_UpperCAmelCase :	Dict=None     ,_UpperCAmelCase :	Optional[int]=None     ,**_UpperCAmelCase :	List[Any]     ,):
									_a					:				Tuple				=							{}
									if truncation is not None:
											_a					:				Union[str, Any]				=							truncation

									_a					:				List[str]				=							generate_kwargs

									_a					:				Optional[Any]				=							{}
									if return_tensors is not None and return_type is None:
											_a					:				Optional[int]				=							ReturnType.TENSORS if return_tensors else ReturnType.TEXT
									if return_type is not None:
											_a					:				List[Any]				=							return_type

									if clean_up_tokenization_spaces is not None:
											_a					:				Optional[int]				=							clean_up_tokenization_spaces

									if stop_sequence is not None:
											_a					:				List[str]				=							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					:				List[Any]				=							stop_sequence_ids[0]

									return preprocess_params, forward_params, postprocess_params







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int		):
									return True







							def 							__lowercase      (							self :	str     ,*_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Union[str, Any]		):
									_a					:				Optional[Any]				=							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					:				Optional[Any]				=							([prefix + arg for arg in args[0]],)
											_a					:				Any				=							True

									elif isinstance(args[0]     ,_UpperCAmelCase		):
											_a					:				List[str]				=							(prefix + args[0],)
											_a					:				str				=							False
									else:
											raise ValueError(
											    F""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""		)
									_a					:				Optional[Any]				=							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 :	Optional[int]     ,*_UpperCAmelCase :	List[Any]     ,**_UpperCAmelCase :	Any		):
									_a					:				Tuple				=							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 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	List[str]=TruncationStrategy.DO_NOT_TRUNCATE     ,**_UpperCAmelCase :	int		):
									_a					:				Dict				=							self._parse_and_tokenize(_UpperCAmelCase     ,truncation=_UpperCAmelCase     ,**_UpperCAmelCase		)
									return inputs







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	str     ,**_UpperCAmelCase :	Tuple		):
									if self.framework == "pt":
											_a	, _a					:				int				=							model_inputs['input_ids'].shape
									elif self.framework == "tf":
											_a	, _a					:				Dict				=							tf.shape(model_inputs['input_ids']		).numpy()

									_a					:				Optional[Any]				=							generate_kwargs.get('min_length'     ,self.model.config.min_length		)
									_a					:				Optional[int]				=							generate_kwargs.get('max_length'     ,self.model.config.max_length		)
									self.check_inputs(_UpperCAmelCase     ,generate_kwargs['min_length']     ,generate_kwargs['max_length']		)
									_a					:				List[str]				=							self.model.generate(**_UpperCAmelCase     ,**_UpperCAmelCase		)
									_a					:				int				=							output_ids.shape[0]
									if self.framework == "pt":
											_a					:				int				=							output_ids.reshape(_UpperCAmelCase     ,out_b // in_b     ,*output_ids.shape[1:]		)
									elif self.framework == "tf":
											_a					:				Any				=							tf.reshape(_UpperCAmelCase     ,(in_b, out_b // in_b, *output_ids.shape[1:])		)
									return {"output_ids": output_ids}







							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	Any=ReturnType.TEXT     ,_UpperCAmelCase :	Dict=False		):
									_a					:				Union[str, Any]				=							[]
									for output_ids in model_outputs["output_ids"][0]:
											if return_type == ReturnType.TENSORS:
													_a					:				int				=							{F"""{self.return_name}_token_ids""": output_ids}
											elif return_type == ReturnType.TEXT:
													_a					:				str				=							{
													    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      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Any           = 'summary'







							def __call__(							self :	Any     ,*_UpperCAmelCase :	Union[str, Any]     ,**_UpperCAmelCase :	List[str]		):
									return super().__call__(*_UpperCAmelCase     ,**_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int		):
									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      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   List[Any]           = 'translation'







							def 							__lowercase      (							self :	Optional[int]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int		):
									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 							__lowercase      (							self :	Any     ,*_UpperCAmelCase :	Any     ,_UpperCAmelCase :	List[str]=TruncationStrategy.DO_NOT_TRUNCATE     ,_UpperCAmelCase :	Optional[int]=None     ,_UpperCAmelCase :	Any=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 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	List[Any]=None     ,_UpperCAmelCase :	List[str]=None     ,**_UpperCAmelCase :	Dict		):
									_a	, _a	, _a					:				str				=							super()._sanitize_parameters(**_UpperCAmelCase		)
									if src_lang is not None:
											_a					:				Optional[int]				=							src_lang
									if tgt_lang is not None:
											_a					:				List[Any]				=							tgt_lang
									if src_lang is None and tgt_lang is None:
											# Backward compatibility, direct arguments use is preferred.
											_a					:				int				=							kwargs.get('task'     ,self.task		)
											_a					:				int				=							task.split('_'		)
											if task and len(_UpperCAmelCase		) == 4:
													# translation, XX, to YY
													_a					:				List[Any]				=							items[1]
													_a					:				Any				=							items[3]
									return preprocess_params, forward_params, postprocess_params







							def __call__(							self :	Optional[Any]     ,*_UpperCAmelCase :	Tuple     ,**_UpperCAmelCase :	Optional[int]		):
									return super().__call__(*_UpperCAmelCase     ,**_UpperCAmelCase		)




