




"""simple docstring"""







import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
		import tensorflow as tf






class       SCREAMING_SNAKE_CASE__					(					enum.Enum					):
						_a    						=						0
						_a    						=						1
						_a    						=						2






@add_end_docstrings(_a					)
class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						'\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    '


						def __init__(							self				:			Any						,						*lowerCAmelCase				:			Optional[int]						,						**lowerCAmelCase				:			Any					):
										super().__init__(*lowerCAmelCase						,						**lowerCAmelCase					)
										self.check_model_type(
										    TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == """tf""" else MODEL_FOR_CAUSAL_LM_MAPPING					)
										if "prefix" not in self._preprocess_params:
														# This is very specific. The logic is quite complex and needs to be done
														# as a "default".
														# It also defines both some preprocess_kwargs and generate_kwargs
														# which is why we cannot put them in their respective methods.
														lowerCAmelCase  =				None
														if self.model.config.prefix is not None:
																		lowerCAmelCase  =				self.model.config.prefix
														if prefix is None and self.model.__class__.__name__ in [
														    "XLNetLMHeadModel",
														    "TransfoXLLMHeadModel",
														    "TFXLNetLMHeadModel",
														    "TFTransfoXLLMHeadModel",
														]:
																		# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
																		lowerCAmelCase  =				self.XL_PREFIX
														if prefix is not None:
																		# Recalculate some generate_kwargs linked to prefix.
																		lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				self._sanitize_parameters(prefix=lowerCAmelCase						,						**self._forward_params					)
																		lowerCAmelCase  =				{**self._preprocess_params, **preprocess_params}
																		lowerCAmelCase  =				{**self._forward_params, **forward_params}


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			Any=None						,						lowerCAmelCase				:			Tuple=None						,						lowerCAmelCase				:			Union[str, Any]=None						,						lowerCAmelCase				:			List[str]=None						,						lowerCAmelCase				:			int=None						,						lowerCAmelCase				:			Optional[Any]=None						,						lowerCAmelCase				:			Optional[int]=None						,						lowerCAmelCase				:			int=None						,						**lowerCAmelCase				:			Tuple						,						):
										lowerCAmelCase  =				{}
										if prefix is not None:
														lowerCAmelCase  =				prefix
										if prefix:
														lowerCAmelCase  =				self.tokenizer(
														    lowerCAmelCase						,						padding=lowerCAmelCase						,						add_special_tokens=lowerCAmelCase						,						return_tensors=self.framework					)
														lowerCAmelCase  =				prefix_inputs["""input_ids"""].shape[-1]

										if handle_long_generation is not None:
														if handle_long_generation not in {"hole"}:
																		raise ValueError(
																		    f'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
																		    """ [None, 'hole']"""					)
														lowerCAmelCase  =				handle_long_generation

										preprocess_params.update(lowerCAmelCase					)
										lowerCAmelCase  =				generate_kwargs

										lowerCAmelCase  =				{}
										if return_full_text is not None and return_type is None:
														if return_text is not None:
																		raise ValueError("""`return_text` is mutually exclusive with `return_full_text`"""					)
														if return_tensors is not None:
																		raise ValueError("""`return_full_text` is mutually exclusive with `return_tensors`"""					)
														lowerCAmelCase  =				ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
										if return_tensors is not None and return_type is None:
														if return_text is not None:
																		raise ValueError("""`return_text` is mutually exclusive with `return_tensors`"""					)
														lowerCAmelCase  =				ReturnType.TENSORS
										if return_type is not None:
														lowerCAmelCase  =				return_type
										if clean_up_tokenization_spaces is not None:
														lowerCAmelCase  =				clean_up_tokenization_spaces

										if stop_sequence is not None:
														lowerCAmelCase  =				self.tokenizer.encode(lowerCAmelCase						,						add_special_tokens=lowerCAmelCase					)
														if len(lowerCAmelCase					) > 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."""					)
														lowerCAmelCase  =				stop_sequence_ids[0]

										return preprocess_params, forward_params, postprocess_params


						def   __lowercase (							self				:			Optional[int]						,						*lowerCAmelCase				:			Tuple						,						**lowerCAmelCase				:			Dict					):
										# Parse arguments
										if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
														kwargs.update({"""add_space_before_punct_symbol""": True}					)

										return super()._parse_and_tokenize(*lowerCAmelCase						,						**lowerCAmelCase					)


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


						def   __lowercase (							self				:			List[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int=""						,						lowerCAmelCase				:			List[str]=None						,						**lowerCAmelCase				:			Optional[Any]					):
										lowerCAmelCase  =				self.tokenizer(
										    prefix + prompt_text						,						padding=lowerCAmelCase						,						add_special_tokens=lowerCAmelCase						,						return_tensors=self.framework					)
										lowerCAmelCase  =				prompt_text

										if handle_long_generation == "hole":
														lowerCAmelCase  =				inputs["""input_ids"""].shape[-1]
														if "max_new_tokens" in generate_kwargs:
																		lowerCAmelCase  =				generate_kwargs["""max_new_tokens"""]
														else:
																		lowerCAmelCase  =				generate_kwargs.get("""max_length"""						,						self.model.config.max_length					) - cur_len
																		if new_tokens < 0:
																						raise ValueError("""We cannot infer how many new tokens are expected"""					)
														if cur_len + new_tokens > self.tokenizer.model_max_length:
																		lowerCAmelCase  =				self.tokenizer.model_max_length - new_tokens
																		if keep_length <= 0:
																						raise ValueError(
																						    """We cannot use `hole` to handle this generation the number of desired tokens exceeds the"""
																						    """ models max length"""					)

																		lowerCAmelCase  =				inputs["""input_ids"""][:, -keep_length:]
																		if "attention_mask" in inputs:
																						lowerCAmelCase  =				inputs["""attention_mask"""][:, -keep_length:]

										return inputs


						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			Optional[int]						,						**lowerCAmelCase				:			Union[str, Any]					):
										lowerCAmelCase  =				model_inputs["""input_ids"""]
										lowerCAmelCase  =				model_inputs.get("""attention_mask"""						,						lowerCAmelCase					)
										# Allow empty prompts
										if input_ids.shape[1] == 0:
														lowerCAmelCase  =				None
														lowerCAmelCase  =				None
														lowerCAmelCase  =				1
										else:
														lowerCAmelCase  =				input_ids.shape[0]
										lowerCAmelCase  =				model_inputs.pop("""prompt_text"""					)

										# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
										# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
										lowerCAmelCase  =				generate_kwargs.pop("""prefix_length"""						,						0					)
										if prefix_length > 0:
														lowerCAmelCase  =				"""max_new_tokens""" in generate_kwargs or (
														    """generation_config""" in generate_kwargs
														    and generate_kwargs["""generation_config"""].max_new_tokens is not None
														)
														if not has_max_new_tokens:
																		lowerCAmelCase  =				generate_kwargs.get("""max_length"""					) or self.model.config.max_length
																		generate_kwargs["max_length"] += prefix_length
														lowerCAmelCase  =				"""min_new_tokens""" in generate_kwargs or (
														    """generation_config""" in generate_kwargs
														    and generate_kwargs["""generation_config"""].min_new_tokens is not None
														)
														if not has_min_new_tokens and "min_length" in generate_kwargs:
																		generate_kwargs["min_length"] += prefix_length

        # BS x SL
										lowerCAmelCase  =				self.model.generate(input_ids=lowerCAmelCase						,						attention_mask=lowerCAmelCase						,						**lowerCAmelCase					)
										lowerCAmelCase  =				generated_sequence.shape[0]
										if self.framework == "pt":
														lowerCAmelCase  =				generated_sequence.reshape(lowerCAmelCase						,						out_b // in_b						,						*generated_sequence.shape[1:]					)
										elif self.framework == "tf":
														lowerCAmelCase  =				tf.reshape(lowerCAmelCase						,						(in_b, out_b // in_b, *generated_sequence.shape[1:])					)
										return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}




						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Optional[Any]=ReturnType.FULL_TEXT						,						lowerCAmelCase				:			Any=True					):
										lowerCAmelCase  =				model_outputs["""generated_sequence"""][0]
										lowerCAmelCase  =				model_outputs["""input_ids"""]
										lowerCAmelCase  =				model_outputs["""prompt_text"""]
										lowerCAmelCase  =				generated_sequence.numpy().tolist()
										lowerCAmelCase  =				[]
										for sequence in generated_sequence:
														if return_type == ReturnType.TENSORS:
																		lowerCAmelCase  =				{"""generated_token_ids""": sequence}
														elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
																		# Decode text
																		lowerCAmelCase  =				self.tokenizer.decode(
																		    lowerCAmelCase						,						skip_special_tokens=lowerCAmelCase						,						clean_up_tokenization_spaces=lowerCAmelCase						,						)

																		# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
																		if input_ids is None:
																						lowerCAmelCase  =				0
																		else:
																						lowerCAmelCase  =				len(
																						    self.tokenizer.decode(
																						        input_ids[0]						,						skip_special_tokens=lowerCAmelCase						,						clean_up_tokenization_spaces=lowerCAmelCase						,						)					)

																		if return_type == ReturnType.FULL_TEXT:
																						lowerCAmelCase  =				prompt_text + text[prompt_length:]
																		else:
																						lowerCAmelCase  =				text[prompt_length:]

																		lowerCAmelCase  =				{"""generated_text""": all_text}
														records.append(lowerCAmelCase					)

										return records











"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						@property
						def   __lowercase (							self				:			Any					):
										torch.manual_seed(0					)
										lowerCAmelCase  =				UNetaDModel(
										    block_out_channels=(32, 64)						,						layers_per_block=2						,						sample_size=32						,						in_channels=3						,						out_channels=3						,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,						)
										return model




						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				self.dummy_uncond_unet
										lowerCAmelCase  =				KarrasVeScheduler()

										lowerCAmelCase  =				KarrasVePipeline(unet=lowerCAmelCase						,						scheduler=lowerCAmelCase					)
										pipe.to(lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=2						,						generator=lowerCAmelCase						,						output_type="""numpy"""					).images

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=2						,						generator=lowerCAmelCase						,						output_type="""numpy"""						,						return_dict=lowerCAmelCase					)[0]

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										lowerCAmelCase  =				image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCAmelCase  =				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2






@slow
@require_torch
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				"""google/ncsnpp-celebahq-256"""
										lowerCAmelCase  =				UNetaDModel.from_pretrained(lowerCAmelCase					)
										lowerCAmelCase  =				KarrasVeScheduler()

										lowerCAmelCase  =				KarrasVePipeline(unet=lowerCAmelCase						,						scheduler=lowerCAmelCase					)
										pipe.to(lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=20						,						generator=lowerCAmelCase						,						output_type="""numpy"""					).images

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										assert image.shape == (1, 256, 256, 3)
										lowerCAmelCase  =				np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2





