



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							(DDPMScheduler,)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {
						    "num_train_timesteps": 1000,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "variance_type": "fixed_small",
						    "clip_sample": True,
						}

						config.update(**lowerCamelCase_		)
						return config




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]		,       [0.002, 0.02, 0.2, 2]		):
											self.check_over_configs(beta_start=lowerCamelCase_		,       beta_end=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for variance in ["fixed_small", "fixed_large", "other"]:
											self.check_over_configs(variance_type=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.check_over_configs(thresholding=lowerCamelCase_		)
						for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
																self.check_over_configs(
																    thresholding=lowerCamelCase_		,       prediction_type=lowerCamelCase_		,       sample_max_value=lowerCamelCase_		,       )




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for prediction_type in ["epsilon", "sample", "v_prediction"]:
											self.check_over_configs(prediction_type=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for t in [0, 500, 999]:
											self.check_over_forward(time_step=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_0979		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   len(lowerCamelCase_		)

						a					     =   self.dummy_model()
						a					     =   self.dummy_sample_deter
						a					     =   torch.manual_seed(0		)

						for t in reversed(range(lowerCamelCase_		)		):
											# 1. predict noise residual
											a					     =   model(lowerCamelCase_		,       lowerCamelCase_		)

											# 2. predict previous mean of sample x_t-1
											a					     =   scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       generator=lowerCamelCase_		).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											a					     =   pred_prev_sample

						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

						assert abs(result_sum.item() - 258.9606		) < 1E-2
						assert abs(result_mean.item() - 0.3372		) < 1E-3




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config(prediction_type="v_prediction"		)
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   len(lowerCamelCase_		)

						a					     =   self.dummy_model()
						a					     =   self.dummy_sample_deter
						a					     =   torch.manual_seed(0		)

						for t in reversed(range(lowerCamelCase_		)		):
											# 1. predict noise residual
											a					     =   model(lowerCamelCase_		,       lowerCamelCase_		)

											# 2. predict previous mean of sample x_t-1
											a					     =   scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       generator=lowerCamelCase_		).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											a					     =   pred_prev_sample

						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

						assert abs(result_sum.item() - 202.0296		) < 1E-2
						assert abs(result_mean.item() - 0.2631		) < 1E-3




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 1, 0]

						scheduler.set_timesteps(timesteps=lowerCamelCase_		)

						a					     =   scheduler.timesteps

						for i, timestep in enumerate(lowerCamelCase_		):
											if i == len(lowerCamelCase_		) - 1:
																a					     =   -1
											else:
																a					     =   timesteps[i + 1]

											a					     =   scheduler.previous_timestep(lowerCamelCase_		)
											a					     =   prev_t.item()

											self.assertEqual(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 51, 0]

						with self.assertRaises(lowerCamelCase_		,       msg="`custom_timesteps` must be in descending order."		):
											scheduler.set_timesteps(timesteps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 1, 0]
						a					     =   len(lowerCamelCase_		)

						with self.assertRaises(lowerCamelCase_		,       msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."		):
											scheduler.set_timesteps(num_inference_steps=lowerCamelCase_		,       timesteps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    lowerCamelCase_		,       msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"		,       ):
											scheduler.set_timesteps(timesteps=lowerCamelCase_		)





from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_tf_available():
				from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_lowercase: Union[str, Any]      			=		logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       *lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						super().__init__(*lowerCamelCase_		,       **lowerCamelCase_		)
						requires_backends(self		,       "vision"		)
						self.check_model_type(
						    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       lowerCamelCase_=None		):
						"""simple docstring"""

						a					     =   {}
						a					     =   {}

						if prompt is not None:
											a					     =   prompt

						if generate_kwargs is not None:
											a					     =   generate_kwargs
						if max_new_tokens is not None:
											if "generate_kwargs" not in forward_kwargs:
																a					     =   {}
											if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																raise ValueError(
																    "'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"
																    " please use only one"		)
											a					     =   max_new_tokens
						return preprocess_params, forward_kwargs, {}




	def __call__(self		,       lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						return super().__call__(lowerCamelCase_		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

						a					     =   load_image(lowerCamelCase_		)

						if prompt is not None:
											if not isinstance(lowerCamelCase_		,       lowerCamelCase_		):
																raise ValueError(
																    F'''Received an invalid text input, got - {type(lowerCamelCase_		)} - but expected a single string. '''
																    "Note also that one single text can be provided for conditional image to text generation."		)

											a					     =   self.model.config.model_type

											if model_type == "git":
																a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors=self.framework		)
																a					     =   self.tokenizer(text=lowerCamelCase_		,       add_special_tokens=lowerCamelCase_		).input_ids
																a					     =   [self.tokenizer.cls_token_id] + input_ids
																a					     =   torch.tensor(lowerCamelCase_		).unsqueeze(0		)
																model_inputs.update({"input_ids": input_ids}		)

											elif model_type == "pix2struct":
																a					     =   self.image_processor(images=lowerCamelCase_		,       header_text=lowerCamelCase_		,       return_tensors=self.framework		)

											elif model_type != "vision-encoder-decoder":
																# vision-encoder-decoder does not support conditional generation
																a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors=self.framework		)
																a					     =   self.tokenizer(lowerCamelCase_		,       return_tensors=self.framework		)
																model_inputs.update(lowerCamelCase_		)

											else:
																raise ValueError(F'''Model type {model_type} does not support conditional text generation'''		)

						else:
											a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors=self.framework		)

						if self.model.config.model_type == "git" and prompt is None:
											a					     =   None

						return model_inputs




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

						if (
						    "input_ids" in model_inputs
						    and isinstance(model_inputs["input_ids"]		,       lowerCamelCase_		)
						    and all(x is None for x in model_inputs["input_ids"]		)
						):
											a					     =   None

						if generate_kwargs is None:
											a					     =   {}
						# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
						#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
						#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
						#  in the `_prepare_model_inputs` method.
						a					     =   model_inputs.pop(self.model.main_input_name		)
						a					     =   self.model.generate(lowerCamelCase_		,       **lowerCamelCase_		,       **lowerCamelCase_		)
						return model_outputs




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   []
						for output_ids in model_outputs:
											a					     =   {
											    "generated_text": self.tokenizer.decode(
											        lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		,       )
											}
											records.append(lowerCamelCase_		)
						return records
