



from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowerCAmelCase__		:					str							=8






def      __lowercase (			a__							,       a__=BITS       )    ->			Any:
							__SCREAMING_SNAKE_CASE									= x.device

							__SCREAMING_SNAKE_CASE									= (x * 2_55).int().clamp(0							,       2_55       )

							__SCREAMING_SNAKE_CASE									= 2 ** torch.arange(bits - 1							,       -1							,       -1							,       device=a__       )
							__SCREAMING_SNAKE_CASE									= rearrange(a__							,       'd -> d 1 1'       )
							__SCREAMING_SNAKE_CASE									= rearrange(a__							,       'b c h w -> b c 1 h w'       )

							__SCREAMING_SNAKE_CASE									= ((x & mask) != 0).float()
							__SCREAMING_SNAKE_CASE									= rearrange(a__							,       'b c d h w -> b (c d) h w'       )
							__SCREAMING_SNAKE_CASE									= bits * 2 - 1
							return bits






def      __lowercase (			a__							,       a__=BITS       )    ->			List[str]:
							__SCREAMING_SNAKE_CASE									= x.device

							__SCREAMING_SNAKE_CASE									= (x > 0).int()
							__SCREAMING_SNAKE_CASE									= 2 ** torch.arange(bits - 1							,       -1							,       -1							,       device=a__							,       dtype=torch.intaa       )

							__SCREAMING_SNAKE_CASE									= rearrange(a__							,       'd -> d 1 1'       )
							__SCREAMING_SNAKE_CASE									= rearrange(a__							,       'b (c d) h w -> b c d h w'							,       d=8       )
							__SCREAMING_SNAKE_CASE									= reduce(x * mask							,       'b c d h w -> b c h w'							,       'sum'       )
							return (dec / 2_55).clamp(0.0							,       1.0       )






def      __lowercase (			self							,       a__							,       a__							,       a__							,       a__ = 0.0							,       a__ = True							,       a__=None							,       a__ = True							,       )    ->			Union[DDIMSchedulerOutput, Tuple]:
							if self.num_inference_steps is None:
														raise ValueError(
														    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'       )

							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
							# Ideally, read DDIM paper in-detail understanding

							# Notation (<variable name> -> <name in paper>
							# - pred_noise_t -> e_theta(x_t, t)
							# - pred_original_sample -> f_theta(x_t, t) or x_0
							# - std_dev_t -> sigma_t
							# - eta -> η
							# - pred_sample_direction -> "direction pointing to x_t"
							# - pred_prev_sample -> "x_t-1"

							# 1. get previous step value (=t-1)
							__SCREAMING_SNAKE_CASE									= timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							__SCREAMING_SNAKE_CASE									= self.alphas_cumprod[timestep]
							__SCREAMING_SNAKE_CASE									= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

							__SCREAMING_SNAKE_CASE									= 1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							__SCREAMING_SNAKE_CASE									= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

							# 4. Clip "predicted x_0"
							__SCREAMING_SNAKE_CASE									= self.bit_scale
							if self.config.clip_sample:
														__SCREAMING_SNAKE_CASE									= torch.clamp(a__							,       -scale							,       a__       )

							# 5. compute variance: "sigma_t(η)" -> see formula (16)
							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
							__SCREAMING_SNAKE_CASE									= self._get_variance(a__							,       a__       )
							__SCREAMING_SNAKE_CASE									= eta * variance ** 0.5

							if use_clipped_model_output:
														# the model_output is always re-derived from the clipped x_0 in Glide
														__SCREAMING_SNAKE_CASE									= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

							# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							__SCREAMING_SNAKE_CASE									= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							__SCREAMING_SNAKE_CASE									= alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if eta > 0:
														# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
														__SCREAMING_SNAKE_CASE									= model_output.device if torch.is_tensor(a__       ) else 'cpu'
														__SCREAMING_SNAKE_CASE									= torch.randn(model_output.shape							,       dtype=model_output.dtype							,       generator=a__       ).to(a__       )
														__SCREAMING_SNAKE_CASE									= self._get_variance(a__							,       a__       ) ** 0.5 * eta * noise

														__SCREAMING_SNAKE_CASE									= prev_sample + variance

							if not return_dict:
														return (prev_sample,)

							return DDIMSchedulerOutput(prev_sample=a__							,       pred_original_sample=a__       )






def      __lowercase (			self							,       a__							,       a__							,       a__							,       a__="epsilon"							,       a__=None							,       a__ = True							,       )    ->			Union[DDPMSchedulerOutput, Tuple]:
							__SCREAMING_SNAKE_CASE									= timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
														__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= torch.split(a__							,       sample.shape[1]							,       dim=1       )
							else:
														__SCREAMING_SNAKE_CASE									= None

							# 1. compute alphas, betas
							__SCREAMING_SNAKE_CASE									= self.alphas_cumprod[t]
							__SCREAMING_SNAKE_CASE									= self.alphas_cumprod[t - 1] if t > 0 else self.one
							__SCREAMING_SNAKE_CASE									= 1 - alpha_prod_t
							__SCREAMING_SNAKE_CASE									= 1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if prediction_type == "epsilon":
														__SCREAMING_SNAKE_CASE									= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif prediction_type == "sample":
														__SCREAMING_SNAKE_CASE									= model_output
							else:
														raise ValueError(f"""Unsupported prediction_type {prediction_type}."""       )

							# 3. Clip "predicted x_0"
							__SCREAMING_SNAKE_CASE									= self.bit_scale
							if self.config.clip_sample:
														__SCREAMING_SNAKE_CASE									= torch.clamp(a__							,       -scale							,       a__       )

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							__SCREAMING_SNAKE_CASE									= (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
							__SCREAMING_SNAKE_CASE									= self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							__SCREAMING_SNAKE_CASE									= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							__SCREAMING_SNAKE_CASE									= 0
							if t > 0:
														__SCREAMING_SNAKE_CASE									= torch.randn(
														    model_output.size()							,       dtype=model_output.dtype							,       layout=model_output.layout							,       generator=a__       ).to(model_output.device       )
														__SCREAMING_SNAKE_CASE									= (self._get_variance(a__							,       predicted_variance=a__       ) ** 0.5) * noise

							__SCREAMING_SNAKE_CASE									= pred_prev_sample + variance

							if not return_dict:
														return (pred_prev_sample,)

							return DDPMSchedulerOutput(prev_sample=a__							,       pred_original_sample=a__       )

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A				,							_A = 1.0				,							):







													'''simple docstring'''

													super().__init__()
													__SCREAMING_SNAKE_CASE									= bit_scale
													__SCREAMING_SNAKE_CASE									= (
													    ddim_bit_scheduler_step if isinstance(_A				,							_A		) else ddpm_bit_scheduler_step
													)

													self.register_modules(unet=_A				,							scheduler=_A		)






						@torch.no_grad()
						def __call__(   self				,							_A = 256				,							_A = 256				,							_A = 50				,							_A = None				,							_A = 1				,							_A = "pil"				,							_A = True				,							**_A				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= torch.randn(
													    (batch_size, self.unet.config.in_channels, height, width)				,							generator=_A				,							)
													__SCREAMING_SNAKE_CASE									= decimal_to_bits(_A		) * self.bit_scale
													__SCREAMING_SNAKE_CASE									= latents.to(self.device		)

													self.scheduler.set_timesteps(_A		)

													for t in self.progress_bar(self.scheduler.timesteps		):
																				# predict the noise residual
																				__SCREAMING_SNAKE_CASE									= self.unet(_A				,							_A		).sample

																				# compute the previous noisy sample x_t -> x_t-1
																				__SCREAMING_SNAKE_CASE									= self.scheduler.step(_A				,							_A				,							_A		).prev_sample

													__SCREAMING_SNAKE_CASE									= bits_to_decimal(_A		)

													if output_type == "pil":
																				__SCREAMING_SNAKE_CASE									= self.numpy_to_pil(_A		)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=_A		)











from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
			import tensorflow as tf

			from transformers import TFBlipTextModel
			from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class        UpperCAmelCase_  :

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A=12				,							_A=7				,							_A=True				,							_A=True				,							_A=True				,							_A=99				,							_A=32				,							_A=32				,							_A=2				,							_A=4				,							_A=37				,							_A=0.1				,							_A=0.1				,							_A=512				,							_A=0.0_2				,							_A=0				,							_A=None				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= parent
													__SCREAMING_SNAKE_CASE									= batch_size
													__SCREAMING_SNAKE_CASE									= seq_length
													__SCREAMING_SNAKE_CASE									= is_training
													__SCREAMING_SNAKE_CASE									= use_input_mask
													__SCREAMING_SNAKE_CASE									= use_labels
													__SCREAMING_SNAKE_CASE									= vocab_size
													__SCREAMING_SNAKE_CASE									= hidden_size
													__SCREAMING_SNAKE_CASE									= projection_dim
													__SCREAMING_SNAKE_CASE									= num_hidden_layers
													__SCREAMING_SNAKE_CASE									= num_attention_heads
													__SCREAMING_SNAKE_CASE									= intermediate_size
													__SCREAMING_SNAKE_CASE									= dropout
													__SCREAMING_SNAKE_CASE									= attention_dropout
													__SCREAMING_SNAKE_CASE									= max_position_embeddings
													__SCREAMING_SNAKE_CASE									= initializer_range
													__SCREAMING_SNAKE_CASE									= scope
													__SCREAMING_SNAKE_CASE									= bos_token_id






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size		)

													__SCREAMING_SNAKE_CASE									= None
													if self.use_input_mask:
																				__SCREAMING_SNAKE_CASE									= random_attention_mask([self.batch_size, self.seq_length]		)

													if input_mask is not None:
																				__SCREAMING_SNAKE_CASE									= input_mask.numpy()
																				__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= input_mask.shape
																				__SCREAMING_SNAKE_CASE									= np.random.randint(1				,							seq_length - 1				,							size=(batch_size,)		)
																				for batch_idx, start_index in enumerate(_A		):
																											__SCREAMING_SNAKE_CASE									= 1
																											__SCREAMING_SNAKE_CASE									= 0

													__SCREAMING_SNAKE_CASE									= self.get_config()

													return config, input_ids, tf.convert_to_tensor(_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													return BlipTextConfig(
													    vocab_size=self.vocab_size				,							hidden_size=self.hidden_size				,							projection_dim=self.projection_dim				,							num_hidden_layers=self.num_hidden_layers				,							num_attention_heads=self.num_attention_heads				,							intermediate_size=self.intermediate_size				,							dropout=self.dropout				,							attention_dropout=self.attention_dropout				,							max_position_embeddings=self.max_position_embeddings				,							initializer_range=self.initializer_range				,							bos_token_id=self.bos_token_id				,							)






						def 	_A     (   self				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= TFBlipTextModel(config=_A		)
													__SCREAMING_SNAKE_CASE									= model(_A				,							attention_mask=_A				,							training=_A		)
													__SCREAMING_SNAKE_CASE									= model(_A				,							training=_A		)
													self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size)		)
													self.parent.assertEqual(result.pooler_output.shape				,							(self.batch_size, self.hidden_size)		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.prepare_config_and_inputs()
													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= config_and_inputs
													__SCREAMING_SNAKE_CASE									= {'input_ids': input_ids, 'attention_mask': input_mask}
													return config, inputs_dict







@require_tf
class        UpperCAmelCase_  (       UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			str      =  (TFBlipTextModel,) if is_tf_available() else ()
						UpperCamelCase__      :			int      =  False
						UpperCamelCase__      :			Optional[Any]      =  False
						UpperCamelCase__      :			Tuple      =  False






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= BlipTextModelTester(self		)
													__SCREAMING_SNAKE_CASE									= ConfigTester(self				,							config_class=_A				,							hidden_size=37		)






						def 	_A     (   self		):







													'''simple docstring'''

													self.config_tester.run_common_tests()






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													pass






						def 	_A     (   self		):







													'''simple docstring'''

													pass






						@unittest.skip(reason='Blip does not use inputs_embeds'		)
						def 	_A     (   self		):







													'''simple docstring'''

													pass






						@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'		)
						def 	_A     (   self		):







													'''simple docstring'''

													pass






						@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'		)
						def 	_A     (   self		):







													'''simple docstring'''

													pass






						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__SCREAMING_SNAKE_CASE									= TFBlipTextModel.from_pretrained(_A		)
																				self.assertIsNotNone(_A		)






						def 	_A     (   self				,							_A=True		):







													'''simple docstring'''

													super().test_pt_tf_model_equivalence(allow_missing_keys=_A		)






