

'''simple docstring'''





import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
		from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image


class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=3 ,				__UpperCamelCase=32 ,				__UpperCamelCase=3 ,				__UpperCamelCase=10 ,				__UpperCamelCase=[8, 16, 32, 64] ,				__UpperCamelCase=[1, 1, 2, 1] ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase="relu" ,				__UpperCamelCase=3 ,				__UpperCamelCase=None ,				__UpperCamelCase=["stage2", "stage3", "stage4"] ,				__UpperCamelCase=[2, 3, 4] ,				__UpperCamelCase=1 ,				):

				"""simple docstring"""


				snake_case_      		= parent
				snake_case_      		= batch_size
				snake_case_      		= image_size
				snake_case_      		= num_channels
				snake_case_      		= embeddings_size
				snake_case_      		= hidden_sizes
				snake_case_      		= depths
				snake_case_      		= is_training
				snake_case_      		= use_labels
				snake_case_      		= hidden_act
				snake_case_      		= num_labels
				snake_case_      		= scope
				snake_case_      		= len(__UpperCamelCase							)
				snake_case_      		= out_features
				snake_case_      		= out_indices
				snake_case_      		= num_groups


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

				snake_case_      		= None
				if self.use_labels:
					snake_case_      		= ids_tensor([self.batch_size] ,				self.num_labels							)

				snake_case_      		= self.get_config()

				return config, pixel_values, labels


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return BitConfig(
				    num_channels=self.num_channels ,				embeddings_size=self.embeddings_size ,				hidden_sizes=self.hidden_sizes ,				depths=self.depths ,				hidden_act=self.hidden_act ,				num_labels=self.num_labels ,				out_features=self.out_features ,				out_indices=self.out_indices ,				num_groups=self.num_groups ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= BitModel(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(
				    result.last_hidden_state.shape ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.num_labels
				snake_case_      		= BitForImageClassification(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= BitBackbone(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase							)

				# verify feature maps
				self.parent.assertEqual(len(result.feature_maps							) ,				len(config.out_features							)							)
				self.parent.assertListEqual(list(result.feature_maps[0].shape							) ,				[self.batch_size, self.hidden_sizes[1], 4, 4]							)

				# verify channels
				self.parent.assertEqual(len(model.channels							) ,				len(config.out_features							)							)
				self.parent.assertListEqual(model.channels ,				config.hidden_sizes[1:]							)

				# verify backbone works with out_features=None
				snake_case_      		= None
				snake_case_      		= BitBackbone(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase							)

				# verify feature maps
				self.parent.assertEqual(len(result.feature_maps							) ,				1							)
				self.parent.assertListEqual(list(result.feature_maps[0].shape							) ,				[self.batch_size, self.hidden_sizes[-1], 1, 1]							)

				# verify channels
				self.parent.assertEqual(len(model.channels							) ,				1							)
				self.parent.assertListEqual(model.channels ,				[config.hidden_sizes[-1]]							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.prepare_config_and_inputs()
				snake_case_		,       snake_case_		,       snake_case_      		= config_and_inputs
				snake_case_      		= {'pixel_values': pixel_values}
				return config, inputs_dict







@require_torch
class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
			__A						  =  (
			    {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
			    if is_torch_available()
			    else {}
			)

			__A						  =  False
			__A						  =  False
			__A						  =  False
			__A						  =  False
			__A						  =  False


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= BitModelTester(self							)
				snake_case_      		= ConfigTester(self ,				config_class=__UpperCamelCase ,				has_text_modality=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return


			@unittest.skip(reason='Bit does not output attentions'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


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

				"""simple docstring"""


				pass


			@unittest.skip(reason='Bit does not support input and output embeddings'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					snake_case_      		= model_class(__UpperCamelCase							)
					snake_case_      		= inspect.signature(model.forward							)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					snake_case_      		= [*signature.parameters.keys()]

					snake_case_      		= ['pixel_values']
					self.assertListEqual(arg_names[:1] ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_backbone(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					snake_case_      		= model_class(config=__UpperCamelCase							)
					for name, module in model.named_modules():
						if isinstance(__UpperCamelCase ,				(nn.BatchNormad, nn.GroupNorm)							):
							self.assertTrue(
							    torch.all(module.weight == 1							) ,				msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,				)
							self.assertTrue(
							    torch.all(module.bias == 0							) ,				msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,				)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				def check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= model_class(__UpperCamelCase							)
					model.to(__UpperCamelCase							)
					model.eval()

					with torch.no_grad():
						snake_case_      		= model(**self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)							)

					snake_case_      		= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

					snake_case_      		= self.model_tester.num_stages
					self.assertEqual(len(__UpperCamelCase							) ,				expected_num_stages + 1							)

					# Bit's feature maps are of shape (batch_size, num_channels, height, width)
					self.assertListEqual(
					    list(hidden_states[0].shape[-2:]							) ,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,				)

				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()
				snake_case_      		= ['preactivation', 'bottleneck']
				for model_class in self.all_model_classes:
					for layer_type in layers_type:
						snake_case_      		= layer_type
						snake_case_      		= True
						check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						snake_case_      		= True

						check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)


			@unittest.skip(reason='Bit does not use feedforward chunking'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase							)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					snake_case_      		= BitModel.from_pretrained(__UpperCamelCase							)
					self.assertIsNotNone(__UpperCamelCase							)







def a():





	'''simple docstring'''
	snake_case_      		= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image


@require_torch
@require_vision
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@cached_property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return (
				    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							) if is_vision_available() else None
				)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(__UpperCamelCase							)

				snake_case_      		= self.default_image_processor
				snake_case_      		= prepare_img()
				snake_case_      		= image_processor(images=__UpperCamelCase ,				return_tensors='pt'							).to(__UpperCamelCase							)

				# forward pass
				with torch.no_grad():
					snake_case_      		= model(**__UpperCamelCase							)

				# verify the logits
				snake_case_      		= torch.Size((1, 10_00)							)
				self.assertEqual(outputs.logits.shape ,				__UpperCamelCase							)

				snake_case_      		= torch.tensor([[-0.6526, -0.5263, -1.4398]]							).to(__UpperCamelCase							)

				self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				__UpperCamelCase ,				atol=1E-4							)							)







@require_torch
class SCREAMING_SNAKE_CASE  (		__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  (BitBackbone,) if is_torch_available() else ()
			__A						  =  BitConfig

			__A						  =  False


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= BitModelTester(self							)



from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


A											=     logging.get_logger(__name__)  # pylint: disable=invalid-name


class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			@register_to_config
			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = None							):

				"""simple docstring"""


				super().__init__()

				snake_case_      		= learnable

				if self.learnable:
					assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
					assert length is not None, "learnable=True requires `length` to be set"

					snake_case_      		= torch.zeros(__UpperCamelCase ,				__UpperCamelCase							)
				else:
					snake_case_      		= None

				snake_case_      		= torch.nn.Parameter(__UpperCamelCase							)







class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42


			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__()

				self.register_modules(
				    vqvae=__UpperCamelCase ,				transformer=__UpperCamelCase ,				text_encoder=__UpperCamelCase ,				tokenizer=__UpperCamelCase ,				scheduler=__UpperCamelCase ,				learned_classifier_free_sampling_embeddings=__UpperCamelCase ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= len(__UpperCamelCase							) if isinstance(__UpperCamelCase ,				__UpperCamelCase							) else 1

				# get prompt text embeddings
				snake_case_      		= self.tokenizer(
				    __UpperCamelCase ,				padding='max_length' ,				max_length=self.tokenizer.model_max_length ,				return_tensors='pt' ,				)
				snake_case_      		= text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
					snake_case_      		= self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
					logger.warning(
					    'The following part of your input was truncated because CLIP can only handle sequences up to'
					    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""							)
					snake_case_      		= text_input_ids[:, : self.tokenizer.model_max_length]
				snake_case_      		= self.text_encoder(text_input_ids.to(self.device							)							)[0]

				# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
				# While CLIP does normalize the pooled output of the text transformer when combining
				# the image and text embeddings, CLIP does not directly normalize the last hidden state.
				#
				# CLIP normalizing the pooled output.
				# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
				snake_case_      		= prompt_embeds / prompt_embeds.norm(dim=-1 ,				keepdim=__UpperCamelCase							)

				# duplicate text embeddings for each generation per prompt
				snake_case_      		= prompt_embeds.repeat_interleave(__UpperCamelCase ,				dim=0							)

				if do_classifier_free_guidance:
					if self.learned_classifier_free_sampling_embeddings.learnable:
						snake_case_      		= self.learned_classifier_free_sampling_embeddings.embeddings
						snake_case_      		= negative_prompt_embeds.unsqueeze(0							).repeat(__UpperCamelCase ,				1 ,				1							)
					else:
						snake_case_      		= [''] * batch_size

						snake_case_      		= text_input_ids.shape[-1]
						snake_case_      		= self.tokenizer(
						    __UpperCamelCase ,				padding='max_length' ,				max_length=__UpperCamelCase ,				truncation=__UpperCamelCase ,				return_tensors='pt' ,				)
						snake_case_      		= self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
						# See comment for normalizing text embeddings
						snake_case_      		= negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 ,				keepdim=__UpperCamelCase							)

					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
					snake_case_      		= negative_prompt_embeds.shape[1]
					snake_case_      		= negative_prompt_embeds.repeat(1 ,				__UpperCamelCase ,				1							)
					snake_case_      		= negative_prompt_embeds.view(batch_size * num_images_per_prompt ,				__UpperCamelCase ,				-1							)

					# For classifier free guidance, we need to do two forward passes.
					# Here we concatenate the unconditional and text embeddings into a single batch
					# to avoid doing two forward passes
					snake_case_      		= torch.cat([negative_prompt_embeds, prompt_embeds]							)

				return prompt_embeds


			@torch.no_grad()
			def __call__(    self ,				__UpperCamelCase ,				__UpperCamelCase = 1_00 ,				__UpperCamelCase = 5.0 ,				__UpperCamelCase = 1.0 ,				__UpperCamelCase = 1 ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = "pil" ,				__UpperCamelCase = True ,				__UpperCamelCase = None ,				__UpperCamelCase = 1 ,				):

				"""simple docstring"""


				if isinstance(__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= 1
				elif isinstance(__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= len(__UpperCamelCase							)
				else:
					raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase							)}"""							)

				snake_case_      		= batch_size * num_images_per_prompt

				snake_case_      		= guidance_scale > 1.0

				snake_case_      		= self._encode_prompt(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(__UpperCamelCase ,				__UpperCamelCase							) or callback_steps <= 0)
				):
					raise ValueError(
					    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
					    f""" {type(__UpperCamelCase							)}."""							)

				# get the initial completely masked latents unless the user supplied it

				snake_case_      		= (batch_size, self.transformer.num_latent_pixels)
				if latents is None:
					snake_case_      		= self.transformer.num_vector_embeds - 1
					snake_case_      		= torch.full(__UpperCamelCase ,				__UpperCamelCase							).to(self.device							)
				else:
					if latents.shape != latents_shape:
						raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""							)
					if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
						raise ValueError(
						    'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
						    f""" {self.transformer.num_vector_embeds - 1} (inclusive)."""							)
					snake_case_      		= latents.to(self.device							)

				# set timesteps
				self.scheduler.set_timesteps(__UpperCamelCase ,				device=self.device							)

				snake_case_      		= self.scheduler.timesteps.to(self.device							)

				snake_case_      		= latents

				for i, t in enumerate(self.progress_bar(__UpperCamelCase							)							):
					# expand the sample if we are doing classifier free guidance
					snake_case_      		= torch.cat([sample] * 2							) if do_classifier_free_guidance else sample

					# predict the un-noised image
					# model_output == `log_p_x_0`
					snake_case_      		= self.transformer(__UpperCamelCase ,				encoder_hidden_states=__UpperCamelCase ,				timestep=__UpperCamelCase							).sample

					if do_classifier_free_guidance:
						snake_case_		,       snake_case_      		= model_output.chunk(2							)
						snake_case_      		= model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
						model_output -= torch.logsumexp(__UpperCamelCase ,				dim=1 ,				keepdim=__UpperCamelCase							)

					snake_case_      		= self.truncate(__UpperCamelCase ,				__UpperCamelCase							)

					# remove `log(0)`'s (`-inf`s)
					snake_case_      		= model_output.clamp(-70							)

					# compute the previous noisy sample x_t -> x_t-1
					snake_case_      		= self.scheduler.step(__UpperCamelCase ,				timestep=__UpperCamelCase ,				sample=__UpperCamelCase ,				generator=__UpperCamelCase							).prev_sample

					# call the callback, if provided
					if callback is not None and i % callback_steps == 0:
						callback(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

				snake_case_      		= self.vqvae.config.vq_embed_dim
				snake_case_      		= (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
				snake_case_      		= self.vqvae.quantize.get_codebook_entry(__UpperCamelCase ,				shape=__UpperCamelCase							)
				snake_case_      		= self.vqvae.decode(__UpperCamelCase ,				force_not_quantize=__UpperCamelCase							).sample

				snake_case_      		= (image / 2 + 0.5).clamp(0 ,				1							)
				snake_case_      		= image.cpu().permute(0 ,				2 ,				3 ,				1							).numpy()

				if output_type == "pil":
					snake_case_      		= self.numpy_to_pil(__UpperCamelCase							)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=__UpperCamelCase							)







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= torch.sort(__UpperCamelCase ,				1 ,				descending=__UpperCamelCase							)
				snake_case_      		= torch.exp(__UpperCamelCase							)
				snake_case_      		= sorted_p_x_0.cumsum(dim=1							) < truncation_rate

				# Ensure that at least the largest probability is not zeroed out
				snake_case_      		= torch.full_like(keep_mask[:, 0:1, :] ,				__UpperCamelCase							)
				snake_case_      		= torch.cat((all_true, keep_mask) ,				dim=1							)
				snake_case_      		= keep_mask[:, :-1, :]

				snake_case_      		= keep_mask.gather(1 ,				indices.argsort(1							)							)

				snake_case_      		= log_p_x_0.clone()

				snake_case_      		= -torch.inf  # -inf = log(0)

				return rv
