
'''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__      :







				def __init__(       self						,				A_						,				A_=3						,				A_=32						,				A_=3						,				A_=10						,				A_=[8, 16, 32, 64]						,				A_=[1, 1, 2, 1]						,				A_=True						,				A_=True						,				A_="relu"						,				A_=3						,				A_=None						,				A_=["stage2", "stage3", "stage4"]						,				A_=[2, 3, 4]						,				A_=1						,				)->		Union[str, Any]:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






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

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

											UpperCamelCase   			=     self.get_config()

											return config, pixel_values, labels







				def 						UpperCAmelCase_    (       self     )->		Union[str, Any]:







											'''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 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_     )->		Tuple:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_     )->		int:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     BitBackbone(config=A_     )
											model.to(A_     )
											model.eval()
											UpperCamelCase   			=     model(A_     )

											# 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
											UpperCamelCase   			=     None
											UpperCamelCase   			=     BitBackbone(config=A_     )
											model.to(A_     )
											model.eval()
											UpperCamelCase   			=     model(A_     )

											# 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 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






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


@require_torch
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
				lowerCAmelCase_     			=							(
				    {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
				    if is_torch_available()
				    else {}
				)

				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     BitModelTester(self     )
											UpperCamelCase   			=     ConfigTester(self						,				config_class=A_						,				has_text_modality=A_     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''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 						UpperCAmelCase_    (       self     )->		Union[str, Any]:







											'''simple docstring'''






											return







				@unittest.skip(reason='Bit does not output attentions'     )
				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											pass







				@unittest.skip(reason='Bit does not use inputs_embeds'     )
				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											pass







				@unittest.skip(reason='Bit does not support input and output embeddings'     )
				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											pass







				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs_for_common()

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

																		UpperCamelCase   			=     ['pixel_values']
																		self.assertListEqual(arg_names[:1]						,				A_     )







				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*A_     )







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*A_     )







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		UpperCamelCase   			=     model_class(config=A_     )
																		for name, module in model.named_modules():
																									if isinstance(A_						,				(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 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											def check_hidden_states_output(A_						,				A_						,				A_     ):
																		UpperCamelCase   			=     model_class(A_     )
																		model.to(A_     )
																		model.eval()

																		with torch.no_grad():
																									UpperCamelCase   			=     model(**self._prepare_for_class(A_						,				A_     )     )

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

																		UpperCamelCase   			=     self.model_tester.num_stages
																		self.assertEqual(len(A_     )						,				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]						,				)

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

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

																									check_hidden_states_output(A_						,				A_						,				A_     )







				@unittest.skip(reason='Bit does not use feedforward chunking'     )
				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											pass







				def 						UpperCAmelCase_    (       self     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*A_     )







				@slow
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






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







def 		A_(     ):
							UpperCamelCase   			=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
							return image


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







				@cached_property
				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






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







				@slow
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(A_     )

											UpperCamelCase   			=     self.default_image_processor
											UpperCamelCase   			=     prepare_img()
											UpperCamelCase   			=     image_processor(images=A_						,				return_tensors='pt'     ).to(A_     )

											# forward pass
											with torch.no_grad():
																		UpperCamelCase   			=     model(**A_     )

											# verify the logits
											UpperCamelCase   			=     torch.Size((1, 1000)     )
											self.assertEqual(outputs.logits.shape						,				A_     )

											UpperCamelCase   			=     torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]     ).to(A_     )

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


@require_torch
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							(BitBackbone,) if is_torch_available() else ()
				lowerCAmelCase_     			=							BitConfig

				lowerCAmelCase_     			=							False







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     BitModelTester(self     )






'''simple docstring'''
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


lowerCAmelCase					:    Optional[Any]           = logging.get_logger(__name__)  # pylint: disable=invalid-name


class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       snake_case_):







				@register_to_config
				def __init__(       self						,				A_						,				A_ = None						,				A_ = None     )->		Tuple:







											'''simple docstring'''






											super().__init__()

											UpperCamelCase   			=     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"

																		UpperCamelCase   			=     torch.zeros(A_						,				A_     )
											else:
																		UpperCamelCase   			=     None

											UpperCamelCase   			=     torch.nn.Parameter(A_     )


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42







				def __init__(       self						,				A_						,				A_						,				A_						,				A_						,				A_						,				A_						,				)->		Union[str, Any]:







											'''simple docstring'''






											super().__init__()

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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     len(A_     ) if isinstance(A_						,				A_     ) else 1

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

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		UpperCamelCase   			=     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}'''     )
																		UpperCamelCase   			=     text_input_ids[:, : self.tokenizer.model_max_length]
											UpperCamelCase   			=     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
											UpperCamelCase   			=     prompt_embeds / prompt_embeds.norm(dim=-1						,				keepdim=A_     )

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

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

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

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

											return prompt_embeds







				@torch.no_grad()
				def __call__(       self						,				A_						,				A_ = 100						,				A_ = 5.0						,				A_ = 1.0						,				A_ = 1						,				A_ = None						,				A_ = None						,				A_ = "pil"						,				A_ = True						,				A_ = None						,				A_ = 1						,				)->		Union[ImagePipelineOutput, Tuple]:







											'''simple docstring'''






											if isinstance(A_						,				A_     ):
																		UpperCamelCase   			=     1
											elif isinstance(A_						,				A_     ):
																		UpperCamelCase   			=     len(A_     )
											else:
																		raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A_     )}'''     )

											UpperCamelCase   			=     batch_size * num_images_per_prompt

											UpperCamelCase   			=     guidance_scale > 1.0

											UpperCamelCase   			=     self._encode_prompt(A_						,				A_						,				A_     )

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

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

											UpperCamelCase   			=     (batch_size, self.transformer.num_latent_pixels)
											if latents is None:
																		UpperCamelCase   			=     self.transformer.num_vector_embeds - 1
																		UpperCamelCase   			=     torch.full(A_						,				A_     ).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).'''     )
																		UpperCamelCase   			=     latents.to(self.device     )

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

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

											UpperCamelCase   			=     latents

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

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

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

																		UpperCamelCase   			=     self.truncate(A_						,				A_     )

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

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

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

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

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

											if output_type == "pil":
																		UpperCamelCase   			=     self.numpy_to_pil(A_     )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=A_     )







				def 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		torch.FloatTensor:







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase   			=     torch.sort(A_						,				1						,				descending=A_     )
											UpperCamelCase   			=     torch.exp(A_     )
											UpperCamelCase   			=     sorted_p_x_0.cumsum(dim=1     ) < truncation_rate

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

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

											UpperCamelCase   			=     log_p_x_0.clone()

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

											return rv




