





'''simple docstring'''



from collections import UserDict
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_torch_available():
	from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
	from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
	from ..tf_utils import stable_softmax

_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase__						)
class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,**_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





				super().__init__(**_lowerCamelCase )

				requires_backends(self     ,'''vision''' )
				self.check_model_type(
				    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
				    if self.framework == '''tf'''
				    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )


	def __call__(self     ,_lowerCamelCase     ,**_lowerCamelCase )							->							Dict:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self     ,**_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							{}
				if "candidate_labels" in kwargs:
							__lowercase										=							kwargs['''candidate_labels''']
				if "hypothesis_template" in kwargs:
							__lowercase										=							kwargs['''hypothesis_template''']

				return preprocess_params, {}, {}


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase=None     ,_lowerCamelCase="This is a photo of {}." )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							load_image(_lowerCamelCase )
				__lowercase										=							self.image_processor(images=[image]     ,return_tensors=self.framework )
				__lowercase										=							candidate_labels
				__lowercase										=							[hypothesis_template.format(_lowerCamelCase ) for x in candidate_labels]
				__lowercase										=							self.tokenizer(_lowerCamelCase     ,return_tensors=self.framework     ,padding=_lowerCamelCase )
				__lowercase										=							[text_inputs]
				return inputs


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							model_inputs.pop('''candidate_labels''' )
				__lowercase										=							model_inputs.pop('''text_inputs''' )
				if isinstance(text_inputs[0]     ,_lowerCamelCase ):
							__lowercase										=							text_inputs[0]
				else:
							# Batching case.
							__lowercase										=							text_inputs[0][0]

				__lowercase										=							self.model(**_lowerCamelCase     ,**_lowerCamelCase )

				__lowercase										=							{
				    '''candidate_labels''': candidate_labels,
				    '''logits''': outputs.logits_per_image,
				}
				return model_outputs


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Dict:







				'''simple docstring'''





				__lowercase										=							model_outputs.pop('''candidate_labels''' )
				__lowercase										=							model_outputs['''logits'''][0]
				if self.framework == "pt":
							__lowercase										=							logits.softmax(dim=-1 ).squeeze(-1 )
							__lowercase										=							probs.tolist()
							if not isinstance(_lowerCamelCase     ,_lowerCamelCase ):
										__lowercase										=							[scores]
				elif self.framework == "tf":
							__lowercase										=							stable_softmax(_lowerCamelCase     ,axis=-1 )
							__lowercase										=							probs.numpy().tolist()
				else:
							raise ValueError(f"Unsupported framework: {self.framework}" )

				__lowercase										=							[
				    {'''score''': score, '''label''': candidate_label}
				    for score, candidate_label in sorted(zip(_lowerCamelCase     ,_lowerCamelCase )     ,key=lambda _lowerCamelCase : -x[0] )
				]
				return result












'''simple docstring'''



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_SCREAMING_SNAKE_CASE					=    False






class 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

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







				'''simple docstring'''





				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()


	@property
	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				return 12


	@property
	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				return 12


	@property
	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				return 32


	@property
	def 						_UpperCAmelCase     (self )							->							List[str]:







				'''simple docstring'''





				torch.manual_seed(0 )
				__lowercase										=							VQModel(
				    block_out_channels=[32, 64]     ,in_channels=3     ,out_channels=3     ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,latent_channels=3     ,num_vq_embeddings=self.num_embed     ,vq_embed_dim=3     ,)
				return model


	@property
	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				__lowercase										=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
				return tokenizer


	@property
	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				torch.manual_seed(0 )
				__lowercase										=							CLIPTextConfig(
				    bos_token_id=0     ,eos_token_id=2     ,hidden_size=self.text_embedder_hidden_size     ,intermediate_size=37     ,layer_norm_eps=1E-0_5     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=1000     ,)
				return CLIPTextModel(_lowerCamelCase )


	@property
	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				torch.manual_seed(0 )

				__lowercase										=							12
				__lowercase										=							12

				__lowercase										=							{
				    '''attention_bias''': True,
				    '''cross_attention_dim''': 32,
				    '''attention_head_dim''': height * width,
				    '''num_attention_heads''': 1,
				    '''num_vector_embeds''': self.num_embed,
				    '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
				    '''norm_num_groups''': 32,
				    '''sample_size''': width,
				    '''activation_fn''': '''geglu-approximate''',
				}

				__lowercase										=							TransformeraDModel(**_lowerCamelCase )
				return model


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







				'''simple docstring'''





				__lowercase										=							'''cpu'''

				__lowercase										=							self.dummy_vqvae
				__lowercase										=							self.dummy_text_encoder
				__lowercase										=							self.dummy_tokenizer
				__lowercase										=							self.dummy_transformer
				__lowercase										=							VQDiffusionScheduler(self.num_embed )
				__lowercase										=							LearnedClassifierFreeSamplingEmbeddings(learnable=_lowerCamelCase )

				__lowercase										=							VQDiffusionPipeline(
				    vqvae=_lowerCamelCase     ,text_encoder=_lowerCamelCase     ,tokenizer=_lowerCamelCase     ,transformer=_lowerCamelCase     ,scheduler=_lowerCamelCase     ,learned_classifier_free_sampling_embeddings=_lowerCamelCase     ,)
				__lowercase										=							pipe.to(_lowerCamelCase )
				pipe.set_progress_bar_config(disable=_lowerCamelCase )

				__lowercase										=							'''teddy bear playing in the pool'''

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe([prompt]     ,generator=_lowerCamelCase     ,num_inference_steps=2     ,output_type='''np''' )
				__lowercase										=							output.images

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe(
				    [prompt]     ,generator=_lowerCamelCase     ,output_type='''np'''     ,return_dict=_lowerCamelCase     ,num_inference_steps=2 )[0]

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

				assert image.shape == (1, 24, 24, 3)

				__lowercase										=							np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2] )

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


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				__lowercase										=							'''cpu'''

				__lowercase										=							self.dummy_vqvae
				__lowercase										=							self.dummy_text_encoder
				__lowercase										=							self.dummy_tokenizer
				__lowercase										=							self.dummy_transformer
				__lowercase										=							VQDiffusionScheduler(self.num_embed )
				__lowercase										=							LearnedClassifierFreeSamplingEmbeddings(
				    learnable=_lowerCamelCase     ,hidden_size=self.text_embedder_hidden_size     ,length=tokenizer.model_max_length )

				__lowercase										=							VQDiffusionPipeline(
				    vqvae=_lowerCamelCase     ,text_encoder=_lowerCamelCase     ,tokenizer=_lowerCamelCase     ,transformer=_lowerCamelCase     ,scheduler=_lowerCamelCase     ,learned_classifier_free_sampling_embeddings=_lowerCamelCase     ,)
				__lowercase										=							pipe.to(_lowerCamelCase )
				pipe.set_progress_bar_config(disable=_lowerCamelCase )

				__lowercase										=							'''teddy bear playing in the pool'''

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe([prompt]     ,generator=_lowerCamelCase     ,num_inference_steps=2     ,output_type='''np''' )
				__lowercase										=							output.images

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe(
				    [prompt]     ,generator=_lowerCamelCase     ,output_type='''np'''     ,return_dict=_lowerCamelCase     ,num_inference_steps=2 )[0]

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

				assert image.shape == (1, 24, 24, 3)

				__lowercase										=							np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8] )

				assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2






@slow
@require_torch_gpu
class 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy''' )

				__lowercase										=							VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq''' )
				__lowercase										=							pipeline.to(_lowerCamelCase )
				pipeline.set_progress_bar_config(disable=_lowerCamelCase )

				# requires GPU generator for gumbel softmax
				# don't use GPU generator in tests though
				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipeline(
				    '''teddy bear playing in the pool'''     ,num_images_per_prompt=1     ,generator=_lowerCamelCase     ,output_type='''np'''     ,)

				__lowercase										=							output.images[0]

				assert image.shape == (256, 256, 3)
				assert np.abs(expected_image - image ).max() < 2.0





