'''simple docstring'''


import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


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

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[int]			):
  return 1.0 / (1.0 + np.exp(-_outputs			))


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[Any]			):
  __a			=					np.max(_outputs				,					axis=-1				,					keepdims=a_			)
  __a			=					np.exp(_outputs - maxes			)
  return shifted_exp / shifted_exp.sum(axis=-1				,					keepdims=a_			)





class    __lowercase						(  __magic_name__ ):
   _a							=      """sigmoid"""
   _a							=      """softmax"""
   _a							=      """none"""





@add_end_docstrings(
    __magic_name__       ,							R"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """       ,							)
class    __lowercase						(  __magic_name__ ):
   _a							=      False
   _a							=      ClassificationFunction.NONE







   def __init__(	self      ,							**UpperCamelCase      )				->    Union[str, Any]:
     super().__init__(**UpperCamelCase      )

     self.check_model_type(
         TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
         if self.framework == 'tf'
         else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=""      ,							**UpperCamelCase      )				->    int:
     # Using "" as default argument because we're going to use `top_k=None` in user code to declare
     # "No top_k"
     __a			=					tokenizer_kwargs

     __a			=					{}
     if hasattr(self.model.config      ,							'return_all_scores'      ) and return_all_scores is None:
       __a			=					self.model.config.return_all_scores

     if isinstance(UpperCamelCase      ,							UpperCamelCase      ) or top_k is None:
       __a			=					top_k
       __a			=					False
     elif return_all_scores is not None:
       warnings.warn(
           '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
           ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'      ,							UpperCamelCase      ,							)
       if return_all_scores:
         __a			=					None
       else:
         __a			=					1

     if isinstance(UpperCamelCase      ,							UpperCamelCase      ):
       __a			=					ClassificationFunction[function_to_apply.upper()]

     if function_to_apply is not None:
       __a			=					function_to_apply
     return preprocess_params, {}, postprocess_params







   def __call__(	self      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Dict:
     __a			=					super().__call__(*UpperCamelCase      ,							**UpperCamelCase      )
     # TODO try and retrieve it in a nicer way from _sanitize_parameters.
     __a			=					'top_k' not in kwargs
     if isinstance(args[0]      ,							UpperCamelCase      ) and _legacy:
       # This pipeline is odd, and return a list when single item is run
       return [result]
     else:
       return result







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							**UpperCamelCase      )				->    Dict[str, GenericTensor]:
     __a			=					self.framework
     if isinstance(UpperCamelCase      ,							UpperCamelCase      ):
       return self.tokenizer(**UpperCamelCase      ,							return_tensors=UpperCamelCase      ,							**UpperCamelCase      )
     elif isinstance(UpperCamelCase      ,							UpperCamelCase      ) and len(UpperCamelCase      ) == 1 and isinstance(inputs[0]      ,							UpperCamelCase      ) and len(inputs[0]      ) == 2:
       # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
       return self.tokenizer(
           text=inputs[0][0]      ,							text_pair=inputs[0][1]      ,							return_tensors=UpperCamelCase      ,							**UpperCamelCase      )
     elif isinstance(UpperCamelCase      ,							UpperCamelCase      ):
       # This is likely an invalid usage of the pipeline attempting to pass text pairs.
       raise ValueError(
           'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
           ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'      )
     return self.tokenizer(UpperCamelCase      ,							return_tensors=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[Any]:
     return self.model(**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      ,							UpperCamelCase=1      ,							UpperCamelCase=True      )				->    List[Any]:
     # `_legacy` is used to determine if we're running the naked pipeline and in backward
     # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
     # the more natural result containing the list.
     # Default value before `set_parameters`
     if function_to_apply is None:
       if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
         __a			=					ClassificationFunction.SIGMOID
       elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
         __a			=					ClassificationFunction.SOFTMAX
       elif hasattr(self.model.config      ,							'function_to_apply'      ) and function_to_apply is None:
         __a			=					self.model.config.function_to_apply
       else:
         __a			=					ClassificationFunction.NONE

     __a			=					model_outputs['logits'][0]
     __a			=					outputs.numpy()

     if function_to_apply == ClassificationFunction.SIGMOID:
       __a			=					sigmoid(UpperCamelCase      )
     elif function_to_apply == ClassificationFunction.SOFTMAX:
       __a			=					softmax(UpperCamelCase      )
     elif function_to_apply == ClassificationFunction.NONE:
       __a			=					outputs
     else:
       raise ValueError(f"Unrecognized `function_to_apply` argument: {function_to_apply}"      )

     if top_k == 1 and _legacy:
       return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

     __a			=					[
         {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(UpperCamelCase      )
     ]
     if not _legacy:
       dict_scores.sort(key=lambda UpperCamelCase      : x["score"]      ,							reverse=UpperCamelCase      )
       if top_k is not None:
         __a			=					dict_scores[:top_k]
     return dict_scores









'''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


UpperCAmelCase_ 		=      False





class    __lowercase						(  unittest.TestCase ):







   def 		UpperCamelCase__			(	self      )				->    int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   @property
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     return 12







   @property
   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     return 12







   @property
   def 		UpperCamelCase__			(	self      )				->    List[str]:
     return 32







   @property
   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     torch.manual_seed(0      )
     __a			=					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      )				->    Optional[int]:
     __a			=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
     return tokenizer







   @property
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     torch.manual_seed(0      )
     __a			=					CLIPTextConfig(
         bos_token_id=0      ,							eos_token_id=2      ,							hidden_size=self.text_embedder_hidden_size      ,							intermediate_size=37      ,							layer_norm_eps=1e-05      ,							num_attention_heads=4      ,							num_hidden_layers=5      ,							pad_token_id=1      ,							vocab_size=1000      ,							)
     return CLIPTextModel(UpperCamelCase      )







   @property
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     torch.manual_seed(0      )

     __a			=					12
     __a			=					12

     __a			=					{
         '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',
     }

     __a			=					TransformeraDModel(**UpperCamelCase      )
     return model







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					'cpu'

     __a			=					self.dummy_vqvae
     __a			=					self.dummy_text_encoder
     __a			=					self.dummy_tokenizer
     __a			=					self.dummy_transformer
     __a			=					VQDiffusionScheduler(self.num_embed      )
     __a			=					LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCamelCase      )

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

     __a			=					'teddy bear playing in the pool'

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

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

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

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

     __a			=					np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]      )

     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      )				->    int:
     __a			=					'cpu'

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

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

     __a			=					'teddy bear playing in the pool'

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

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

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

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

     __a			=					np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]      )

     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 ):







   def 		UpperCamelCase__			(	self      )				->    Any:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'      )

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

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

     __a			=					output.images[0]

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



