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_tf_available():
      import tensorflow as tf

      from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
      from ..tf_utils import stable_softmax

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

UpperCamelCase  							=						logging.get_logger(__name__)



@add_end_docstrings(__A       )
class   snake_case_       (		__A       ):






   def __init__(      self				:     str	,						*lowercase_				:     Any	,						**lowercase_				:     Optional[Any]  )					->						Dict:
       super().__init__(*lowercase_	,						**lowercase_  )
       requires_backends(self	,						"vision"  )
       self.check_model_type(
           TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
           if self.framework == "tf"
           else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING  )






   def       __UpperCamelCase     (      self				:     int	,						lowercase_				:     List[Any]=None  )					->						List[Any]:
       lowercase__ :   Optional[int]    					=				{}
       if top_k is not None:
           lowercase__ :   Tuple    					=				top_k
       return {}, {}, postprocess_params






   def __call__(      self				:     Optional[Any]	,						lowercase_				:     Union[str, List[str], "Image.Image", List["Image.Image"]]	,						**lowercase_				:     Optional[int]  )					->						Optional[int]:
       return super().__call__(lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     Any	,						lowercase_				:     Union[str, Any]  )					->						Any:
       lowercase__ :   str    					=				load_image(lowercase_  )
       lowercase__ :   str    					=				self.image_processor(images=lowercase_	,						return_tensors=self.framework  )
       return model_inputs






   def       __UpperCamelCase     (      self				:     int	,						lowercase_				:     Any  )					->						List[str]:
       lowercase__ :   Optional[Any]    					=				self.model(**lowercase_  )
       return model_outputs







   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     List[Any]	,						lowercase_				:     List[Any]=5  )					->						Optional[Any]:
       if top_k > self.model.config.num_labels:
           lowercase__ :   Optional[Any]    					=				self.model.config.num_labels

       if self.framework == "pt":
           lowercase__ :   int    					=				model_outputs.logits.softmax(-1  )[0]
           lowercase__					,   lowercase__ :   Optional[Any]    					=				probs.topk(lowercase_  )
       elif self.framework == "tf":
           lowercase__ :   Optional[int]    					=				stable_softmax(model_outputs.logits	,						axis=-1  )[0]
           lowercase__ :   int    					=				tf.math.top_k(lowercase_	,						k=lowercase_  )
           lowercase__					,   lowercase__ :   Any    					=				topk.values.numpy(), topk.indices.numpy()
       else:
           raise ValueError(F'''Unsupported framework: {self.framework}'''  )

       lowercase__ :   Any    					=				scores.tolist()
       lowercase__ :   List[Any]    					=				ids.tolist()
       return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_	,						lowercase_  )]







import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
@skip_mps
class  SCREAMING_SNAKE_CASE__  (				lowercase__						,    unittest.TestCase			):
      snake_case__				:     Optional[Any]        =						TextToVideoSDPipeline
      snake_case__				:     Optional[int]        =						TEXT_TO_IMAGE_PARAMS
      snake_case__				:     str        =						TEXT_TO_IMAGE_BATCH_PARAMS
      # No `output_type`.
      snake_case__				:     Optional[Any]        =						frozenset(
          [
              '''num_inference_steps''',
              '''generator''',
              '''latents''',
              '''return_dict''',
              '''callback''',
              '''callback_steps''',
          ]			)
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Union[str, Any]:
             torch.manual_seed(0     )
             a_					:		Optional[int]							=  UNetaDConditionModel(
                 block_out_channels=(3_2, 6_4, 6_4, 6_4)     ,							layers_per_block=2     ,							sample_size=3_2     ,							in_channels=4     ,							out_channels=4     ,							down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D')     ,							up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D')     ,							cross_attention_dim=3_2     ,							attention_head_dim=4     ,							)
             a_					:		int							=  DDIMScheduler(
                 beta_start=0.00085     ,							beta_end=0.012     ,							beta_schedule='scaled_linear'     ,							clip_sample=SCREAMING_SNAKE_CASE__     ,							set_alpha_to_one=SCREAMING_SNAKE_CASE__     ,							)
             torch.manual_seed(0     )
             a_					:		int							=  AutoencoderKL(
                 block_out_channels=[3_2, 6_4]     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     ,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     ,							latent_channels=4     ,							sample_size=1_2_8     ,							)
             torch.manual_seed(0     )
             a_					:		Optional[int]							=  CLIPTextConfig(
                 bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=3_2     ,							intermediate_size=3_7     ,							layer_norm_eps=1E-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=1_0_0_0     ,							hidden_act='gelu'     ,							projection_dim=5_1_2     ,							)
             a_					:		Dict							=  CLIPTextModel(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

             a_					:		Union[str, Any]							=  {
                 'unet': unet,
                 'scheduler': scheduler,
                 'vae': vae,
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
             }
             return components
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=0     )		->			List[str]:
             if str(SCREAMING_SNAKE_CASE__     ).startswith('mps'     ):
                    a_					:		Dict							=  torch.manual_seed(SCREAMING_SNAKE_CASE__     )
             else:
                    a_					:		Optional[Any]							=  torch.Generator(device=SCREAMING_SNAKE_CASE__     ).manual_seed(SCREAMING_SNAKE_CASE__     )
             a_					:		int							=  {
                 'prompt': 'A painting of a squirrel eating a burger',
                 'generator': generator,
                 'num_inference_steps': 2,
                 'guidance_scale': 6.0,
                 'output_type': 'pt',
             }
             return inputs
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Tuple:
             a_					:		List[Any]							=  'cpu'  # ensure determinism for the device-dependent torch.Generator
             a_					:		Dict							=  self.get_dummy_components()
             a_					:		str							=  TextToVideoSDPipeline(**SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  sd_pipe.to(SCREAMING_SNAKE_CASE__     )
             sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Tuple							=  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  'np'
             a_					:		Dict							=  sd_pipe(**SCREAMING_SNAKE_CASE__     ).frames
             a_					:		int							=  frames[0][-3:, -3:, -1]

             assert frames[0].shape == (6_4, 6_4, 3)
             a_					:		Union[str, Any]							=  np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			List[Any]:
             self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE__     ,							expected_max_diff=3E-3     )
      @unittest.skipIf(
          torch_device != 'cuda' or not is_xformers_available()     ,							reason='XFormers attention is only available with CUDA and `xformers` installed'     ,							)
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			str:
             self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE__     ,							expected_max_diff=1E-2     )
      @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Union[str, Any]:
             pass
      @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			List[str]:
             pass
      @unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'     )
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			List[Any]:
             pass

      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Union[str, Any]:
             return super().test_progress_bar()
@slow
@skip_mps
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			List[Any]:
             a_					:		str							=  load_numpy(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy'     )

             a_					:		Any							=  TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b'     )
             a_					:		Optional[Any]							=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
             a_					:		Optional[Any]							=  pipe.to('cuda'     )

             a_					:		Any							=  'Spiderman is surfing'
             a_					:		List[Any]							=  torch.Generator(device='cpu'     ).manual_seed(0     )

             a_					:		Optional[Any]							=  pipe(SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ,							num_inference_steps=2_5     ,							output_type='pt'     ).frames
             a_					:		str							=  video_frames.cpu().numpy()

             assert np.abs(expected_video - video     ).mean() < 5E-2

      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Any:
             a_					:		Dict							=  load_numpy(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy'     )

             a_					:		Tuple							=  TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b'     )
             a_					:		Tuple							=  pipe.to('cuda'     )

             a_					:		Any							=  'Spiderman is surfing'
             a_					:		List[str]							=  torch.Generator(device='cpu'     ).manual_seed(0     )

             a_					:		List[Any]							=  pipe(SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ,							num_inference_steps=2     ,							output_type='pt'     ).frames
             a_					:		List[str]							=  video_frames.cpu().numpy()

             assert np.abs(expected_video - video     ).mean() < 5E-2


