from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
  import numpy as np
  from decord import VideoReader


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

SCREAMING_SNAKE_CASE :	Any							     =		logging.get_logger(__name__)
@add_end_docstrings(a_ )
class    A_ (       a_ ):

  def __init__(		self :      Dict     ,							*__SCREAMING_SNAKE_CASE :      Tuple     ,							**__SCREAMING_SNAKE_CASE :      str       ):
   super().__init__(*__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )
   requires_backends(self     ,							"decord"       )
   self.check_model_type(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Dict=None     ,							__SCREAMING_SNAKE_CASE :      Any=None     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=None       ):
   __a       =  {}
   if frame_sampling_rate is not None:
    __a       =  frame_sampling_rate
   if num_frames is not None:
    __a       =  num_frames

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

  def __call__(		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Union[str, List[str]]     ,							**__SCREAMING_SNAKE_CASE :      Dict       ):
   return super().__call__(__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=None     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=1       ):
   if num_frames is None:
    __a       =  self.model.config.num_frames

   if video.startswith("http://"       ) or video.startswith("https://"       ):
    __a       =  BytesIO(requests.get(__SCREAMING_SNAKE_CASE       ).content       )

   __a       =  VideoReader(__SCREAMING_SNAKE_CASE       )
   videoreader.seek(0       )

   __a       =  0
   __a       =  num_frames * frame_sampling_rate - 1
   __a       =  np.linspace(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							num=__SCREAMING_SNAKE_CASE     ,							dtype=np.intaa       )

   __a       =  videoreader.get_batch(__SCREAMING_SNAKE_CASE       ).asnumpy()
   __a       =  list(__SCREAMING_SNAKE_CASE       )

   __a       =  self.image_processor(__SCREAMING_SNAKE_CASE     ,							return_tensors=self.framework       )
   return model_inputs

  def 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]       ):
   __a       =  self.model(**__SCREAMING_SNAKE_CASE       )
   return model_outputs

  def 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any=5       ):
   if top_k > self.model.config.num_labels:
    __a       =  self.model.config.num_labels

   if self.framework == "pt":
    __a       =  model_outputs.logits.softmax(-1       )[0]
    __a       ,     __a       =  probs.topk(__SCREAMING_SNAKE_CASE       )
   else:
    raise ValueError(f"""Unsupported framework: {self.framework}"""       )

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



import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class    A_ (       unittest.TestCase ):

  @property
  def 		_UpperCAmelCase   (		self :      str       ):
   torch.manual_seed(0       )
   __a       =  UNetaDModel(
       block_out_channels=(32, 64)     ,							layers_per_block=2     ,							sample_size=32     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=("DownBlock2D", "AttnDownBlock2D")     ,							up_block_types=("AttnUpBlock2D", "UpBlock2D")     ,							)
   return model

  @property
  def 		_UpperCAmelCase   (		self :      Dict       ):
   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     ,							)
   return model

  @property
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   torch.manual_seed(0       )
   __a       =  CLIPTextConfig(
       bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=32     ,							intermediate_size=37     ,							layer_norm_eps=1E-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=10_00     ,							)
   return CLIPTextModel(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  self.dummy_uncond_unet
   __a       =  DDIMScheduler()
   __a       =  self.dummy_vq_model

   __a       =  LDMPipeline(unet=__SCREAMING_SNAKE_CASE     ,							vqvae=__SCREAMING_SNAKE_CASE     ,							scheduler=__SCREAMING_SNAKE_CASE       )
   ldm.to(__SCREAMING_SNAKE_CASE       )
   ldm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="numpy"       ).images

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="numpy"     ,							return_dict=__SCREAMING_SNAKE_CASE       )[0]

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

   assert image.shape == (1, 64, 64, 3)
   __a       =  np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]       )
   __a       =  1E-2 if torch_device != "mps" else 3E-2

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < tolerance
@slow
@require_torch
class    A_ (       unittest.TestCase ):

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"       )
   ldm.to(__SCREAMING_SNAKE_CASE       )
   ldm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=5     ,							output_type="numpy"       ).images

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

   assert image.shape == (1, 2_56, 2_56, 3)
   __a       =  np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]       )
   __a       =  1E-2 if torch_device != "mps" else 3E-2

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance


