import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class __snake_case    (						lowerCamelCase_       ,		unittest.TestCase		):
   lowerCAmelCase_														=							AudioLDMPipeline
   lowerCAmelCase_														=							TEXT_TO_AUDIO_PARAMS
   lowerCAmelCase_														=							TEXT_TO_AUDIO_BATCH_PARAMS
   lowerCAmelCase_														=							frozenset(
       [
           "num_inference_steps",
           "num_waveforms_per_prompt",
           "generator",
           "latents",
           "output_type",
           "return_dict",
           "callback",
           "callback_steps",
       ]		)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				UNetaDConditionModel(
         block_out_channels=(32, 64)	,     layers_per_block=2	,     sample_size=32	,     in_channels=4	,     out_channels=4	,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,     up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")	,     cross_attention_dim=(32, 64)	,     class_embed_type="""simple_projection"""	,     projection_class_embeddings_input_dim=32	,     class_embeddings_concat=_lowercase	,     )
     SCREAMING_SNAKE_CASE__      							=				DDIMScheduler(
         beta_start=0.0_00_85	,     beta_end=0.0_12	,     beta_schedule="""scaled_linear"""	,     clip_sample=_lowercase	,     set_alpha_to_one=_lowercase	,     )
     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				AutoencoderKL(
         block_out_channels=[32, 64]	,     in_channels=1	,     out_channels=1	,     down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,     up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,     latent_channels=4	,     )
     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				ClapTextConfig(
         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	,     projection_dim=32	,     )
     SCREAMING_SNAKE_CASE__      							=				ClapTextModelWithProjection(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta"""	,     model_max_length=77				)

     SCREAMING_SNAKE_CASE__      							=				SpeechTaHifiGanConfig(
         model_in_dim=8	,     sampling_rate=1_60_00	,     upsample_initial_channel=16	,     upsample_rates=[2, 2]	,     upsample_kernel_sizes=[4, 4]	,     resblock_kernel_sizes=[3, 7]	,     resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]	,     normalize_before=_lowercase	,     )

     SCREAMING_SNAKE_CASE__      							=				SpeechTaHifiGan(_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				{
         """unet""": unet,
         """scheduler""": scheduler,
         """vae""": vae,
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
         """vocoder""": vocoder,
     }
     return components




   def      __a						(   self      :    Dict	,     _lowercase      :    List[Any]	,     _lowercase      :    Union[str, Any]=0				):



     """simple docstring"""






     if str(_lowercase				).startswith("""mps"""				):
       SCREAMING_SNAKE_CASE__      							=				torch.manual_seed(_lowercase				)
     else:
       SCREAMING_SNAKE_CASE__      							=				torch.Generator(device=_lowercase				).manual_seed(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				{
         """prompt""": """A hammer hitting a wooden surface""",
         """generator""": generator,
         """num_inference_steps""": 2,
         """guidance_scale""": 6.0,
     }
     return inputs




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator

     SCREAMING_SNAKE_CASE__      							=				self.get_dummy_components()
     SCREAMING_SNAKE_CASE__      							=				AudioLDMPipeline(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.to(_lowercase				)
     audioldm_pipe.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				self.get_dummy_inputs(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				output.audios[0]

     assert audio.ndim == 1
     assert len(_lowercase				) == 2_56

     SCREAMING_SNAKE_CASE__      							=				audio[:10]
     SCREAMING_SNAKE_CASE__      							=				np.array(
         [-0.00_50, 0.00_50, -0.00_60, 0.00_33, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_33]				)

     assert np.abs(audio_slice - expected_slice				).max() < 1E-2




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.get_dummy_components()
     SCREAMING_SNAKE_CASE__      							=				AudioLDMPipeline(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.to(_lowercase				)
     audioldm_pipe.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				self.get_dummy_inputs(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				3 * [inputs["""prompt"""]]

     # forward
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				output.audios[0]

     SCREAMING_SNAKE_CASE__      							=				self.get_dummy_inputs(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				3 * [inputs.pop("""prompt"""				)]

     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.tokenizer(
         _lowercase	,     padding="""max_length"""	,     max_length=audioldm_pipe.tokenizer.model_max_length	,     truncation=_lowercase	,     return_tensors="""pt"""	,     )
     SCREAMING_SNAKE_CASE__      							=				text_inputs["""input_ids"""].to(_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.text_encoder(
         _lowercase	,     )
     SCREAMING_SNAKE_CASE__      							=				prompt_embeds.text_embeds
     # additional L_2 normalization over each hidden-state
     SCREAMING_SNAKE_CASE__      							=				F.normalize(_lowercase	,     dim=-1				)

     SCREAMING_SNAKE_CASE__      							=				prompt_embeds

     # forward
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				output.audios[0]

     assert np.abs(audio_a - audio_a				).max() < 1E-2




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.get_dummy_components()
     SCREAMING_SNAKE_CASE__      							=				AudioLDMPipeline(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.to(_lowercase				)
     audioldm_pipe.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				self.get_dummy_inputs(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				3 * ["""this is a negative prompt"""]
     SCREAMING_SNAKE_CASE__      							=				negative_prompt
     SCREAMING_SNAKE_CASE__      							=				3 * [inputs["""prompt"""]]

     # forward
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				output.audios[0]

     SCREAMING_SNAKE_CASE__      							=				self.get_dummy_inputs(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				3 * [inputs.pop("""prompt"""				)]

     SCREAMING_SNAKE_CASE__      							=				[]
     for p in [prompt, negative_prompt]:
       SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.tokenizer(
           _lowercase	,     padding="""max_length"""	,     max_length=audioldm_pipe.tokenizer.model_max_length	,     truncation=_lowercase	,     return_tensors="""pt"""	,     )
       SCREAMING_SNAKE_CASE__      							=				text_inputs["""input_ids"""].to(_lowercase				)

       SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.text_encoder(
           _lowercase	,     )
       SCREAMING_SNAKE_CASE__      							=				text_embeds.text_embeds
       # additional L_2 normalization over each hidden-state
       SCREAMING_SNAKE_CASE__      							=				F.normalize(_lowercase	,     dim=-1				)

       embeds.append(_lowercase				)

     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				embeds

     # forward
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				output.audios[0]

     assert np.abs(audio_a - audio_a				).max() < 1E-2




   def      __a						(   self      :    Any				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__      							=				self.get_dummy_components()
     SCREAMING_SNAKE_CASE__      							=				PNDMScheduler(skip_prk_steps=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AudioLDMPipeline(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.to(_lowercase				)
     audioldm_pipe.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				self.get_dummy_inputs(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				"""egg cracking"""
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe(**_lowercase	,     negative_prompt=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				output.audios[0]

     assert audio.ndim == 1
     assert len(_lowercase				) == 2_56

     SCREAMING_SNAKE_CASE__      							=				audio[:10]
     SCREAMING_SNAKE_CASE__      							=				np.array(
         [-0.00_51, 0.00_50, -0.00_60, 0.00_34, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_32]				)

     assert np.abs(audio_slice - expected_slice				).max() < 1E-2




   def      __a						(   self      :    Any				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__      							=				self.get_dummy_components()
     SCREAMING_SNAKE_CASE__      							=				PNDMScheduler(skip_prk_steps=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AudioLDMPipeline(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.to(_lowercase				)
     audioldm_pipe.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				"""A hammer hitting a wooden surface"""

     # test num_waveforms_per_prompt=1 (default)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe(_lowercase	,     num_inference_steps=2				).audios

     assert audios.shape == (1, 2_56)

     # test num_waveforms_per_prompt=1 (default) for batch of prompts
     SCREAMING_SNAKE_CASE__      							=				2
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe([prompt] * batch_size	,     num_inference_steps=2				).audios

     assert audios.shape == (batch_size, 2_56)

     # test num_waveforms_per_prompt for single prompt
     SCREAMING_SNAKE_CASE__      							=				2
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe(_lowercase	,     num_inference_steps=2	,     num_waveforms_per_prompt=_lowercase				).audios

     assert audios.shape == (num_waveforms_per_prompt, 2_56)

     # test num_waveforms_per_prompt for batch of prompts
     SCREAMING_SNAKE_CASE__      							=				2
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe(
         [prompt] * batch_size	,     num_inference_steps=2	,     num_waveforms_per_prompt=_lowercase				).audios

     assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_56)




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__      							=				self.get_dummy_components()
     SCREAMING_SNAKE_CASE__      							=				AudioLDMPipeline(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.to(_lowercase				)
     audioldm_pipe.set_progress_bar_config(disable=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.vocoder.config.sampling_rate

     SCREAMING_SNAKE_CASE__      							=				self.get_dummy_inputs(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe(audio_length_in_s=0.0_16	,     **_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				output.audios[0]

     assert audio.ndim == 1
     assert len(_lowercase				) / vocoder_sampling_rate == 0.0_16

     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe(audio_length_in_s=0.0_32	,     **_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				output.audios[0]

     assert audio.ndim == 1
     assert len(_lowercase				) / vocoder_sampling_rate == 0.0_32




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.get_dummy_components()
     SCREAMING_SNAKE_CASE__      							=				AudioLDMPipeline(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.to(_lowercase				)
     audioldm_pipe.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				["""hey"""]

     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe(_lowercase	,     num_inference_steps=1				)
     SCREAMING_SNAKE_CASE__      							=				output.audios.shape
     assert audio_shape == (1, 2_56)

     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.vocoder.config
     config.model_in_dim *= 2
     SCREAMING_SNAKE_CASE__      							=				SpeechTaHifiGan(_lowercase				).to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe(_lowercase	,     num_inference_steps=1				)
     SCREAMING_SNAKE_CASE__      							=				output.audios.shape
     # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
     assert audio_shape == (1, 2_56)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_lowercase				)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     self._test_inference_batch_single_identical(test_mean_pixel_difference=_lowercase				)




   @unittest.skipIf(
       torch_device != """cuda""" or not is_xformers_available()	,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""	,     )
   def      __a						(   self      :    Dict				):



     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_lowercase				)







@slow
class __snake_case    (						unittest.TestCase		):




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






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




   def      __a						(   self      :    Any	,     _lowercase      :    Tuple	,     _lowercase      :    Any="cpu"	,     _lowercase      :    List[str]=torch.floataa	,     _lowercase      :    List[Any]=0				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				torch.Generator(device=_lowercase				).manual_seed(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				np.random.RandomState(_lowercase				).standard_normal((1, 8, 1_28, 16)				)
     SCREAMING_SNAKE_CASE__      							=				torch.from_numpy(_lowercase				).to(device=_lowercase	,     dtype=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				{
         """prompt""": """A hammer hitting a wooden surface""",
         """latents""": latents,
         """generator""": generator,
         """num_inference_steps""": 3,
         """guidance_scale""": 2.5,
     }
     return inputs




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.to(_lowercase				)
     audioldm_pipe.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				self.get_inputs(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				25
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe(**_lowercase				).audios[0]

     assert audio.ndim == 1
     assert len(_lowercase				) == 8_19_20

     SCREAMING_SNAKE_CASE__      							=				audio[7_72_30:7_72_40]
     SCREAMING_SNAKE_CASE__      							=				np.array(
         [-0.48_84, -0.46_07, 0.00_23, 0.50_07, 0.58_96, 0.51_51, 0.38_13, -0.02_08, -0.36_87, -0.43_15]				)
     SCREAMING_SNAKE_CASE__      							=				np.abs(expected_slice - audio_slice				).max()
     assert max_diff < 1E-2




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""				)
     SCREAMING_SNAKE_CASE__      							=				LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe.to(_lowercase				)
     audioldm_pipe.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				self.get_inputs(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				audioldm_pipe(**_lowercase				).audios[0]

     assert audio.ndim == 1
     assert len(_lowercase				) == 8_19_20

     SCREAMING_SNAKE_CASE__      							=				audio[2_77_80:2_77_90]
     SCREAMING_SNAKE_CASE__      							=				np.array([-0.21_31, -0.08_73, -0.01_24, -0.01_89, 0.05_69, 0.13_73, 0.18_83, 0.28_86, 0.32_97, 0.22_12]				)
     SCREAMING_SNAKE_CASE__      							=				np.abs(expected_slice - audio_slice				).max()
     assert max_diff < 3E-2

from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
   import PIL

if is_torch_available():
   import torch


__lowerCamelCase      :			int							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(   self      :    int	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = PILImageResampling.BILINEAR	,     _lowercase      :    bool = True	,     _lowercase      :    Union[int, float] = 1 / 2_55	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = True	,     **_lowercase      :    List[Any]	,     ):



     """simple docstring"""






     super().__init__(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				size if size is not None else {"""shortest_edge""": 2_24}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else {"""height""": 2_56, """width""": 2_56}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     param_name="""crop_size"""				)

     SCREAMING_SNAKE_CASE__      							=				do_resize
     SCREAMING_SNAKE_CASE__      							=				size
     SCREAMING_SNAKE_CASE__      							=				resample
     SCREAMING_SNAKE_CASE__      							=				do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_center_crop
     SCREAMING_SNAKE_CASE__      							=				crop_size
     SCREAMING_SNAKE_CASE__      							=				do_flip_channel_order




   def      __a						(   self      :    List[Any]	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    PILImageResampling = PIL.Image.BILINEAR	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    str	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     if "shortest_edge" not in size:
       raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""				)
     SCREAMING_SNAKE_CASE__      							=				get_resize_output_image_size(_lowercase	,     size=size["""shortest_edge"""]	,     default_to_square=_lowercase				)
     return resize(_lowercase	,     size=_lowercase	,     resample=_lowercase	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    str	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Any	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase				)
     if "height" not in size or "width" not in size:
       raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""				)
     return center_crop(_lowercase	,     size=(size["""height"""], size["""width"""])	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    Optional[Any]	,     _lowercase      :    np.ndarray	,     _lowercase      :    Union[int, float]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Any	,     ):



     """simple docstring"""






     return rescale(_lowercase	,     scale=_lowercase	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    Tuple	,     _lowercase      :    np.ndarray	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None				):



     """simple docstring"""






     return flip_channel_order(_lowercase	,     data_format=_lowercase				)




   def      __a						(   self      :    List[str]	,     _lowercase      :    ImageInput	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = None	,     _lowercase      :    bool = None	,     _lowercase      :    float = None	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = None	,     _lowercase      :    Optional[Union[str, TensorType]] = None	,     _lowercase      :    ChannelDimension = ChannelDimension.FIRST	,     **_lowercase      :    int	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__      							=				resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__      							=				do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE__      							=				(
         do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
     )

     SCREAMING_SNAKE_CASE__      							=				size if size is not None else self.size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     param_name="""crop_size"""				)

     SCREAMING_SNAKE_CASE__      							=				make_list_of_images(_lowercase				)

     if not valid_images(_lowercase				):
       raise ValueError(
           """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
           """torch.Tensor, tf.Tensor or jax.ndarray."""				)

     if do_resize and size is None:
       raise ValueError("""Size must be specified if do_resize is True."""				)

     if do_rescale and rescale_factor is None:
       raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

     if do_center_crop and crop_size is None:
       raise ValueError("""Crop size must be specified if do_center_crop is True."""				)

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__      							=				[to_numpy_array(_lowercase				) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE__      							=				[self.resize(image=_lowercase	,     size=_lowercase	,     resample=_lowercase				) for image in images]

     if do_center_crop:
       SCREAMING_SNAKE_CASE__      							=				[self.center_crop(image=_lowercase	,     size=_lowercase				) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE__      							=				[self.rescale(image=_lowercase	,     scale=_lowercase				) for image in images]

     # the pretrained checkpoints assume images are BGR, not RGB
     if do_flip_channel_order:
       SCREAMING_SNAKE_CASE__      							=				[self.flip_channel_order(image=_lowercase				) for image in images]

     SCREAMING_SNAKE_CASE__      							=				[to_channel_dimension_format(_lowercase	,     _lowercase				) for image in images]

     SCREAMING_SNAKE_CASE__      							=				{"""pixel_values""": images}
     return BatchFeature(data=_lowercase	,     tensor_type=_lowercase				)




   def      __a						(   self      :    List[Any]	,     _lowercase      :    Dict	,     _lowercase      :    List[Tuple] = None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
       if len(_lowercase				) != len(_lowercase				):
         raise ValueError(
             """Make sure that you pass in as many target sizes as the batch dimension of the logits"""				)

       if is_torch_tensor(_lowercase				):
         SCREAMING_SNAKE_CASE__      							=				target_sizes.numpy()

       SCREAMING_SNAKE_CASE__      							=				[]

       for idx in range(len(_lowercase				)				):
         SCREAMING_SNAKE_CASE__      							=				torch.nn.functional.interpolate(
             logits[idx].unsqueeze(dim=0				)	,     size=target_sizes[idx]	,     mode="""bilinear"""	,     align_corners=_lowercase				)
         SCREAMING_SNAKE_CASE__      							=				resized_logits[0].argmax(dim=0				)
         semantic_segmentation.append(_lowercase				)
     else:
       SCREAMING_SNAKE_CASE__      							=				logits.argmax(dim=1				)
       SCREAMING_SNAKE_CASE__      							=				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

     return semantic_segmentation
