import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()





class 	A				(							unittest.TestCase	):
   __UpperCAmelCase					:					Tuple    =     StableDiffusionLDMaDPipeline
   __UpperCAmelCase					:					int    =     TEXT_TO_IMAGE_PARAMS
   __UpperCAmelCase					:					Dict    =     TEXT_TO_IMAGE_BATCH_PARAMS
   __UpperCAmelCase					:					Optional[Any]    =     TEXT_TO_IMAGE_IMAGE_PARAMS


   def      lowercase_ (self	:		int						)		->							Optional[Any]:
         """simple docstring"""



         torch.manual_seed(0						)
         UpperCAmelCase__								=    UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)			,    layers_per_block=2			,    sample_size=3_2			,    in_channels=4			,    out_channels=4			,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,    cross_attention_dim=3_2			,    )
         UpperCAmelCase__								=    DDIMScheduler(
             beta_start=0.00085			,    beta_end=0.012			,    beta_schedule="scaled_linear"			,    clip_sample=snake_case__			,    set_alpha_to_one=snake_case__			,    )
         torch.manual_seed(0						)
         UpperCAmelCase__								=    AutoencoderKL(
             block_out_channels=[3_2, 6_4]			,    in_channels=6			,    out_channels=6			,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,    latent_channels=4			,    )
         torch.manual_seed(0						)
         UpperCAmelCase__								=    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			,    )
         UpperCAmelCase__								=    CLIPTextModel(snake_case__						)
         UpperCAmelCase__								=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

         UpperCAmelCase__								=    {
             "unet": unet,
             "scheduler": scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "safety_checker": None,
             "feature_extractor": None,
         }
         return components


   def      lowercase_ (self	:		str			,    __UpperCAmelCase	:		Any			,    __UpperCAmelCase	:		Tuple=0						)		->							Optional[int]:
         """simple docstring"""



         if str(snake_case__						).startswith("mps"						):
               UpperCAmelCase__								=    torch.manual_seed(snake_case__						)
         else:
               UpperCAmelCase__								=    torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
         UpperCAmelCase__								=    {
             "prompt": "A painting of a squirrel eating a burger",
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
             "output_type": "numpy",
         }
         return inputs


   def      lowercase_ (self	:		int						)		->							Optional[int]:
         """simple docstring"""



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

         UpperCAmelCase__								=    self.get_dummy_components()
         UpperCAmelCase__								=    StableDiffusionLDMaDPipeline(**snake_case__						)
         UpperCAmelCase__								=    ldmad_pipe.to(snake_case__						)
         ldmad_pipe.set_progress_bar_config(disable=snake_case__						)

         UpperCAmelCase__								=    self.get_dummy_inputs(snake_case__						)
         UpperCAmelCase__								=    ldmad_pipe(**snake_case__						)
         UpperCAmelCase__						, UpperCAmelCase__								=    output.rgb, output.depth

         UpperCAmelCase__								=    rgb[0, -3:, -3:, -1]
         UpperCAmelCase__								=    depth[0, -3:, -1]

         assert rgb.shape == (1, 6_4, 6_4, 3)
         assert depth.shape == (1, 6_4, 6_4)

         UpperCAmelCase__								=    np.array(
             [0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262]						)
         UpperCAmelCase__								=    np.array([103.46727, 85.812004, 87.849236]						)

         assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb						).max() < 1E-2
         assert np.abs(image_slice_depth.flatten() - expected_slice_depth						).max() < 1E-2


   def      lowercase_ (self	:		Union[str, Any]						)		->							List[str]:
         """simple docstring"""



         UpperCAmelCase__								=    self.get_dummy_components()
         UpperCAmelCase__								=    StableDiffusionLDMaDPipeline(**snake_case__						)
         UpperCAmelCase__								=    ldmad_pipe.to(snake_case__						)
         ldmad_pipe.set_progress_bar_config(disable=snake_case__						)

         UpperCAmelCase__								=    self.get_dummy_inputs(snake_case__						)
         UpperCAmelCase__								=    3 * [inputs["prompt"]]

         # forward
         UpperCAmelCase__								=    ldmad_pipe(**snake_case__						)
         UpperCAmelCase__						, UpperCAmelCase__								=    output.rgb, output.depth
         UpperCAmelCase__								=    rgb_slice_a[0, -3:, -3:, -1]
         UpperCAmelCase__								=    depth_slice_a[0, -3:, -1]

         UpperCAmelCase__								=    self.get_dummy_inputs(snake_case__						)
         UpperCAmelCase__								=    3 * [inputs.pop("prompt"						)]

         UpperCAmelCase__								=    ldmad_pipe.tokenizer(
             snake_case__			,    padding="max_length"			,    max_length=ldmad_pipe.tokenizer.model_max_length			,    truncation=snake_case__			,    return_tensors="pt"			,    )
         UpperCAmelCase__								=    text_inputs["input_ids"].to(snake_case__						)

         UpperCAmelCase__								=    ldmad_pipe.text_encoder(snake_case__						)[0]

         UpperCAmelCase__								=    prompt_embeds

         # forward
         UpperCAmelCase__								=    ldmad_pipe(**snake_case__						)
         UpperCAmelCase__						, UpperCAmelCase__								=    output.rgb, output.depth
         UpperCAmelCase__								=    rgb_slice_a[0, -3:, -3:, -1]
         UpperCAmelCase__								=    depth_slice_a[0, -3:, -1]

         assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()						).max() < 1E-4
         assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()						).max() < 1E-4





   def      lowercase_ (self	:		List[str]						)		->							Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=    "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__								=    self.get_dummy_components()
         UpperCAmelCase__								=    PNDMScheduler(skip_prk_steps=snake_case__						)
         UpperCAmelCase__								=    StableDiffusionLDMaDPipeline(**snake_case__						)
         UpperCAmelCase__								=    ldmad_pipe.to(snake_case__						)
         ldmad_pipe.set_progress_bar_config(disable=snake_case__						)

         UpperCAmelCase__								=    self.get_dummy_inputs(snake_case__						)
         UpperCAmelCase__								=    "french fries"
         UpperCAmelCase__								=    ldmad_pipe(**snake_case__			,    negative_prompt=snake_case__						)

         UpperCAmelCase__						, UpperCAmelCase__								=    output.rgb, output.depth
         UpperCAmelCase__								=    rgb[0, -3:, -3:, -1]
         UpperCAmelCase__								=    depth[0, -3:, -1]

         assert rgb.shape == (1, 6_4, 6_4, 3)
         assert depth.shape == (1, 6_4, 6_4)

         UpperCAmelCase__								=    np.array(
             [0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217]						)
         UpperCAmelCase__								=    np.array([107.84738, 84.62802, 89.962135]						)
         assert np.abs(rgb_slice.flatten() - expected_slice_rgb						).max() < 1E-2
         assert np.abs(depth_slice.flatten() - expected_slice_depth						).max() < 1E-2





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


   def      lowercase_ (self	:		Optional[int]						)		->							int:
         """simple docstring"""



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


   def      lowercase_ (self	:		Optional[int]			,    __UpperCAmelCase	:		List[str]			,    __UpperCAmelCase	:		Dict="cpu"			,    __UpperCAmelCase	:		Tuple=torch.floataa			,    __UpperCAmelCase	:		Tuple=0						)		->							Any:
         """simple docstring"""



         UpperCAmelCase__								=    torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
         UpperCAmelCase__								=    np.random.RandomState(snake_case__						).standard_normal((1, 4, 6_4, 6_4)						)
         UpperCAmelCase__								=    torch.from_numpy(snake_case__						).to(device=snake_case__			,    dtype=snake_case__						)
         UpperCAmelCase__								=    {
             "prompt": "a photograph of an astronaut riding a horse",
             "latents": latents,
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
             "output_type": "numpy",
         }
         return inputs





   def      lowercase_ (self	:		Any						)		->							Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=    StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"						)
         UpperCAmelCase__								=    ldmad_pipe.to(snake_case__						)
         ldmad_pipe.set_progress_bar_config(disable=snake_case__						)

         UpperCAmelCase__								=    self.get_inputs(snake_case__						)
         UpperCAmelCase__								=    ldmad_pipe(**snake_case__						)
         UpperCAmelCase__						, UpperCAmelCase__								=    output.rgb, output.depth
         UpperCAmelCase__								=    rgb[0, -3:, -3:, -1].flatten()
         UpperCAmelCase__								=    rgb[0, -3:, -1].flatten()

         assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
         assert depth.shape == (1, 5_1_2, 5_1_2)

         UpperCAmelCase__								=    np.array(
             [0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706]						)
         UpperCAmelCase__								=    np.array(
             [0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706]						)
         assert np.abs(rgb_slice - expected_slice_rgb						).max() < 3E-3
         assert np.abs(depth_slice - expected_slice_depth						).max() < 3E-3





@nightly
@require_torch_gpu
class 	A				(							unittest.TestCase	):


   def      lowercase_ (self	:		Tuple						)		->							List[str]:
         """simple docstring"""



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


   def      lowercase_ (self	:		Tuple			,    __UpperCAmelCase	:		Optional[int]			,    __UpperCAmelCase	:		Any="cpu"			,    __UpperCAmelCase	:		Optional[int]=torch.floataa			,    __UpperCAmelCase	:		int=0						)		->							Dict:
         """simple docstring"""



         UpperCAmelCase__								=    torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
         UpperCAmelCase__								=    np.random.RandomState(snake_case__						).standard_normal((1, 4, 6_4, 6_4)						)
         UpperCAmelCase__								=    torch.from_numpy(snake_case__						).to(device=snake_case__			,    dtype=snake_case__						)
         UpperCAmelCase__								=    {
             "prompt": "a photograph of an astronaut riding a horse",
             "latents": latents,
             "generator": generator,
             "num_inference_steps": 5_0,
             "guidance_scale": 7.5,
             "output_type": "numpy",
         }
         return inputs


   def      lowercase_ (self	:		List[str]						)		->							str:
         """simple docstring"""



         UpperCAmelCase__								=    StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"						).to(snake_case__						)
         ldmad_pipe.set_progress_bar_config(disable=snake_case__						)

         UpperCAmelCase__								=    self.get_inputs(snake_case__						)
         UpperCAmelCase__								=    ldmad_pipe(**snake_case__						)
         UpperCAmelCase__						, UpperCAmelCase__								=    output.rgb, output.depth

         UpperCAmelCase__								=    0.495586
         UpperCAmelCase__								=    0.33795515
         UpperCAmelCase__								=    112.48518
         UpperCAmelCase__								=    98.489746
         assert np.abs(expected_rgb_mean - rgb.mean()						) < 1E-3
         assert np.abs(expected_rgb_std - rgb.std()						) < 1E-3
         assert np.abs(expected_depth_mean - depth.mean()						) < 1E-3
         assert np.abs(expected_depth_std - depth.std()						) < 1E-3





   def      lowercase_ (self	:		List[str]						)		->							List[str]:
         """simple docstring"""



         UpperCAmelCase__								=    StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c"						).to(snake_case__						)
         ldmad_pipe.set_progress_bar_config(disable=snake_case__						)

         UpperCAmelCase__								=    self.get_inputs(snake_case__						)
         UpperCAmelCase__								=    ldmad_pipe(**snake_case__						)
         UpperCAmelCase__						, UpperCAmelCase__								=    output.rgb, output.depth

         UpperCAmelCase__								=    0.4194127
         UpperCAmelCase__								=    0.35375586
         UpperCAmelCase__								=    0.5638502
         UpperCAmelCase__								=    0.34686103

         assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
         assert depth.shape == (1, 5_1_2, 5_1_2, 1)
         assert np.abs(expected_rgb_mean - rgb.mean()						) < 1E-3
         assert np.abs(expected_rgb_std - rgb.std()						) < 1E-3
         assert np.abs(expected_depth_mean - depth.mean()						) < 1E-3
         assert np.abs(expected_depth_std - depth.std()						) < 1E-3






import argparse
import os
import re


a_     :    List[str]     		=   "src/transformers/models/auto"


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
a_     :    Optional[Any]     		=   re.compile(R"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pattern that matches identifiers in mappings
a_     :    Optional[int]     		=   re.compile(R"\s*\(\s*\"(\S[^\"]+)\"")


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	bool = False						)		->				Optional[int]:
 '''simple docstring'''


 with open(_UpperCamelCase		,				'r'		,				encoding='utf-8'						) as f:
  SCREAMING_SNAKE_CASE		    =      f.read()

 SCREAMING_SNAKE_CASE		    =      content.split('\n'						)
 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE		    =      0
 while line_idx < len(_UpperCamelCase						):
  if _re_intro_mapping.search(lines[line_idx]						) is not None:
   SCREAMING_SNAKE_CASE		    =      len(re.search(R'^(\s*)\S'		,				lines[line_idx]						).groups()[0]						) + 8
   # Start of a new mapping!
   while not lines[line_idx].startswith(' ' * indent + '('						):
    new_lines.append(lines[line_idx]						)
    line_idx += 1

   SCREAMING_SNAKE_CASE		    =      []
   while lines[line_idx].strip() != "]":
    # Blocks either fit in one line or not
    if lines[line_idx].strip() == "(":
     SCREAMING_SNAKE_CASE		    =      line_idx
     while not lines[line_idx].startswith(' ' * indent + ')'						):
      line_idx += 1
     blocks.append('\n'.join(lines[start_idx : line_idx + 1]						)						)
    else:
     blocks.append(lines[line_idx]						)
    line_idx += 1

   # Sort blocks by their identifiers
   SCREAMING_SNAKE_CASE		    =      sorted(_UpperCamelCase		,				key=lambda _UpperCamelCase						: _re_identifier.search(_UpperCamelCase						).groups()[0]						)
   new_lines += blocks
  else:
   new_lines.append(lines[line_idx]						)
   line_idx += 1

 if overwrite:
  with open(_UpperCamelCase		,				'w'		,				encoding='utf-8'						) as f:
   f.write('\n'.join(_UpperCamelCase						)						)
 elif "\n".join(_UpperCamelCase						) != content:
  return True


def 							__lowerCAmelCase     (							_UpperCamelCase		:	bool = False						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      [os.path.join(_UpperCamelCase		,				_UpperCamelCase						) for f in os.listdir(_UpperCamelCase						) if f.endswith('.py'						)]
 SCREAMING_SNAKE_CASE		    =      [sort_auto_mapping(_UpperCamelCase		,				overwrite=_UpperCamelCase						) for fname in fnames]

 if not overwrite and any(_UpperCamelCase						):
  SCREAMING_SNAKE_CASE		    =      [f for f, d in zip(_UpperCamelCase		,				_UpperCamelCase						) if d]
  raise ValueError(
      f"""The following files have auto mappings that need sorting: {', '.join(_UpperCamelCase						)}. Run `make style` to fix"""
      ' this.'						)


if __name__ == "__main__":
      a_     :    Union[str, Any]     		=   argparse.ArgumentParser()
      parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
      a_     :    List[str]     		=   parser.parse_args()

      sort_all_auto_mappings(not args.check_only)

