import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference



class        lowerCamelCase__				(   UpperCAmelCase       ,unittest.TestCase				):
			UpperCamelCase__    							=ShapEPipeline
			UpperCamelCase__    							=["prompt"]
			UpperCamelCase__    							=["prompt"]
			UpperCamelCase__    							=[
			    "num_images_per_prompt",
			    "num_inference_steps",
			    "generator",
			    "latents",
			    "guidance_scale",
			    "frame_size",
			    "output_type",
			    "return_dict",
			]
			UpperCamelCase__    							=False





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										return 32





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										return 32





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										return self.time_input_dim * 4





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										return 8





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										_lowerCAmelCase							=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)
										return tokenizer





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										torch.manual_seed(0						)
										_lowerCAmelCase							=							CLIPTextConfig(
										    bos_token_id=0  ,  eos_token_id=2  ,  hidden_size=self.text_embedder_hidden_size  ,  projection_dim=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=10_00  ,  )
										return CLIPTextModelWithProjection(lowercase__						)





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										torch.manual_seed(0						)

										_lowerCAmelCase							=							{
										    'num_attention_heads': 2,
										    'attention_head_dim': 16,
										    'embedding_dim': self.time_input_dim,
										    'num_embeddings': 32,
										    'embedding_proj_dim': self.text_embedder_hidden_size,
										    'time_embed_dim': self.time_embed_dim,
										    'num_layers': 1,
										    'clip_embed_dim': self.time_input_dim * 2,
										    'additional_embeddings': 0,
										    'time_embed_act_fn': 'gelu',
										    'norm_in_type': 'layer',
										    'encoder_hid_proj_type': None,
										    'added_emb_type': None,
										}

										_lowerCAmelCase							=							PriorTransformer(**lowercase__						)
										return model





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										torch.manual_seed(0						)

										_lowerCAmelCase							=							{
										    'param_shapes': (
										        (self.renderer_dim, 93),
										        (self.renderer_dim, 8),
										        (self.renderer_dim, 8),
										        (self.renderer_dim, 8),
										    ),
										    'd_latent': self.time_input_dim,
										    'd_hidden': self.renderer_dim,
										    'n_output': 12,
										    'background': (
										        0.1,
										        0.1,
										        0.1,
										    ),
										}
										_lowerCAmelCase							=							ShapERenderer(**lowercase__						)
										return model





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase							=							self.dummy_prior
										_lowerCAmelCase							=							self.dummy_text_encoder
										_lowerCAmelCase							=							self.dummy_tokenizer
										_lowerCAmelCase							=							self.dummy_renderer

										_lowerCAmelCase							=							HeunDiscreteScheduler(
										    beta_schedule='exp'  ,  num_train_timesteps=10_24  ,  prediction_type='sample'  ,  use_karras_sigmas=lowercase__  ,  clip_sample=lowercase__  ,  clip_sample_range=1.0  ,  )
										_lowerCAmelCase							=							{
										    'prior': prior,
										    'text_encoder': text_encoder,
										    'tokenizer': tokenizer,
										    'renderer': renderer,
										    'scheduler': scheduler,
										}

										return components





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]  ,  lowercase__	:	Optional[int]  ,  lowercase__	:	Optional[Any]=0						):
										if str(lowercase__						).startswith('mps'						):
																	_lowerCAmelCase							=							torch.manual_seed(lowercase__						)
										else:
																	_lowerCAmelCase							=							torch.Generator(device=lowercase__						).manual_seed(lowercase__						)
										_lowerCAmelCase							=							{
										    'prompt': 'horse',
										    'generator': generator,
										    'num_inference_steps': 1,
										    'frame_size': 32,
										    'output_type': 'np',
										}
										return inputs





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase							=							'cpu'

										_lowerCAmelCase							=							self.get_dummy_components()

										_lowerCAmelCase							=							self.pipeline_class(**lowercase__						)
										_lowerCAmelCase							=							pipe.to(lowercase__						)

										pipe.set_progress_bar_config(disable=lowercase__						)

										_lowerCAmelCase							=							pipe(**self.get_dummy_inputs(lowercase__						)						)
										_lowerCAmelCase							=							output.images[0]
										_lowerCAmelCase							=							image[0, -3:, -3:, -1]

										assert image.shape == (20, 32, 32, 3)

										_lowerCAmelCase							=							np.array(
										    [
										        0.0_0_0_3_9_2_1_6,
										        0.0_0_0_3_9_2_1_6,
										        0.0_0_0_3_9_2_1_6,
										        0.0_0_0_3_9_2_1_6,
										        0.0_0_0_3_9_2_1_6,
										        0.0_0_0_3_9_2_1_6,
										        0.0_0_0_3_9_2_1_6,
										        0.0_0_0_3_9_2_1_6,
										        0.0_0_0_3_9_2_1_6,
										    ]						)

										assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
										self._test_inference_batch_consistent(batch_sizes=[1, 2]						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							torch_device == 'cpu'
										_lowerCAmelCase							=							True

										self._test_inference_batch_single_identical(
										    batch_size=2  ,  test_max_difference=lowercase__  ,  relax_max_difference=lowercase__  ,  )





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase							=							self.get_dummy_components()
										_lowerCAmelCase							=							self.pipeline_class(**lowercase__						)
										_lowerCAmelCase							=							pipe.to(lowercase__						)
										pipe.set_progress_bar_config(disable=lowercase__						)

										_lowerCAmelCase							=							1
										_lowerCAmelCase							=							2

										_lowerCAmelCase							=							self.get_dummy_inputs(lowercase__						)

										for key in inputs.keys():
																	if key in self.batch_params:
																								_lowerCAmelCase							=							batch_size * [inputs[key]]

										_lowerCAmelCase							=							pipe(**lowercase__  ,  num_images_per_prompt=lowercase__						)[0]

										assert images.shape[0] == batch_size * num_images_per_prompt







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





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/shap_e/test_shap_e_np_out.npy'						)
										_lowerCAmelCase							=							ShapEPipeline.from_pretrained('openai/shap-e'						)
										_lowerCAmelCase							=							pipe.to(lowercase__						)
										pipe.set_progress_bar_config(disable=lowercase__						)

										_lowerCAmelCase							=							torch.Generator(device=lowercase__						).manual_seed(0						)

										_lowerCAmelCase							=							pipe(
										    'a shark'  ,  generator=lowercase__  ,  guidance_scale=1_5.0  ,  num_inference_steps=64  ,  frame_size=64  ,  output_type='np'  ,  ).images[0]

										assert images.shape == (20, 64, 64, 3)

										assert_mean_pixel_difference(lowercase__  ,  lowercase__						)







import argparse
import os
import re


_lowercase:	Optional[Any]       			=		'''src/transformers/models/auto'''


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






def _lowerCamelCase  (		snake_case     , snake_case = False  ):
							with open(snake_case     , 'r'     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							f.read()

							_lowerCAmelCase							=							content.split('\n'  )
							_lowerCAmelCase							=							[]
							_lowerCAmelCase							=							0
							while line_idx < len(snake_case  ):
														if _re_intro_mapping.search(lines[line_idx]  ) is not None:
																					_lowerCAmelCase							=							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

																					_lowerCAmelCase							=							[]
																					while lines[line_idx].strip() != "]":
																												# Blocks either fit in one line or not
																												if lines[line_idx].strip() == "(":
																																			_lowerCAmelCase							=							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
																					_lowerCAmelCase							=							sorted(snake_case     , key=lambda snake_case  : _re_identifier.search(snake_case  ).groups()[0]  )
																					new_lines += blocks
														else:
																					new_lines.append(lines[line_idx]  )
																					line_idx += 1

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






def _lowerCamelCase  (		snake_case = False  ):
							_lowerCAmelCase							=							[os.path.join(snake_case     , snake_case  ) for f in os.listdir(snake_case  ) if f.endswith('.py'  )]
							_lowerCAmelCase							=							[sort_auto_mapping(snake_case     , overwrite=snake_case  ) for fname in fnames]

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


if __name__ == "__main__":
				_lowercase:	Any       			=		argparse.ArgumentParser()
				parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
				_lowercase:	Tuple       			=		parser.parse_args()

				sort_all_auto_mappings(not args.check_only)






