


import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowercase							:   Dict						=						logging.get_logger("transformers.models.speecht5")





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  List[Any]    )			-> str:





	"""simple docstring"""







	hf_model.apply_weight_norm()

	A             =					checkpoint["""input_conv.weight_g"""]
	A             =					checkpoint["""input_conv.weight_v"""]
	A             =					checkpoint["""input_conv.bias"""]

	for i in range(len(config.upsample_rates    )    ):
		A             =					checkpoint[f'upsamples.{i}.1.weight_g']
		A             =					checkpoint[f'upsamples.{i}.1.weight_v']
		A             =					checkpoint[f'upsamples.{i}.1.bias']

	for i in range(len(config.upsample_rates    ) * len(config.resblock_kernel_sizes    )    ):
		for j in range(len(config.resblock_dilation_sizes    )    ):
			A             =					checkpoint[f'blocks.{i}.convs1.{j}.1.weight_g']
			A             =					checkpoint[f'blocks.{i}.convs1.{j}.1.weight_v']
			A             =					checkpoint[f'blocks.{i}.convs1.{j}.1.bias']

			A             =					checkpoint[f'blocks.{i}.convs2.{j}.1.weight_g']
			A             =					checkpoint[f'blocks.{i}.convs2.{j}.1.weight_v']
			A             =					checkpoint[f'blocks.{i}.convs2.{j}.1.bias']

	A             =					checkpoint["""output_conv.1.weight_g"""]
	A             =					checkpoint["""output_conv.1.weight_v"""]
	A             =					checkpoint["""output_conv.1.bias"""]

	hf_model.remove_weight_norm()





@torch.no_grad()
def 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  Dict				,       UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  Tuple=None				,       UpperCamelCase__:  Optional[int]=None				,       )			-> Union[str, Any]:





	"""simple docstring"""







	if config_path is not None:
		A             =					SpeechTaHifiGanConfig.from_pretrained(UpperCamelCase__    )
	else:
		A             =					SpeechTaHifiGanConfig()

	A             =					SpeechTaHifiGan(UpperCamelCase__    )

	A             =					torch.load(UpperCamelCase__    )
	load_weights(orig_checkpoint["""model"""]["""generator"""]				,       UpperCamelCase__				,       UpperCamelCase__    )

	A             =					np.load(UpperCamelCase__    )
	A             =					stats[0].reshape(-1    )
	A             =					stats[1].reshape(-1    )
	A             =					torch.from_numpy(UpperCamelCase__    ).float()
	A             =					torch.from_numpy(UpperCamelCase__    ).float()

	model.save_pretrained(UpperCamelCase__    )

	if repo_id:
		print("""Pushing to the hub..."""    )
		model.push_to_hub(UpperCamelCase__    )


if __name__ == "__main__":
				_lowercase							:   List[str]						=						argparse.ArgumentParser()
				parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
				parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				parser.add_argument(
				    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
				)

				_lowercase							:   Any						=						parser.parse_args()
				convert_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


_lowercase							:   Optional[int]						=						False






@skip_mps
class    _UpperCamelCase     (       __snake_case     ,		__snake_case     ,		__snake_case     ,		unittest.TestCase					):

				"""simple docstring"""







				lowerCAmelCase					=			StableDiffusionAttendAndExcitePipeline
				lowerCAmelCase					=			False
				lowerCAmelCase					=			TEXT_TO_IMAGE_PARAMS
				lowerCAmelCase					=			TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'}					)
				lowerCAmelCase					=			TEXT_TO_IMAGE_IMAGE_PARAMS
				lowerCAmelCase					=			TEXT_TO_IMAGE_IMAGE_PARAMS

				@classmethod
				def   _UpperCAmelCase       (    cls )						->					List[Any]:
					super().setUpClass()
					torch.use_deterministic_algorithms(a__ )

				@classmethod
				def   _UpperCAmelCase       (    cls )						->					Tuple:
					super().tearDownClass()
					torch.use_deterministic_algorithms(a__ )

				def   _UpperCAmelCase       (    self )						->					Dict:
					torch.manual_seed(0 )
					A             =					UNetaDConditionModel(
					    block_out_channels=(32, 64)					, layers_per_block=1					, sample_size=32					, in_channels=4					, out_channels=4					, down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					, up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					, cross_attention_dim=32					, attention_head_dim=(2, 4)					, use_linear_projection=a__					, )
					A             =					DDIMScheduler(
					    beta_start=0.0_00_85					, beta_end=0.0_12					, beta_schedule="""scaled_linear"""					, clip_sample=a__					, set_alpha_to_one=a__					, )
					torch.manual_seed(0 )
					A             =					AutoencoderKL(
					    block_out_channels=[32, 64]					, in_channels=3					, out_channels=3					, down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]					, up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]					, latent_channels=4					, sample_size=128					, )
					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=1000					, hidden_act="""gelu"""					, projection_dim=512					, )
					A             =					CLIPTextModel(a__ )
					A             =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

					A             =					{
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """vae""": vae,
					    """text_encoder""": text_encoder,
					    """tokenizer""": tokenizer,
					    """safety_checker""": None,
					    """feature_extractor""": None,
					}

					return components

				def   _UpperCAmelCase       (    self					, a__					, a__=0 )						->					Optional[Any]:
					if str(a__ ).startswith("""mps""" ):
						A             =					torch.manual_seed(a__ )
					else:
						A             =					torch.Generator(device=a__ ).manual_seed(a__ )
					A             =					A             =					{
					    """prompt""": """a cat and a frog""",
					    """token_indices""": [2, 5],
					    """generator""": generator,
					    """num_inference_steps""": 1,
					    """guidance_scale""": 6.0,
					    """output_type""": """numpy""",
					    """max_iter_to_alter""": 2,
					    """thresholds""": {0: 0.7},
					}
					return inputs

				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					A             =					"""cpu"""

					A             =					self.get_dummy_components()
					A             =					self.pipeline_class(**a__ )
					pipe.to(a__ )
					pipe.set_progress_bar_config(disable=a__ )

					A             =					self.get_dummy_inputs(a__ )
					A             =					pipe(**a__ ).images
					A             =					image[0, -3:, -3:, -1]

					self.assertEqual(image.shape					, (1, 64, 64, 3) )
					A             =					np.array(
					    [0.63_90_53_64, 0.62_89_73_07, 0.48_59_90_17, 0.5_13_36_24, 0.5_55_00_48, 0.45_76_95_16, 0.50_32_69_73, 0.5_02_31_39, 0.45_38_44_96] )
					A             =					np.abs(image_slice.flatten() - expected_slice ).max()
					self.assertLessEqual(a__					, 1e-3 )

				def   _UpperCAmelCase       (    self )						->					List[Any]:
					super().test_cpu_offload_forward_pass(expected_max_diff=5e-4 )

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

				def   _UpperCAmelCase       (    self )						->					Any:
					self._test_inference_batch_single_identical(batch_size=2					, expected_max_diff=7e-4 )

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )

				def   _UpperCAmelCase       (    self )						->					str:
					super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4 )

				def   _UpperCAmelCase       (    self )						->					int:
					super().test_save_load_local(expected_max_difference=5e-4 )

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					super().test_save_load_optional_components(expected_max_difference=4e-4 )






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

				"""simple docstring"""







				@classmethod
				def   _UpperCAmelCase       (    cls )						->					Tuple:
					super().setUpClass()
					torch.use_deterministic_algorithms(a__ )

				@classmethod
				def   _UpperCAmelCase       (    cls )						->					Dict:
					super().tearDownClass()
					torch.use_deterministic_algorithms(a__ )

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()

				def   _UpperCAmelCase       (    self )						->					int:
					A             =					torch.manual_seed(51 )

					A             =					StableDiffusionAttendAndExcitePipeline.from_pretrained(
					    """CompVis/stable-diffusion-v1-4"""					, safety_checker=a__					, torch_dtype=torch.floataa )
					pipe.to("""cuda""" )

					A             =					"""a painting of an elephant with glasses"""
					A             =					[5, 7]

					A             =					pipe(
					    prompt=a__					, token_indices=a__					, guidance_scale=7.5					, generator=a__					, num_inference_steps=5					, max_iter_to_alter=5					, output_type="""numpy"""					, ).images[0]

					A             =					load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy""" )
					assert np.abs((expected_image - image).max() ) < 5e-1



