"""simple docstring"""





import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
				from PIL import Image
else:
				class  _snake_case     :






										@staticmethod
										def       lowerCamelCase__     (       *UpperCAmelCase      :			List[str]							,					**UpperCAmelCase      :			Optional[Any]    ):
																	pass


def    lowercase_   (   _lowerCamelCase:	Image      ) ->      str:



							'''simple docstring'''

							__lowerCamelCase					:		Tuple		   =     hashlib.mda(image.tobytes()      )
							return m.hexdigest()[:10]


def    lowercase_   (   _lowerCamelCase:	Image      ) ->      Dict:



							'''simple docstring'''

							__lowerCamelCase					:		Optional[Any]		   =     np.array(_lowerCamelCase      )
							__lowerCamelCase					:		str		   =     npimg.shape
							return {"hash": hashimage(_lowerCamelCase      ), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class  _snake_case     (					unittest.TestCase		):
						snake_case__			  =			dict(
						    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if MODEL_FOR_MASK_GENERATION_MAPPING else [])		)
						snake_case__			  =			dict(
						    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])		)






						def       lowerCamelCase__     (       self      :			Any							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Union[str, Any]    ):
													__lowerCamelCase					:		Any		   =     MaskGenerationPipeline(model=UpperCAmelCase							,					image_processor=UpperCAmelCase    )
													return image_segmenter, [
													    "./tests/fixtures/tests_samples/COCO/000000039769.png",
													    "./tests/fixtures/tests_samples/COCO/000000039769.png",
													]






						def       lowerCamelCase__     (       self      :			Any							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			int    ):
													pass






						@require_tf
						@unittest.skip("Image segmentation not implemented in TF"    )
						def       lowerCamelCase__     (       self      :			Tuple    ):
													pass






						@slow
						@require_torch
						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Tuple		   =     pipeline("mask-generation"							,					model="facebook/sam-vit-huge"    )

													__lowerCamelCase					:		str		   =     image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"							,					points_per_batch=256    )

													# Shortening by hashing
													__lowerCamelCase					:		str		   =     []
													for i, o in enumerate(outputs["masks"]    ):
																				new_outupt += [{"mask": mask_to_test_readable(UpperCAmelCase    ), "scores": outputs["scores"][i]}]

													# fmt: off
													self.assertEqual(
													    nested_simplify(UpperCAmelCase							,					decimals=4    )							,					[
													        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
													        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1},
													        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
													        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
													        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
													        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.9_9_6_7},
													        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_9_3},
													        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.9_9_0_9},
													        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.9_8_7_9},
													        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.9_8_3_4},
													        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.9_7_1_6},
													        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.9_6_1_2},
													        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.9_5_9_9},
													        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.9_5_5_2},
													        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.9_5_3_2},
													        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.9_5_1_6},
													        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.9_4_9_9},
													        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.9_4_8_3},
													        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.9_4_6_4},
													        {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_4_3},
													        {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_4_3},
													        {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.9_4_0_8},
													        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.9_3_3_5},
													        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.9_3_2_6},
													        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.9_2_6_2},
													        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.8_9_9_9},
													        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.8_9_8_6},
													        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.8_9_8_4},
													        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.8_8_7_3},
													        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.8_8_7_1}
													    ]							,					)
													# fmt: on







						@require_torch
						@slow
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		Tuple		   =     "facebook/sam-vit-huge"
													__lowerCamelCase					:		Any		   =     pipeline("mask-generation"							,					model=UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     image_segmenter(
													    "http://images.cocodataset.org/val2017/000000039769.jpg"							,					pred_iou_thresh=1							,					points_per_batch=256    )

													# Shortening by hashing
													__lowerCamelCase					:		int		   =     []
													for i, o in enumerate(outputs["masks"]    ):
																				new_outupt += [{"mask": mask_to_test_readable(UpperCAmelCase    ), "scores": outputs["scores"][i]}]

													self.assertEqual(
													    nested_simplify(UpperCAmelCase							,					decimals=4    )							,					[
													        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
													        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1_0},
													        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
													        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
													        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
													    ]							,					)
"""simple docstring"""





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__A  =				logging.get_logger('''transformers.models.speecht5''')


def    lowercase_   (   _lowerCamelCase:	Any						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Tuple      ) ->      Union[str, Any]:



							'''simple docstring'''

							hf_model.apply_weight_norm()

							__lowerCamelCase					:		Union[str, Any]		   =     checkpoint["input_conv.weight_g"]
							__lowerCamelCase					:		int		   =     checkpoint["input_conv.weight_v"]
							__lowerCamelCase					:		Dict		   =     checkpoint["input_conv.bias"]

							for i in range(len(config.upsample_rates      )      ):
														__lowerCamelCase					:		Dict		   =     checkpoint[F"""upsamples.{i}.1.weight_g"""]
														__lowerCamelCase					:		Optional[int]		   =     checkpoint[F"""upsamples.{i}.1.weight_v"""]
														__lowerCamelCase					:		Any		   =     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      )      ):
																					__lowerCamelCase					:		List[str]		   =     checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
																					__lowerCamelCase					:		str		   =     checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
																					__lowerCamelCase					:		Union[str, Any]		   =     checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

																					__lowerCamelCase					:		str		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
																					__lowerCamelCase					:		int		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
																					__lowerCamelCase					:		int		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

							__lowerCamelCase					:		Any		   =     checkpoint["output_conv.1.weight_g"]
							__lowerCamelCase					:		Tuple		   =     checkpoint["output_conv.1.weight_v"]
							__lowerCamelCase					:		int		   =     checkpoint["output_conv.1.bias"]

							hf_model.remove_weight_norm()


@torch.no_grad()
def    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	Any=None						,   _lowerCamelCase:	Optional[int]=None						,   ) ->      Optional[int]:



							'''simple docstring'''

							if config_path is not None:
														__lowerCamelCase					:		Dict		   =     SpeechTaHifiGanConfig.from_pretrained(_lowerCamelCase      )
							else:
														__lowerCamelCase					:		Tuple		   =     SpeechTaHifiGanConfig()

							__lowerCamelCase					:		List[Any]		   =     SpeechTaHifiGan(_lowerCamelCase      )

							__lowerCamelCase					:		int		   =     torch.load(_lowerCamelCase      )
							load_weights(orig_checkpoint["model"]["generator"]						,   _lowerCamelCase						,   _lowerCamelCase      )

							__lowerCamelCase					:		Dict		   =     np.load(_lowerCamelCase      )
							__lowerCamelCase					:		List[str]		   =     stats[0].reshape(-1      )
							__lowerCamelCase					:		Optional[int]		   =     stats[1].reshape(-1      )
							__lowerCamelCase					:		int		   =     torch.from_numpy(_lowerCamelCase      ).float()
							__lowerCamelCase					:		List[str]		   =     torch.from_numpy(_lowerCamelCase      ).float()

							model.save_pretrained(_lowerCamelCase      )

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


if __name__ == "__main__":
				__A  =				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.'''
				)

				__A  =				parser.parse_args()
				convert_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)