
"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 	_lowercase       (    __UpperCAmelCase    ,	unittest.TestCase		):
	lowercase_				=     KandinskyVaaImgaImgPipeline
	lowercase_				=     ['image_embeds', 'negative_image_embeds', 'image']
	lowercase_				=     [
	    'image_embeds',
	    'negative_image_embeds',
	    'image',
	]
	lowercase_				=     [
	    'generator',
	    'height',
	    'width',
	    'strength',
	    'guidance_scale',
	    'num_inference_steps',
	    'return_dict',
	    'guidance_scale',
	    'num_images_per_prompt',
	    'output_type',
	    'return_dict',
	]
	lowercase_				=     False




	@property
	def       _UpperCamelCase   (			self )		->							List[Any]:
			return 32




	@property
	def       _UpperCamelCase   (			self )		->							int:
			return 32




	@property
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			return self.time_input_dim




	@property
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			return self.time_input_dim * 4




	@property
	def       _UpperCamelCase   (			self )		->							Dict:
			return 100




	@property
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			torch.manual_seed(0 )

			lowerCamelCase					:      str          =   {
			    'in_channels': 4,
			    # Out channels is double in channels because predicts mean and variance
			    'out_channels': 8,
			    'addition_embed_type': 'image',
			    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
			    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
			    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
			    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
			    'layers_per_block': 1,
			    'encoder_hid_dim': self.text_embedder_hidden_size,
			    'encoder_hid_dim_type': 'image_proj',
			    'cross_attention_dim': self.cross_attention_dim,
			    'attention_head_dim': 4,
			    'resnet_time_scale_shift': 'scale_shift',
			    'class_embed_type': None,
			}

			lowerCamelCase					:      Optional[int]          =   UNetaDConditionModel(**UpperCAmelCase_ )
			return model




	@property
	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			return {
			    "block_out_channels": [32, 64],
			    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
			    "in_channels": 3,
			    "latent_channels": 4,
			    "layers_per_block": 1,
			    "norm_num_groups": 8,
			    "norm_type": "spatial",
			    "num_vq_embeddings": 12,
			    "out_channels": 3,
			    "up_block_types": [
			        "AttnUpDecoderBlock2D",
			        "UpDecoderBlock2D",
			    ],
			    "vq_embed_dim": 4,
			}




	@property
	def       _UpperCamelCase   (			self )		->							Any:
			torch.manual_seed(0 )
			lowerCamelCase					:      str          =   VQModel(**self.dummy_movq_kwargs )
			return model




	def       _UpperCamelCase   (			self )		->							Dict:
			lowerCamelCase					:      Optional[Any]          =   self.dummy_unet
			lowerCamelCase					:      int          =   self.dummy_movq

			lowerCamelCase					:      Dict          =   {
			    'num_train_timesteps': 1000,
			    'beta_schedule': 'linear',
			    'beta_start': 0.00085,
			    'beta_end': 0.012,
			    'clip_sample': False,
			    'set_alpha_to_one': False,
			    'steps_offset': 0,
			    'prediction_type': 'epsilon',
			    'thresholding': False,
			}

			lowerCamelCase					:      Tuple          =   DDIMScheduler(**UpperCAmelCase_ )

			lowerCamelCase					:      Dict          =   {
			    'unet': unet,
			    'scheduler': scheduler,
			    'movq': movq,
			}

			return components




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=0 )		->							Dict:
			lowerCamelCase					:      Union[str, Any]          =   floats_tensor((1, self.text_embedder_hidden_size)		,			rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
			lowerCamelCase					:      int          =   floats_tensor((1, self.text_embedder_hidden_size)		,			rng=random.Random(seed + 1 ) ).to(
			    UpperCAmelCase_ )
			# create init_image
			lowerCamelCase					:      Tuple          =   floats_tensor((1, 3, 64, 64)		,			rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
			lowerCamelCase					:      Dict          =   image.cpu().permute(0		,			2		,			3		,			1 )[0]
			lowerCamelCase					:      Optional[int]          =   Image.fromarray(np.uinta(UpperCAmelCase_ ) ).convert('RGB' ).resize((256, 256) )

			if str(UpperCAmelCase_ ).startswith('mps' ):
					lowerCamelCase					:      Dict          =   torch.manual_seed(UpperCAmelCase_ )
			else:
					lowerCamelCase					:      int          =   torch.Generator(device=UpperCAmelCase_ ).manual_seed(UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   {
			    'image': init_image,
			    'image_embeds': image_embeds,
			    'negative_image_embeds': negative_image_embeds,
			    'generator': generator,
			    'height': 64,
			    'width': 64,
			    'num_inference_steps': 10,
			    'guidance_scale': 7.0,
			    'strength': 0.2,
			    'output_type': 'np',
			}
			return inputs




	def       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      Optional[int]          =   'cpu'

			lowerCamelCase					:      int          =   self.get_dummy_components()

			lowerCamelCase					:      Optional[int]          =   self.pipeline_class(**UpperCAmelCase_ )
			lowerCamelCase					:      Union[str, Any]          =   pipe.to(UpperCAmelCase_ )

			pipe.set_progress_bar_config(disable=UpperCAmelCase_ )

			lowerCamelCase					:      List[str]          =   pipe(**self.get_dummy_inputs(UpperCAmelCase_ ) )
			lowerCamelCase					:      int          =   output.images

			lowerCamelCase					:      Optional[Any]          =   pipe(
			    **self.get_dummy_inputs(UpperCAmelCase_ )		,			return_dict=UpperCAmelCase_		,			)[0]

			lowerCamelCase					:      Tuple          =   image[0, -3:, -3:, -1]
			lowerCamelCase					:      List[str]          =   image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)

			lowerCamelCase					:      Any          =   np.array(
			    [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263] )
			assert (
			    np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
			), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
			assert (
			    np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
			), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""






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




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Tuple          =   load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/kandinskyv22/kandinskyv22_img2img_frog.npy' )

			lowerCamelCase					:      Dict          =   load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
			lowerCamelCase					:      Optional[Any]          =   'A red cartoon frog, 4k'

			lowerCamelCase					:      str          =   KandinskyVaaPriorPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-prior'		,			torch_dtype=torch.floataa )
			pipe_prior.to(UpperCAmelCase_ )

			lowerCamelCase					:      int          =   KandinskyVaaImgaImgPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-decoder'		,			torch_dtype=torch.floataa )
			lowerCamelCase					:      Tuple          =   pipeline.to(UpperCAmelCase_ )

			pipeline.set_progress_bar_config(disable=UpperCAmelCase_ )

			lowerCamelCase					:      List[Any]          =   torch.Generator(device='cpu' ).manual_seed(0 )
			lowerCamelCase     ,   lowerCamelCase					:      Tuple          =   pipe_prior(
			    UpperCAmelCase_		,			generator=UpperCAmelCase_		,			num_inference_steps=5		,			negative_prompt=''		,			).to_tuple()

			lowerCamelCase					:      Any          =   pipeline(
			    image=UpperCAmelCase_		,			image_embeds=UpperCAmelCase_		,			negative_image_embeds=UpperCAmelCase_		,			generator=UpperCAmelCase_		,			num_inference_steps=100		,			height=768		,			width=768		,			strength=0.2		,			output_type='np'		,			)

			lowerCamelCase					:      List[Any]          =   output.images[0]

			assert image.shape == (768, 768, 3)

			assert_mean_pixel_difference(UpperCAmelCase_		,			UpperCAmelCase_ )








"""simple docstring"""

import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


_A						=			{
    'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt',
    'tiny': 'https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt',
    'base.en': 'https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt',
    'base': 'https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt',
    'small.en': 'https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt',
    'small': 'https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt',
    'medium.en': 'https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt',
    'medium': 'https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt',
    'large': 'https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt',
    'large-v2': 'https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt',
}
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      str          =   ['layers', 'blocks']
		for k in ignore_keys:
				state_dict.pop(a_,						a_      )


_A						=			{
    'blocks': 'layers',
    'mlp.0': 'fc1',
    'mlp.2': 'fc2',
    'mlp_ln': 'final_layer_norm',
    '.attn.query': '.self_attn.q_proj',
    '.attn.key': '.self_attn.k_proj',
    '.attn.value': '.self_attn.v_proj',
    '.attn_ln': '.self_attn_layer_norm',
    '.attn.out': '.self_attn.out_proj',
    '.cross_attn.query': '.encoder_attn.q_proj',
    '.cross_attn.key': '.encoder_attn.k_proj',
    '.cross_attn.value': '.encoder_attn.v_proj',
    '.cross_attn_ln': '.encoder_attn_layer_norm',
    '.cross_attn.out': '.encoder_attn.out_proj',
    'decoder.ln.': 'decoder.layer_norm.',
    'encoder.ln.': 'encoder.layer_norm.',
    'token_embedding': 'embed_tokens',
    'encoder.positional_embedding': 'encoder.embed_positions.weight',
    'decoder.positional_embedding': 'decoder.embed_positions.weight',
    'ln_post': 'layer_norm',
}
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Tuple          =   list(s_dict.keys()      )
		for key in keys:
				lowerCamelCase					:      List[Any]          =   key
				for k, v in WHISPER_MAPPING.items():
						if k in key:
								lowerCamelCase					:      Optional[int]          =   new_key.replace(a_,						a_      )

				print(F"""{key} -> {new_key}"""      )

				lowerCamelCase					:      Any          =   s_dict.pop(a_      )
		return s_dict
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase     ,   lowerCamelCase					:      int          =   emb.weight.shape
		lowerCamelCase					:      Dict          =   nn.Linear(a_,						a_,						bias=a_      )
		lowerCamelCase					:      Union[str, Any]          =   emb.weight.data
		return lin_layer
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		os.makedirs(a_,						exist_ok=a_      )
		lowerCamelCase					:      Union[str, Any]          =   os.path.basename(a_      )

		lowerCamelCase					:      Any          =   url.split('/'      )[-2]
		lowerCamelCase					:      Tuple          =   os.path.join(a_,						a_      )

		if os.path.exists(a_      ) and not os.path.isfile(a_      ):
				raise RuntimeError(F"""{download_target} exists and is not a regular file"""      )

		if os.path.isfile(a_      ):
				lowerCamelCase					:      Union[str, Any]          =   open(a_,						'rb'      ).read()
				if hashlib.shaaaa(a_      ).hexdigest() == expected_shaaaa:
						return model_bytes
				else:
						warnings.warn(F"""{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"""      )

		with urllib.request.urlopen(a_      ) as source, open(a_,						'wb'      ) as output:
				with tqdm(
				    total=int(source.info().get('Content-Length'      )      ),						ncols=80,						unit='iB',						unit_scale=a_,						unit_divisor=1024      ) as loop:
						while True:
								lowerCamelCase					:      Union[str, Any]          =   source.read(8192      )
								if not buffer:
										break

								output.write(a_      )
								loop.update(len(a_      )      )

		lowerCamelCase					:      int          =   open(a_,						'rb'      ).read()
		if hashlib.shaaaa(a_      ).hexdigest() != expected_shaaaa:
				raise RuntimeError(
				    'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.'      )

		return model_bytes
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		if ".pt" not in checkpoint_path:
				lowerCamelCase					:      str          =   _download(_MODELS[checkpoint_path]      )
		else:
				lowerCamelCase					:      Any          =   torch.load(a_,						map_location='cpu'      )
		lowerCamelCase					:      List[str]          =   original_checkpoint['dims']
		lowerCamelCase					:      Any          =   original_checkpoint['model_state_dict']
		lowerCamelCase					:      Tuple          =   state_dict['decoder.token_embedding.weight']
		remove_ignore_keys_(a_      )
		rename_keys(a_      )
		lowerCamelCase					:      List[Any]          =   True
		lowerCamelCase					:      str          =   state_dict['decoder.layers.0.fc1.weight'].shape[0]

		lowerCamelCase					:      Optional[int]          =   WhisperConfig(
		    vocab_size=dimensions['n_vocab'],						encoder_ffn_dim=a_,						decoder_ffn_dim=a_,						num_mel_bins=dimensions['n_mels'],						d_model=dimensions['n_audio_state'],						max_target_positions=dimensions['n_text_ctx'],						encoder_layers=dimensions['n_audio_layer'],						encoder_attention_heads=dimensions['n_audio_head'],						decoder_layers=dimensions['n_text_layer'],						decoder_attention_heads=dimensions['n_text_state'],						max_source_positions=dimensions['n_audio_ctx'],						)

		lowerCamelCase					:      Union[str, Any]          =   WhisperForConditionalGeneration(a_      )
		lowerCamelCase     ,   lowerCamelCase					:      Optional[int]          =   model.model.load_state_dict(a_,						strict=a_      )
		if len(a_      ) > 0 and not set(a_      ) <= {
		    "encoder.embed_positions.weights",
		    "decoder.embed_positions.weights",
		}:
				raise ValueError(
				    'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
				    F""" but all the following weights are missing {missing}"""      )

		if tie_embeds:
				lowerCamelCase					:      List[Any]          =   make_linear_from_emb(model.model.decoder.embed_tokens      )
		else:
				lowerCamelCase					:      Tuple          =   proj_out_weights

		model.save_pretrained(a_      )


if __name__ == "__main__":
					_A						=			argparse.ArgumentParser()
					# # Required parameters
					parser.add_argument('--checkpoint_path', type=str, help='Patht to the downloaded checkpoints')
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					_A						=			parser.parse_args()

					convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)






