import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
    import jax
    import jax.numpy as jnp
    from flax.jax_utils import replicate
    from flax.training.common_utils import shard

    from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline


@require_flax
class      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            # pipeline has Flax weights
            __UpperCamelCase					:List[str]        =		FlaxDiffusionPipeline.from_pretrained(
                '''hf-internal-testing/tiny-stable-diffusion-pipe'''			,			safety_checker=__lowercase			,			cache_dir=__lowercase)

            __UpperCamelCase					:int        =		[t[-1] for t in os.walk(os.path.join(__lowercase			,			os.listdir(__lowercase)[0]			,			'''snapshots'''))]
            __UpperCamelCase					:Any        =		[item for sublist in all_root_files for item in sublist]

            # None of the downloaded files should be a PyTorch file even if we have some here:
            # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
            assert not any(f.endswith('''.bin''') for f in files)


@slow
@require_flax
class      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase  ,	__UpperCamelCase					:int        =		FlaxStableDiffusionPipeline.from_pretrained(
            '''hf-internal-testing/tiny-stable-diffusion-pipe'''			,			safety_checker=__lowercase)

        __UpperCamelCase					:Any        =		(
            '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
            ''' field, close up, split lighting, cinematic'''
        )

        __UpperCamelCase					:Any        =		jax.random.PRNGKey(0)
        __UpperCamelCase					:Dict        =		4

        __UpperCamelCase					:int        =		jax.device_count()
        __UpperCamelCase					:Any        =		num_samples * [prompt]
        __UpperCamelCase					:List[str]        =		pipeline.prepare_inputs(__lowercase)

        # shard inputs and rng
        __UpperCamelCase					:Union[str, Any]        =		replicate(__lowercase)
        __UpperCamelCase					:str        =		jax.random.split(__lowercase			,			__lowercase)
        __UpperCamelCase					:int        =		shard(__lowercase)

        __UpperCamelCase					:str        =		pipeline(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			jit=__lowercase).images

        assert images.shape == (num_samples, 1, 64, 64, 3)
        if jax.device_count() == 8:
            assert np.abs(np.abs(images[0, 0, :2, :2, -2:]			,			dtype=np.floataa).sum() - 4.1_51_47_45) < 1E-3
            assert np.abs(np.abs(__lowercase			,			dtype=np.floataa).sum() - 4_99_47.8_75) < 5E-1

        __UpperCamelCase					:Union[str, Any]        =		pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
        assert len(__lowercase) == num_samples







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase  ,	__UpperCamelCase					:Optional[int]        =		FlaxStableDiffusionPipeline.from_pretrained(
            '''CompVis/stable-diffusion-v1-4'''			,			revision='''flax'''			,			safety_checker=__lowercase)

        __UpperCamelCase					:Optional[Any]        =		(
            '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
            ''' field, close up, split lighting, cinematic'''
        )

        __UpperCamelCase					:Any        =		jax.random.PRNGKey(0)
        __UpperCamelCase					:Union[str, Any]        =		50

        __UpperCamelCase					:Union[str, Any]        =		jax.device_count()
        __UpperCamelCase					:int        =		num_samples * [prompt]
        __UpperCamelCase					:Dict        =		pipeline.prepare_inputs(__lowercase)

        # shard inputs and rng
        __UpperCamelCase					:Any        =		replicate(__lowercase)
        __UpperCamelCase					:Optional[Any]        =		jax.random.split(__lowercase			,			__lowercase)
        __UpperCamelCase					:str        =		shard(__lowercase)

        __UpperCamelCase					:Optional[int]        =		pipeline(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			jit=__lowercase).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:]			,			dtype=np.floataa).sum() - 0.05_65_24_01)) < 1E-3
            assert np.abs((np.abs(__lowercase			,			dtype=np.floataa).sum() - 2_38_38_08.2)) < 5E-1







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase  ,	__UpperCamelCase					:Any        =		FlaxStableDiffusionPipeline.from_pretrained(
            '''CompVis/stable-diffusion-v1-4'''			,			revision='''bf16'''			,			dtype=jnp.bfloataa			,			safety_checker=__lowercase)

        __UpperCamelCase					:Optional[int]        =		(
            '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
            ''' field, close up, split lighting, cinematic'''
        )

        __UpperCamelCase					:Union[str, Any]        =		jax.random.PRNGKey(0)
        __UpperCamelCase					:Optional[int]        =		50

        __UpperCamelCase					:Optional[int]        =		jax.device_count()
        __UpperCamelCase					:int        =		num_samples * [prompt]
        __UpperCamelCase					:str        =		pipeline.prepare_inputs(__lowercase)

        # shard inputs and rng
        __UpperCamelCase					:Dict        =		replicate(__lowercase)
        __UpperCamelCase					:Optional[int]        =		jax.random.split(__lowercase			,			__lowercase)
        __UpperCamelCase					:List[str]        =		shard(__lowercase)

        __UpperCamelCase					:int        =		pipeline(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			jit=__lowercase).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:]			,			dtype=np.floataa).sum() - 0.04_00_39_06)) < 1E-3
            assert np.abs((np.abs(__lowercase			,			dtype=np.floataa).sum() - 2_37_35_16.75)) < 5E-1







    def       UpperCamelCase__       (     self)	->     str:
        __UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		FlaxStableDiffusionPipeline.from_pretrained(
            '''CompVis/stable-diffusion-v1-4'''			,			revision='''bf16'''			,			dtype=jnp.bfloataa)

        __UpperCamelCase					:Any        =		(
            '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
            ''' field, close up, split lighting, cinematic'''
        )

        __UpperCamelCase					:Any        =		jax.random.PRNGKey(0)
        __UpperCamelCase					:Dict        =		50

        __UpperCamelCase					:Union[str, Any]        =		jax.device_count()
        __UpperCamelCase					:str        =		num_samples * [prompt]
        __UpperCamelCase					:Tuple        =		pipeline.prepare_inputs(__lowercase)

        # shard inputs and rng
        __UpperCamelCase					:Optional[int]        =		replicate(__lowercase)
        __UpperCamelCase					:int        =		jax.random.split(__lowercase			,			__lowercase)
        __UpperCamelCase					:List[str]        =		shard(__lowercase)

        __UpperCamelCase					:int        =		pipeline(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			jit=__lowercase).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:]			,			dtype=np.floataa).sum() - 0.04_00_39_06)) < 1E-3
            assert np.abs((np.abs(__lowercase			,			dtype=np.floataa).sum() - 2_37_35_16.75)) < 5E-1







    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:List[str]        =		FlaxDDIMScheduler(
            beta_start=0.0_00_85			,			beta_end=0.0_12			,			beta_schedule='''scaled_linear'''			,			set_alpha_to_one=__lowercase			,			steps_offset=1			,			)

        __UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		FlaxStableDiffusionPipeline.from_pretrained(
            '''CompVis/stable-diffusion-v1-4'''			,			revision='''bf16'''			,			dtype=jnp.bfloataa			,			scheduler=__lowercase			,			safety_checker=__lowercase			,			)
        __UpperCamelCase					:Dict        =		scheduler.create_state()

        __UpperCamelCase					:Optional[Any]        =		scheduler_state

        __UpperCamelCase					:List[str]        =		(
            '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
            ''' field, close up, split lighting, cinematic'''
        )

        __UpperCamelCase					:int        =		jax.random.PRNGKey(0)
        __UpperCamelCase					:List[Any]        =		50

        __UpperCamelCase					:Optional[int]        =		jax.device_count()
        __UpperCamelCase					:int        =		num_samples * [prompt]
        __UpperCamelCase					:Dict        =		pipeline.prepare_inputs(__lowercase)

        # shard inputs and rng
        __UpperCamelCase					:Dict        =		replicate(__lowercase)
        __UpperCamelCase					:Optional[int]        =		jax.random.split(__lowercase			,			__lowercase)
        __UpperCamelCase					:List[str]        =		shard(__lowercase)

        __UpperCamelCase					:Optional[Any]        =		pipeline(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			jit=__lowercase).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
            assert np.abs((np.abs(images[0, 0, :2, :2, -2:]			,			dtype=np.floataa).sum() - 0.0_45_04_39_45)) < 1E-3
            assert np.abs((np.abs(__lowercase			,			dtype=np.floataa).sum() - 2_34_76_93.5)) < 5E-1







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase					:int        =		(
            '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
            ''' field, close up, split lighting, cinematic'''
        )

        __UpperCamelCase					:Any        =		jax.device_count()
        __UpperCamelCase					:Dict        =		num_samples * [prompt]
        __UpperCamelCase					:List[Any]        =		jax.random.split(jax.random.PRNGKey(0)			,			__lowercase)

        __UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		FlaxStableDiffusionPipeline.from_pretrained(
            '''CompVis/stable-diffusion-v1-4'''			,			revision='''bf16'''			,			dtype=jnp.bfloataa			,			safety_checker=__lowercase			,			)

        __UpperCamelCase					:str        =		replicate(__lowercase)
        __UpperCamelCase					:str        =		pipeline.prepare_inputs(__lowercase)
        __UpperCamelCase					:Union[str, Any]        =		shard(__lowercase)
        __UpperCamelCase					:Union[str, Any]        =		pipeline(__lowercase			,			__lowercase			,			__lowercase			,			jit=__lowercase).images
        assert images.shape == (num_samples, 1, 512, 512, 3)
        __UpperCamelCase					:List[Any]        =		images[2, 0, 256, 10:17, 1]

        # With memory efficient attention
        __UpperCamelCase  ,	__UpperCamelCase					:Union[str, Any]        =		FlaxStableDiffusionPipeline.from_pretrained(
            '''CompVis/stable-diffusion-v1-4'''			,			revision='''bf16'''			,			dtype=jnp.bfloataa			,			safety_checker=__lowercase			,			use_memory_efficient_attention=__lowercase			,			)

        __UpperCamelCase					:Optional[int]        =		replicate(__lowercase)
        __UpperCamelCase					:List[str]        =		pipeline.prepare_inputs(__lowercase)
        __UpperCamelCase					:Any        =		shard(__lowercase)
        __UpperCamelCase					:Any        =		pipeline(__lowercase			,			__lowercase			,			__lowercase			,			jit=__lowercase).images
        assert images_eff.shape == (num_samples, 1, 512, 512, 3)
        __UpperCamelCase					:List[str]        =		images[2, 0, 256, 10:17, 1]

        # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
        # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
        assert abs(slice_eff - slice).max() < 1E-2






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


__lowercase										= {
    '''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 lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:int        =		['''layers''', '''blocks''']
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )


__lowercase										= {
    '''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 lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[str]        =		list(s_dict.keys()     )
    for key in keys:
        __UpperCamelCase					:Dict        =		key
        for k, v in WHISPER_MAPPING.items():
            if k in key:
                __UpperCamelCase					:str        =		new_key.replace(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

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

        __UpperCamelCase					:Any        =		s_dict.pop(SCREAMING_SNAKE_CASE     )
    return s_dict



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		emb.weight.shape
    __UpperCamelCase					:Any        =		nn.Linear(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      bias=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:str        =		emb.weight.data
    return lin_layer



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    os.makedirs(SCREAMING_SNAKE_CASE  ,      exist_ok=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:int        =		os.path.basename(SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Optional[Any]        =		url.split('''/'''     )[-2]
    __UpperCamelCase					:Tuple        =		os.path.join(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

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

    if os.path.isfile(SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase					:List[str]        =		open(SCREAMING_SNAKE_CASE  ,      '''rb'''     ).read()
        if hashlib.shaaaa(SCREAMING_SNAKE_CASE     ).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(SCREAMING_SNAKE_CASE     ) as source, open(SCREAMING_SNAKE_CASE  ,      '''wb'''     ) as output:
        with tqdm(
            total=int(source.info().get('''Content-Length'''     )     )  ,      ncols=80  ,      unit='''iB'''  ,      unit_scale=SCREAMING_SNAKE_CASE  ,      unit_divisor=1_024     ) as loop:
            while True:
                __UpperCamelCase					:Optional[Any]        =		source.read(8_192     )
                if not buffer:
                    break

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

    __UpperCamelCase					:str        =		open(SCREAMING_SNAKE_CASE  ,      '''rb'''     ).read()
    if hashlib.shaaaa(SCREAMING_SNAKE_CASE     ).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 lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







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

    __UpperCamelCase					:Dict        =		WhisperConfig(
        vocab_size=dimensions['''n_vocab''']  ,      encoder_ffn_dim=SCREAMING_SNAKE_CASE  ,      decoder_ffn_dim=SCREAMING_SNAKE_CASE  ,      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''']  ,      )

    __UpperCamelCase					:str        =		WhisperForConditionalGeneration(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase  ,	__UpperCamelCase					:Any        =		model.model.load_state_dict(SCREAMING_SNAKE_CASE  ,      strict=SCREAMING_SNAKE_CASE     )
    if len(SCREAMING_SNAKE_CASE     ) > 0 and not set(SCREAMING_SNAKE_CASE     ) <= {
        "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:
        __UpperCamelCase					:Optional[Any]        =		make_linear_from_emb(model.model.decoder.embed_tokens     )
    else:
        __UpperCamelCase					:Union[str, Any]        =		proj_out_weights

    model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
    __lowercase										= 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.''')
    __lowercase										= parser.parse_args()

    convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)





