import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






  @property
  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> Tuple:




    '''simple docstring'''




    torch.manual_seed(0   )
    __UpperCAmelCase						    =       UNetaDModel(
        block_out_channels=(32, 64)				,				layers_per_block=2				,				sample_size=32				,				in_channels=3				,				out_channels=3				,				down_block_types=("DownBlock2D", "AttnDownBlock2D")				,				up_block_types=("AttnUpBlock2D", "UpBlock2D")				,				)
    return model






  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> str:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.dummy_uncond_unet
    __UpperCAmelCase						    =       PNDMScheduler()

    __UpperCAmelCase						    =       PNDMPipeline(unet=__lowerCAmelCase				,				scheduler=__lowerCAmelCase   )
    pndm.to(__lowerCAmelCase   )
    pndm.set_progress_bar_config(disable=__lowerCAmelCase   )

    __UpperCAmelCase						    =       torch.manual_seed(0   )
    __UpperCAmelCase						    =       pndm(generator=__lowerCAmelCase				,				num_inference_steps=20				,				output_type="numpy"   ).images

    __UpperCAmelCase						    =       torch.manual_seed(0   )
    __UpperCAmelCase						    =       pndm(generator=__lowerCAmelCase				,				num_inference_steps=20				,				output_type="numpy"				,				return_dict=__lowerCAmelCase   )[0]

    __UpperCAmelCase						    =       image[0, -3:, -3:, -1]
    __UpperCAmelCase						    =       image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __UpperCAmelCase						    =       np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

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






@slow
@require_torch
class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       "google/ddpm-cifar10-32"

    __UpperCAmelCase						    =       UNetaDModel.from_pretrained(__lowerCAmelCase   )
    __UpperCAmelCase						    =       PNDMScheduler()

    __UpperCAmelCase						    =       PNDMPipeline(unet=__lowerCAmelCase				,				scheduler=__lowerCAmelCase   )
    pndm.to(__lowerCAmelCase   )
    pndm.set_progress_bar_config(disable=__lowerCAmelCase   )
    __UpperCAmelCase						    =       torch.manual_seed(0   )
    __UpperCAmelCase						    =       pndm(generator=__lowerCAmelCase				,				output_type="numpy"   ).images

    __UpperCAmelCase						    =       image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __UpperCAmelCase						    =       np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]   )

    assert np.abs(image_slice.flatten() - expected_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


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       __lowerCAmelCase      (						A_   : Optional[int]					)    ->  str:
  __UpperCAmelCase						    =       ["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       __lowerCAmelCase      (						A_   : str					)    ->  List[str]:
  __UpperCAmelCase						    =       list(s_dict.keys()					)
  for key in keys:
    __UpperCAmelCase						    =       key
    for k, v in WHISPER_MAPPING.items():
      if k in key:
        __UpperCAmelCase						    =       new_key.replace(A_						,	A_					)

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

    __UpperCAmelCase						    =       s_dict.pop(A_					)
  return s_dict




def       __lowerCAmelCase      (						A_   : List[Any]					)    ->  List[Any]:
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       emb.weight.shape
  __UpperCAmelCase						    =       nn.Linear(A_						,	A_						,	bias=A_					)
  __UpperCAmelCase						    =       emb.weight.data
  return lin_layer




def       __lowerCAmelCase      (						A_   : str						,	A_   : str					)    ->  bytes:
  os.makedirs(A_						,	exist_ok=A_					)
  __UpperCAmelCase						    =       os.path.basename(A_					)

  __UpperCAmelCase						    =       url.split("/"					)[-2]
  __UpperCAmelCase						    =       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_					):
    __UpperCAmelCase						    =       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=10_24					) as loop:
      while True:
        __UpperCAmelCase						    =       source.read(81_92					)
        if not buffer:
          break

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

  __UpperCAmelCase						    =       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       __lowerCAmelCase      (						A_   : Dict						,	A_   : Optional[Any]					)    ->  Optional[Any]:
  if ".pt" not in checkpoint_path:
    __UpperCAmelCase						    =       _download(_MODELS[checkpoint_path]					)
  else:
    __UpperCAmelCase						    =       torch.load(A_						,	map_location="cpu"					)
  __UpperCAmelCase						    =       original_checkpoint["dims"]
  __UpperCAmelCase						    =       original_checkpoint["model_state_dict"]
  __UpperCAmelCase						    =       state_dict["decoder.token_embedding.weight"]
  remove_ignore_keys_(A_					)
  rename_keys(A_					)
  __UpperCAmelCase						    =       True
  __UpperCAmelCase						    =       state_dict["decoder.layers.0.fc1.weight"].shape[0]

  __UpperCAmelCase						    =       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"]						,	)

  __UpperCAmelCase						    =       WhisperForConditionalGeneration(A_					)
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       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:
    __UpperCAmelCase						    =       make_linear_from_emb(model.model.decoder.embed_tokens					)
  else:
    __UpperCAmelCase						    =       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)



