"""simple docstring"""



import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import DPTImageProcessor
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=7   ,      snake_case_=3   ,      snake_case_=18   ,      snake_case_=30   ,      snake_case_=400   ,      snake_case_=True   ,      snake_case_=None   ,      snake_case_=True   ,      snake_case_=[0.5, 0.5, 0.5]   ,      snake_case_=[0.5, 0.5, 0.5]   ,      ):







										'''simple docstring'''


										A__       :     int								=			size if size is not None else {"""height""": 18, """width""": 18}
										A__       :     Dict								=			parent
										A__       :     Optional[Any]								=			batch_size
										A__       :     Union[str, Any]								=			num_channels
										A__       :     Tuple								=			image_size
										A__       :     Dict								=			min_resolution
										A__       :     Union[str, Any]								=			max_resolution
										A__       :     Union[str, Any]								=			do_resize
										A__       :     List[str]								=			size
										A__       :     List[Any]								=			do_normalize
										A__       :     Tuple								=			image_mean
										A__       :     str								=			image_std





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										}





@require_torch
@require_vision
class 						__UpperCAmelCase    (__A							,   unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						List[str]					     =				DPTImageProcessor if is_vision_available() else None





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			DPTImageProcessingTester(self							)





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return self.image_processor_tester.prepare_image_processor_dict()





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(snake_case_   ,      """image_mean"""							)							)
										self.assertTrue(hasattr(snake_case_   ,      """image_std"""							)							)
										self.assertTrue(hasattr(snake_case_   ,      """do_normalize"""							)							)
										self.assertTrue(hasattr(snake_case_   ,      """do_resize"""							)							)
										self.assertTrue(hasattr(snake_case_   ,      """size"""							)							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     int								=			self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size   ,      {"""height""": 18, """width""": 18}							)

										A__       :     Dict								=			self.image_processing_class.from_dict(self.image_processor_dict   ,      size=42							)
										self.assertEqual(image_processor.size   ,      {"""height""": 42, """width""": 42}							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										A__       :     Union[str, Any]								=			prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=snake_case_							)
										for image in image_inputs:
																	self.assertIsInstance(snake_case_   ,      Image.Image							)

										# Test not batched input
										A__       :     str								=			image_processing(image_inputs[0]   ,      return_tensors="""pt"""							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,      (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )   ,      )

										# Test batched
										A__       :     str								=			image_processing(snake_case_   ,      return_tensors="""pt"""							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,      (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )   ,      )





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Tuple								=			self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										A__       :     Optional[Any]								=			prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=snake_case_   ,      numpify=snake_case_							)
										for image in image_inputs:
																	self.assertIsInstance(snake_case_   ,      np.ndarray							)

										# Test not batched input
										A__       :     Union[str, Any]								=			image_processing(image_inputs[0]   ,      return_tensors="""pt"""							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,      (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )   ,      )

										# Test batched
										A__       :     str								=			image_processing(snake_case_   ,      return_tensors="""pt"""							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,      (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )   ,      )





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										A__       :     List[Any]								=			prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=snake_case_   ,      torchify=snake_case_							)
										for image in image_inputs:
																	self.assertIsInstance(snake_case_   ,      torch.Tensor							)

										# Test not batched input
										A__       :     Tuple								=			image_processing(image_inputs[0]   ,      return_tensors="""pt"""							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,      (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )   ,      )

										# Test batched
										A__       :     Dict								=			image_processing(snake_case_   ,      return_tensors="""pt"""							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,      (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )   ,      )






"""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


_UpperCamelCase								=		{
    "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  _A(		lowerCAmelCase							):
							A__       :     Any								=			["""layers""", """blocks"""]
							for k in ignore_keys:
														state_dict.pop(lowerCAmelCase			,  lowerCAmelCase							)


_UpperCamelCase								=		{
    "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  _A(		lowerCAmelCase							):
							A__       :     str								=			list(s_dict.keys()							)
							for key in keys:
														A__       :     Optional[Any]								=			key
														for k, v in WHISPER_MAPPING.items():
																					if k in key:
																												A__       :     Optional[Any]								=			new_key.replace(lowerCAmelCase			,  lowerCAmelCase							)

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

														A__       :     Any								=			s_dict.pop(lowerCAmelCase							)
							return s_dict

def  _A(		lowerCAmelCase							):
							A__				,							A__       :     Union[str, Any]								=			emb.weight.shape
							A__       :     Optional[Any]								=			nn.Linear(lowerCAmelCase			,  lowerCAmelCase			,  bias=lowerCAmelCase							)
							A__       :     int								=			emb.weight.data
							return lin_layer

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							os.makedirs(lowerCAmelCase			,  exist_ok=lowerCAmelCase							)
							A__       :     Union[str, Any]								=			os.path.basename(lowerCAmelCase							)

							A__       :     str								=			url.split("""/"""							)[-2]
							A__       :     Union[str, Any]								=			os.path.join(lowerCAmelCase			,  lowerCAmelCase							)

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

							if os.path.isfile(lowerCAmelCase							):
														A__       :     Tuple								=			open(lowerCAmelCase			,  """rb"""							).read()
														if hashlib.shaaaa(lowerCAmelCase							).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(lowerCAmelCase							) as source, open(lowerCAmelCase			,  """wb"""							) as output:
														with tqdm(
														    total=int(source.info().get("""Content-Length"""							)							)			,  ncols=80			,  unit="""iB"""			,  unit_scale=lowerCAmelCase			,  unit_divisor=1024							) as loop:
																					while True:
																												A__       :     Dict								=			source.read(8192							)
																												if not buffer:
																																			break

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

							A__       :     List[str]								=			open(lowerCAmelCase			,  """rb"""							).read()
							if hashlib.shaaaa(lowerCAmelCase							).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  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							if ".pt" not in checkpoint_path:
														A__       :     str								=			_download(_MODELS[checkpoint_path]							)
							else:
														A__       :     Optional[int]								=			torch.load(lowerCAmelCase			,  map_location="""cpu"""							)
							A__       :     List[str]								=			original_checkpoint["""dims"""]
							A__       :     List[Any]								=			original_checkpoint["""model_state_dict"""]
							A__       :     Dict								=			state_dict["""decoder.token_embedding.weight"""]
							remove_ignore_keys_(lowerCAmelCase							)
							rename_keys(lowerCAmelCase							)
							A__       :     Any								=			True
							A__       :     List[str]								=			state_dict["""decoder.layers.0.fc1.weight"""].shape[0]

							A__       :     str								=			WhisperConfig(
							    vocab_size=dimensions["""n_vocab"""]			,  encoder_ffn_dim=lowerCAmelCase			,  decoder_ffn_dim=lowerCAmelCase			,  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"""]			,  )

							A__       :     int								=			WhisperForConditionalGeneration(lowerCAmelCase							)
							A__				,							A__       :     List[Any]								=			model.model.load_state_dict(lowerCAmelCase			,  strict=lowerCAmelCase							)
							if len(lowerCAmelCase							) > 0 and not set(lowerCAmelCase							) <= {
							    "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:
														A__       :     Tuple								=			make_linear_from_emb(model.model.decoder.embed_tokens							)
							else:
														A__       :     Any								=			proj_out_weights

							model.save_pretrained(lowerCAmelCase							)


if __name__ == "__main__":
				_UpperCamelCase								=		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.")
				_UpperCamelCase								=		parser.parse_args()

				convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)





