






'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_				: int    	=							logging.get_logger(__name__)







def 	__magic_name__( _A						,      _A=False					):
						'''simple docstring'''





						UpperCamelCase__         = []
						for i in range(config.num_hidden_layers					):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")					)
												rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")					)
												rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")					)
												rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")					)
												rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")					)
												rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")					)
												rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")					)
												rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")					)
												rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")					)
												rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")					)

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        ("""cls_token""", """vit.embeddings.cls_token"""),
						        ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
						        ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
						        ("""pos_embed""", """vit.embeddings.position_embeddings"""),
						    ]					)

						if base_model:
												# layernorm + pooler
												rename_keys.extend(
												    [
												        ("""norm.weight""", """layernorm.weight"""),
												        ("""norm.bias""", """layernorm.bias"""),
												    ]					)

												# if just the base model, we should remove "vit" from all keys that start with "vit"
												UpperCamelCase__         = [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""					) else pair for pair in rename_keys]
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ("""norm.weight""", """vit.layernorm.weight"""),
												        ("""norm.bias""", """vit.layernorm.bias"""),
												        ("""head.weight""", """classifier.weight"""),
												        ("""head.bias""", """classifier.bias"""),
												    ]					)

						return rename_keys







def 	__magic_name__( _A						,      _A						,      _A=False					):
						'''simple docstring'''





						for i in range(config.num_hidden_layers					):
												if base_model:
																		UpperCamelCase__         = """"""
												else:
																		UpperCamelCase__         = """vit."""
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												UpperCamelCase__         = state_dict.pop(f"blocks.{i}.attn.qkv.weight"					)
												UpperCamelCase__         = state_dict.pop(f"blocks.{i}.attn.qkv.bias"					)
												# next, add query, keys and values (in that order) to the state dict
												UpperCamelCase__         = in_proj_weight[
												    : config.hidden_size, :
												]
												UpperCamelCase__         = in_proj_bias[: config.hidden_size]
												UpperCamelCase__         = in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												UpperCamelCase__         = in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												UpperCamelCase__         = in_proj_weight[
												    -config.hidden_size :, :
												]
												UpperCamelCase__         = in_proj_bias[-config.hidden_size :]







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = ["""head.weight""", """head.bias"""]
						for k in ignore_keys:
												state_dict.pop(_A						,      _A					)







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = dct.pop(_A					)
						UpperCamelCase__         = val







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = """http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCamelCase__         = Image.open(requests.get(_A						,      stream=_A					).raw					)
						return im







@torch.no_grad()
def 	__magic_name__( _A						,      _A						,      _A=True					):
						'''simple docstring'''





						UpperCamelCase__         = ViTConfig()
						# patch_size
						if model_name[-1] == "8":
												UpperCamelCase__         = 8
						# set labels if required
						if not base_model:
												UpperCamelCase__         = 1000
												UpperCamelCase__         = """huggingface/label-files"""
												UpperCamelCase__         = """imagenet-1k-id2label.json"""
												UpperCamelCase__         = json.load(open(hf_hub_download(_A						,      _A						,      repo_type="""dataset"""					)						,      """r"""					)					)
												UpperCamelCase__         = {int(_A					): v for k, v in idalabel.items()}
												UpperCamelCase__         = idalabel
												UpperCamelCase__         = {v: k for k, v in idalabel.items()}
						# size of the architecture
						if model_name in ["dino_vits8", "dino_vits16"]:
												UpperCamelCase__         = 384
												UpperCamelCase__         = 1536
												UpperCamelCase__         = 12
												UpperCamelCase__         = 6

						# load original model from torch hub
						UpperCamelCase__         = torch.hub.load("""facebookresearch/dino:main"""						,      _A					)
						original_model.eval()

						# load state_dict of original model, remove and rename some keys
						UpperCamelCase__         = original_model.state_dict()
						if base_model:
												remove_classification_head_(_A					)
						UpperCamelCase__         = create_rename_keys(_A						,      base_model=_A					)
						for src, dest in rename_keys:
												rename_key(_A						,      _A						,      _A					)
						read_in_q_k_v(_A						,      _A						,      _A					)

						# load HuggingFace model
						if base_model:
												UpperCamelCase__         = ViTModel(_A						,      add_pooling_layer=_A					).eval()
						else:
												UpperCamelCase__         = ViTForImageClassification(_A					).eval()
						model.load_state_dict(_A					)

						# Check outputs on an image, prepared by ViTImageProcessor
						UpperCamelCase__         = ViTImageProcessor()
						UpperCamelCase__         = image_processor(images=prepare_img()						,      return_tensors="""pt"""					)
						UpperCamelCase__         = encoding["""pixel_values"""]
						UpperCamelCase__         = model(_A					)

						if base_model:
												UpperCamelCase__         = original_model(_A					)
												assert torch.allclose(_A						,      outputs.last_hidden_state[:, 0, :]						,      atol=1e-1					)
						else:
												UpperCamelCase__         = original_model(_A					)
												assert logits.shape == outputs.logits.shape
												assert torch.allclose(_A						,      outputs.logits						,      atol=1e-3					)

						Path(_A					).mkdir(exist_ok=_A					)
						print(f"Saving model {model_name} to {pytorch_dump_folder_path}"					)
						model.save_pretrained(_A					)
						print(f"Saving image processor to {pytorch_dump_folder_path}"					)
						image_processor.save_pretrained(_A					)


if __name__ == "__main__":
					lowerCamelCase_				: Tuple    	=							argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--model_name''',
					    default='''dino_vitb16''',
					    type=str,
					    help='''Name of the model trained with DINO you\'d like to convert.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
					)
					parser.add_argument(
					    '''--base_model''',
					    action='''store_true''',
					    help='''Whether to only convert the base model (no projection head weights).''',
					)

					parser.set_defaults(base_model=True)
					lowerCamelCase_				: int    	=							parser.parse_args()
					convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)








'''simple docstring'''


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							int					     =					"microsoft/speecht5_tts"
			__a							:							int					     =					(
			    "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
			    "text to read (in English) and returns a waveform object containing the sound."
			)
			__a							:							Optional[Any]					     =					"text_reader"
			__a							:							Tuple					     =					SpeechTaProcessor
			__a							:							int					     =					SpeechTaForTextToSpeech
			__a							:							int					     =					SpeechTaHifiGan

			__a							:							Optional[int]					     =					["text"]
			__a							:							Union[str, Any]					     =					["audio"]






			def 				A      (   self							:						str			)							->    Optional[int]:

									'''simple docstring'''







									if self.post_processor is None:
															UpperCamelCase__         = """microsoft/speecht5_hifigan"""
									super().setup()






			def 				A      (   self							:						List[Any] ,      lowercase							:						Any ,      lowercase							:						Union[str, Any]=None			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = self.pre_processor(text=lowercase ,      return_tensors="""pt""" ,      truncation=lowercase			)

									if speaker_embeddings is None:
															if not is_datasets_available():
																					raise ImportError("""Datasets needs to be installed if not passing speaker embeddings."""			)

															UpperCamelCase__         = load_dataset("""Matthijs/cmu-arctic-xvectors""" ,      split="""validation"""			)
															UpperCamelCase__         = torch.tensor(embeddings_dataset[7_3_0_5]["""xvector"""]			).unsqueeze(0			)

									return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Optional[int]			)							->    int:

									'''simple docstring'''







									with torch.no_grad():
															return self.model.generate_speech(**lowercase			)







			def 				A      (   self							:						str ,      lowercase							:						Union[str, Any]			)							->    Tuple:

									'''simple docstring'''







									with torch.no_grad():
															return self.post_processor(lowercase			).cpu().detach()
