


from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


snake_case							:	Tuple					      =				logging.get_logger(__name__)







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   List[Any]   				=      ['''input_features''', '''attention_mask''']
					def __init__(				self       :Dict      ,__snake_case       :Union[str, Any]=80      ,__snake_case       :int=1_60_00      ,__snake_case       :Any=80      ,__snake_case       :Optional[Any]=0.0      ,__snake_case       :int=True      ,__snake_case       :Union[str, Any]=True      ,__snake_case       :Union[str, Any]=True      ,**__snake_case       :Union[str, Any]      ,) ->   Union[str, Any]:
												super().__init__(feature_size=__snake_case      ,sampling_rate=__snake_case      ,padding_value=__snake_case      ,**__snake_case				)
												a__					=					num_mel_bins
												a__					=					do_ceptral_normalize
												a__					=					normalize_means
												a__					=					normalize_vars
												a__					=					True
					def 			lowerCamelCase__(				self       :str      ,__snake_case       :np.ndarray      ,) ->   np.ndarray:
												a__					=					waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
												a__					=					torch.from_numpy(__snake_case				).unsqueeze(0				)
												a__					=					ta_kaldi.fbank(__snake_case      ,num_mel_bins=self.num_mel_bins      ,sample_frequency=self.sampling_rate				)
												return features.numpy()
					@staticmethod
					def 			lowerCamelCase__(				__snake_case       :np.ndarray      ,__snake_case       :int      ,__snake_case       :Optional[bool] = True      ,__snake_case       :Optional[bool] = True      ,__snake_case       :float = 0.0      ,) ->   np.ndarray:
												# make sure we normalize float32 arrays
												if normalize_means:
																			a__					=					x[:input_length].mean(axis=0				)
																			a__					=					np.subtract(__snake_case      ,__snake_case				)
												if normalize_vars:
																			a__					=					x[:input_length].std(axis=0				)
																			a__					=					np.divide(__snake_case      ,__snake_case				)

												if input_length < x.shape[0]:
																			a__					=					padding_value

												# make sure array is in float32
												a__					=					x.astype(np.floataa				)

												return x
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :List[np.ndarray]      ,__snake_case       :Optional[np.ndarray] = None				) ->   List[np.ndarray]:
												a__					=					attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
												return [
												    self.utterance_cmvn(__snake_case      ,__snake_case      ,self.normalize_means      ,self.normalize_vars      ,self.padding_value				)
												    for x, n in zip(__snake_case      ,__snake_case				)
												]



					def __call__(				self       :Dict      ,__snake_case       :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,__snake_case       :Union[bool, str, PaddingStrategy] = False      ,__snake_case       :Optional[int] = None      ,__snake_case       :bool = False      ,__snake_case       :Optional[int] = None      ,__snake_case       :Optional[Union[str, TensorType]] = None      ,__snake_case       :Optional[int] = None      ,__snake_case       :Optional[bool] = None      ,**__snake_case       :Tuple      ,) ->   BatchFeature:

												if sampling_rate is not None:
																			if sampling_rate != self.sampling_rate:
																										raise ValueError(
																										    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
																										    F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
																										    F' {self.sampling_rate} and not {sampling_rate}.'				)
												else:
																			logger.warning(
																			    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
																			    'Failing to do so can result in silent errors that might be hard to debug.'				)

												a__					=					isinstance(__snake_case      ,np.ndarray				) and len(raw_speech.shape				) > 1
												if is_batched_numpy and len(raw_speech.shape				) > 2:
																			raise ValueError(F'Only mono-channel audio is supported for input to {self}'				)
												a__					=					is_batched_numpy or (
												    isinstance(__snake_case      ,(list, tuple)				) and (isinstance(raw_speech[0]      ,(np.ndarray, tuple, list)				))
												)

												if is_batched:
																			a__					=					[np.asarray(__snake_case      ,dtype=np.floataa				) for speech in raw_speech]
												elif not is_batched and not isinstance(__snake_case      ,np.ndarray				):
																			a__					=					np.asarray(__snake_case      ,dtype=np.floataa				)
												elif isinstance(__snake_case      ,np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
																			a__					=					raw_speech.astype(np.floataa				)

												# always return batch
												if not is_batched:
																			a__					=					[raw_speech]

												# extract fbank features
												a__					=					[self._extract_fbank_features(__snake_case				) for waveform in raw_speech]

												# convert into correct format for padding
												a__					=					BatchFeature({'input_features': features}				)

												a__					=					self.pad(
												    __snake_case      ,padding=__snake_case      ,max_length=__snake_case      ,truncation=__snake_case      ,pad_to_multiple_of=__snake_case      ,return_attention_mask=__snake_case      ,**__snake_case      ,)

												# make sure list is in array format
												a__					=					padded_inputs.get('input_features'				)
												if isinstance(input_features[0]      ,__snake_case				):
																			a__					=					[np.asarray(__snake_case      ,dtype=np.floataa				) for feature in input_features]

												a__					=					padded_inputs.get('attention_mask'				)
												if attention_mask is not None:
																			a__					=					[np.asarray(__snake_case      ,dtype=np.intaa				) for array in attention_mask]

												# Utterance-level cepstral mean and variance normalization
												if self.do_ceptral_normalize:
																			a__					=					(
																			    np.array(__snake_case      ,dtype=np.intaa				)
																			    if self._get_padding_strategies(__snake_case      ,max_length=__snake_case				) is not PaddingStrategy.DO_NOT_PAD
																			    else None
																			)
																			a__					=					self.normalize(
																			    padded_inputs['input_features']      ,attention_mask=__snake_case				)

												if return_tensors is not None:
																			a__					=					padded_inputs.convert_to_tensors(__snake_case				)

												return padded_inputs








import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


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






def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str       ):
							a__					=					[]
							for i in range(encoder_config.num_hidden_layers       ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.norm1.weight', F'encoder.encoder.layer.{i}.layernorm_before.weight')       )
														rename_keys.append((F'encoder.deit.blocks.{i}.norm1.bias', F'encoder.encoder.layer.{i}.layernorm_before.bias')       )
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.attn.proj.weight', F'encoder.encoder.layer.{i}.attention.output.dense.weight')       )
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.attn.proj.bias', F'encoder.encoder.layer.{i}.attention.output.dense.bias')       )
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.norm2.weight', F'encoder.encoder.layer.{i}.layernorm_after.weight')       )
														rename_keys.append((F'encoder.deit.blocks.{i}.norm2.bias', F'encoder.encoder.layer.{i}.layernorm_after.bias')       )
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.mlp.fc1.weight', F'encoder.encoder.layer.{i}.intermediate.dense.weight')       )
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.mlp.fc1.bias', F'encoder.encoder.layer.{i}.intermediate.dense.bias')       )
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.mlp.fc2.weight', F'encoder.encoder.layer.{i}.output.dense.weight')       )
														rename_keys.append((F'encoder.deit.blocks.{i}.mlp.fc2.bias', F'encoder.encoder.layer.{i}.output.dense.bias')       )

							# cls token, position embeddings and patch embeddings of encoder
							rename_keys.extend(
							    [
							        ('encoder.deit.cls_token', 'encoder.embeddings.cls_token'),
							        ('encoder.deit.pos_embed', 'encoder.embeddings.position_embeddings'),
							        ('encoder.deit.patch_embed.proj.weight', 'encoder.embeddings.patch_embeddings.projection.weight'),
							        ('encoder.deit.patch_embed.proj.bias', 'encoder.embeddings.patch_embeddings.projection.bias'),
							        ('encoder.deit.norm.weight', 'encoder.layernorm.weight'),
							        ('encoder.deit.norm.bias', 'encoder.layernorm.bias'),
							    ]       )

							return rename_keys






def 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Dict       ):
							for i in range(encoder_config.num_hidden_layers       ):
														# queries, keys and values (only weights, no biases)
														a__					=					state_dict.pop(F'encoder.deit.blocks.{i}.attn.qkv.weight'       )

														a__					=					in_proj_weight[
														    : encoder_config.hidden_size, :
														]
														a__					=					in_proj_weight[
														    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
														]
														a__					=					in_proj_weight[
														    -encoder_config.hidden_size :, :
														]






def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[Any]       ):
							a__					=					dct.pop(__lowerCAmelCase       )
							a__					=					val






def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any]       ):
							if "handwritten" in checkpoint_url:
														a__					=					'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'  # industry
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
							elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
														a__					=					'https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
							a__					=					Image.open(requests.get(__lowerCAmelCase ,   stream=__lowerCAmelCase       ).raw       ).convert('RGB'       )
							return im






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    int       ):
							a__					=					ViTConfig(image_size=3_8_4 ,   qkv_bias=__lowerCAmelCase       )
							a__					=					TrOCRConfig()

							# size of the architecture
							if "base" in checkpoint_url:
														a__					=					7_6_8
							elif "large" in checkpoint_url:
														# use ViT-large encoder
														a__					=					1_0_2_4
														a__					=					4_0_9_6
														a__					=					2_4
														a__					=					1_6
														a__					=					1_0_2_4
							else:
														raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'       )

							# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
							if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
														a__					=					False
														a__					=					'relu'
														a__					=					1_0_2_4
														a__					=					True
														a__					=					False
														a__					=					False

							# load HuggingFace model
							a__					=					ViTModel(__lowerCAmelCase ,   add_pooling_layer=__lowerCAmelCase       )
							a__					=					TrOCRForCausalLM(__lowerCAmelCase       )
							a__					=					VisionEncoderDecoderModel(encoder=__lowerCAmelCase ,   decoder=__lowerCAmelCase       )
							model.eval()

							# load state_dict of original model, rename some keys
							a__					=					torch.hub.load_state_dict_from_url(__lowerCAmelCase ,   map_location='cpu' ,   check_hash=__lowerCAmelCase       )['model']

							a__					=					create_rename_keys(__lowerCAmelCase ,   __lowerCAmelCase       )
							for src, dest in rename_keys:
														rename_key(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
							read_in_q_k_v(__lowerCAmelCase ,   __lowerCAmelCase       )

							# remove parameters we don't need
							del state_dict["encoder.deit.head.weight"]
							del state_dict["encoder.deit.head.bias"]
							del state_dict["decoder.version"]

							# add prefix to decoder keys
							for key, val in state_dict.copy().items():
														a__					=					state_dict.pop(__lowerCAmelCase       )
														if key.startswith('decoder'       ) and "output_projection" not in key:
																					a__					=					val
														else:
																					a__					=					val

    # load state dict
							model.load_state_dict(__lowerCAmelCase       )

							# Check outputs on an image
							a__					=					ViTImageProcessor(size=encoder_config.image_size       )
							a__					=					RobertaTokenizer.from_pretrained('roberta-large'       )
							a__					=					TrOCRProcessor(__lowerCAmelCase ,   __lowerCAmelCase       )

							a__					=					processor(images=prepare_img(__lowerCAmelCase       ) ,   return_tensors='pt'       ).pixel_values

							# verify logits
							a__					=					torch.tensor([[model.config.decoder.decoder_start_token_id]]       )
							a__					=					model(pixel_values=__lowerCAmelCase ,   decoder_input_ids=__lowerCAmelCase       )
							a__					=					outputs.logits

							a__					=					torch.Size([1, 1, 5_0_2_6_5]       )
							if "trocr-base-handwritten" in checkpoint_url:
														a__					=					torch.tensor(
														    [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311]       )
							elif "trocr-large-handwritten" in checkpoint_url:
														a__					=					torch.tensor(
														    [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170]       )
							elif "trocr-base-printed" in checkpoint_url:
														a__					=					torch.tensor(
														    [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210]       )
							elif "trocr-large-printed" in checkpoint_url:
														a__					=					torch.tensor(
														    [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535]       )

							if "stage1" not in checkpoint_url:
														assert logits.shape == expected_shape, "Shape of logits not as expected"
														assert torch.allclose(logits[0, 0, :1_0] ,   __lowerCAmelCase ,   atol=1E-3       ), "First elements of logits not as expected"

							Path(__lowerCAmelCase       ).mkdir(exist_ok=__lowerCAmelCase       )
							print(F'Saving model to {pytorch_dump_folder_path}'       )
							model.save_pretrained(__lowerCAmelCase       )
							print(F'Saving processor to {pytorch_dump_folder_path}'       )
							processor.save_pretrained(__lowerCAmelCase       )


if __name__ == "__main__":
						snake_case							:	Optional[int]					      =				argparse.ArgumentParser()

						parser.add_argument(
						    '''--checkpoint_url''',
						    default='''https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt''',
						    type=str,
						    help='''URL to the original PyTorch checkpoint (.pth file).''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
						)
						snake_case							:	int					      =				parser.parse_args()
						convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




