'''simple docstring'''


from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


__lowerCAmelCase	:       Union[str, Any]						       =	logging.get_logger(__name__)






class 						A   (   UpperCAmelCase   ):
							a_        =      ['''audio_values''', '''audio_mask''']





							def __init__(      self				:							int     ,		__a				:							Dict=2_0_4_8     ,		__a				:							List[str]=1     ,		__a				:							str=[1_6, 1_6]     ,		__a				:							str=1_2_8     ,		__a				:							Any=4_4_1_0_0     ,		__a				:							Dict=8_6     ,		__a				:							int=2_0_4_8     ,		__a				:							List[Any]=0.0     ,		**__a				:							List[Any]     ,		)							->	List[Any]:
								super().__init__(
								    feature_size=__a     ,		sampling_rate=__a     ,		padding_value=__a     ,		**__a     ,		)

								__UpperCAmelCase							=			spectrogram_length
								__UpperCAmelCase							=			num_channels
								__UpperCAmelCase							=			patch_size
								__UpperCAmelCase							=			feature_size // self.patch_size[1]
								__UpperCAmelCase							=			n_fft
								__UpperCAmelCase							=			sampling_rate // hop_length_to_sampling_rate
								__UpperCAmelCase							=			sampling_rate
								__UpperCAmelCase							=			padding_value
								__UpperCAmelCase							=			mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2     ,		num_mel_filters=__a     ,		min_frequency=0.0     ,		max_frequency=2_2_0_5_0.0     ,		sampling_rate=__a     ,		norm='''slaney'''     ,		mel_scale='''slaney'''     ,		).T





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							np.array	)							->	np.ndarray:
								__UpperCAmelCase							=			spectrogram(
								    __a     ,		window_function(self.n_fft     ,		'''hann'''	)     ,		frame_length=self.n_fft     ,		hop_length=self.hop_length     ,		power=2.0     ,		mel_filters=self.mel_filters.T     ,		log_mel='''dB'''     ,		db_range=8_0.0     ,		)
								__UpperCAmelCase							=			log_spec[:, :-1]
								__UpperCAmelCase							=			log_spec - 2_0.0
								__UpperCAmelCase							=			np.clip(log_spec / 4_0.0     ,		-2.0     ,		0.0	) + 1.0
								return log_spec






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

								if sampling_rate is not None:
									if sampling_rate != self.sampling_rate:
										raise ValueError(
										    '''This feature extractor is set to support sampling rate'''
										    f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
										    f""" with {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.'''	)

								__UpperCAmelCase							=			isinstance(__a     ,		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}"""	)
								__UpperCAmelCase							=			is_batched_numpy or (
								    isinstance(__a     ,		(list, tuple)	) and (isinstance(raw_speech[0]     ,		(np.ndarray, tuple, list)	))
								)
								if is_batched:
									__UpperCAmelCase							=			[np.asarray([speech]     ,		dtype=np.floataa	).T for speech in raw_speech]
								elif not is_batched and not isinstance(__a     ,		np.ndarray	):
									__UpperCAmelCase							=			np.asarray(__a     ,		dtype=np.floataa	)
								elif isinstance(__a     ,		np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
									__UpperCAmelCase							=			raw_speech.astype(np.floataa	)
								# always return batch
								if not is_batched:
									__UpperCAmelCase							=			[np.asarray([raw_speech]	).T]

								# Convert audio signals to log mel spectrograms, truncate by time axis
								__UpperCAmelCase							=			[
								    self._np_extract_fbank_features(waveform.squeeze()	).T[: self.spectrogram_length] for waveform in raw_speech
								]
								if isinstance(audio_features[0]     ,		__a	):
									__UpperCAmelCase							=			[np.asarray(__a     ,		dtype=np.floataa	) for feature in audio_features]

								# Create audio attention mask
								__UpperCAmelCase							=			max(
								    [ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len for feature in audio_features]	)  # The maximum number of audio patches in a batch
								if return_attention_mask:
									__UpperCAmelCase							=			[
									    (ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [1]
									    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [0]
									    for feature in audio_features
									]
									__UpperCAmelCase							=			np.array(__a	).astype(np.floataa	)

								# convert into correct format for padding
								__UpperCAmelCase							=			max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
								__UpperCAmelCase							=			np.ones([len(__a	), 1, max_time_len, self.feature_size]	).astype(np.floataa	)
								__UpperCAmelCase							=			padded_audio_features * self.padding_value
								for i in range(len(__a	)	):
									__UpperCAmelCase							=			audio_features[i]
									__UpperCAmelCase							=			feature

								# return as BatchFeature
								if return_attention_mask:
									__UpperCAmelCase							=			{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
								else:
									__UpperCAmelCase							=			{'''audio_values''': padded_audio_features}

								__UpperCAmelCase							=			BatchFeature(data=__a     ,		tensor_type=__a	)
								return encoded_inputs

'''simple docstring'''


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()
__lowerCAmelCase	:       Dict						       =	logging.get_logger(__name__)





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Union[str, Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]
	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       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   List[Any]       ):


	"""simple docstring"""
	for i in range(encoder_config.num_hidden_layers       ):
		# queries, keys and values (only weights, no biases)
		__UpperCAmelCase							=			state_dict.pop(f"""encoder.deit.blocks.{i}.attn.qkv.weight"""       )

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





def       lowerCAmelCase					(		UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			dct.pop(UpperCamelCase__       )
	__UpperCAmelCase							=			val





def       lowerCAmelCase					(		UpperCamelCase__				:   Dict       ):


	"""simple docstring"""
	if "handwritten" in checkpoint_url:
		__UpperCAmelCase							=			'''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:
		__UpperCAmelCase							=			'''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
	__UpperCAmelCase							=			Image.open(requests.get(UpperCamelCase__    ,					stream=UpperCamelCase__       ).raw       ).convert('''RGB'''       )
	return im





@torch.no_grad()
def       lowerCAmelCase					(		UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Union[str, Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			ViTConfig(image_size=3_8_4    ,					qkv_bias=UpperCamelCase__       )
	__UpperCAmelCase							=			TrOCRConfig()

	# size of the architecture
	if "base" in checkpoint_url:
		__UpperCAmelCase							=			7_6_8
	elif "large" in checkpoint_url:
		# use ViT-large encoder
		__UpperCAmelCase							=			1_0_2_4
		__UpperCAmelCase							=			4_0_9_6
		__UpperCAmelCase							=			2_4
		__UpperCAmelCase							=			1_6
		__UpperCAmelCase							=			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:
		__UpperCAmelCase							=			False
		__UpperCAmelCase							=			'''relu'''
		__UpperCAmelCase							=			1_0_2_4
		__UpperCAmelCase							=			True
		__UpperCAmelCase							=			False
		__UpperCAmelCase							=			False

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

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

	__UpperCAmelCase							=			create_rename_keys(UpperCamelCase__    ,					UpperCamelCase__       )
	for src, dest in rename_keys:
		rename_key(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       )
	read_in_q_k_v(UpperCamelCase__    ,					UpperCamelCase__       )

	# 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():
		__UpperCAmelCase							=			state_dict.pop(UpperCamelCase__       )
		if key.startswith('''decoder'''       ) and "output_projection" not in key:
			__UpperCAmelCase							=			val
		else:
			__UpperCAmelCase							=			val

    # load state dict
	model.load_state_dict(UpperCamelCase__       )

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

	__UpperCAmelCase							=			processor(images=prepare_img(UpperCamelCase__       )    ,					return_tensors='''pt'''       ).pixel_values

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

	__UpperCAmelCase							=			torch.Size([1, 1, 5_0_2_6_5]       )
	if "trocr-base-handwritten" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11]       )
	elif "trocr-large-handwritten" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70]       )
	elif "trocr-base-printed" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10]       )
	elif "trocr-large-printed" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35]       )

	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]    ,					UpperCamelCase__    ,					atol=1E-3       ), "First elements of logits not as expected"

	Path(UpperCamelCase__       ).mkdir(exist_ok=UpperCamelCase__       )
	print(f"""Saving model to {pytorch_dump_folder_path}"""       )
	model.save_pretrained(UpperCamelCase__       )
	print(f"""Saving processor to {pytorch_dump_folder_path}"""       )
	processor.save_pretrained(UpperCamelCase__       )


if __name__ == "__main__":
					__lowerCAmelCase	:       Any						       =	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."
					)
					__lowerCAmelCase	:       Optional[int]						       =	parser.parse_args()
					convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
