





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPImageProcessor, CLIPProcessor

@require_vision
class 		snake_case_   (							unittest.TestCase						):







	'''simple docstring'''




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

			'''simple docstring'''




			__lowercase											=     tempfile.mkdtemp()

			# fmt: off
			__lowercase											=     ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
			# fmt: on
			__lowercase											=     dict(zip(__lowerCamelCase					,      range(len(__lowerCamelCase   )   )   )   )
			__lowercase											=     ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
			__lowercase											=     {'unk_token': '<unk>'}

			__lowercase											=     os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES['vocab_file']   )
			__lowercase											=     os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES['merges_file']   )
			with open(self.vocab_file					,      'w'					,      encoding='utf-8'   ) as fp:
					fp.write(json.dumps(__lowerCamelCase   ) + '\n'   )
			with open(self.merges_file					,      'w'					,      encoding='utf-8'   ) as fp:
					fp.write('\n'.join(__lowerCamelCase   )   )

			__lowercase											=     {
			    'do_resize': True,
			    'size': 20,
			    'do_center_crop': True,
			    'crop_size': 18,
			    'do_normalize': True,
			    'image_mean': [0.48145466, 0.4578275, 0.40821073],
			    'image_std': [0.26862954, 0.26130258, 0.27577711],
			}
			__lowercase											=     os.path.join(self.tmpdirname					,      __lowerCamelCase   )
			with open(self.image_processor_file					,      'w'					,      encoding='utf-8'   ) as fp:
					json.dump(__lowerCamelCase					,      __lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Tuple					,      **__lowerCamelCase							:     Optional[int]   )       -> List[str]:

			'''simple docstring'''




			return CLIPTokenizer.from_pretrained(self.tmpdirname					,      **__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Dict					,      **__lowerCamelCase							:     Union[str, Any]   )       -> Optional[Any]:

			'''simple docstring'''




			return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,      **__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      **__lowerCamelCase							:     Any   )       -> Optional[int]:

			'''simple docstring'''




			return CLIPImageProcessor.from_pretrained(self.tmpdirname					,      **__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     int   )       -> List[Any]:

			'''simple docstring'''




			shutil.rmtree(self.tmpdirname   )




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

			'''simple docstring'''




			__lowercase											=     [np.random.randint(255					,      size=(3, 30, 400)					,      dtype=np.uinta   )]

			__lowercase											=     [Image.fromarray(np.moveaxis(__lowerCamelCase					,      0					,      -1   )   ) for x in image_inputs]

			return image_inputs




	def    UpperCAmelCase			(						self							:     List[Any]   )       -> int:

			'''simple docstring'''




			__lowercase											=     self.get_tokenizer()
			__lowercase											=     self.get_rust_tokenizer()
			__lowercase											=     self.get_image_processor()

			__lowercase											=     CLIPProcessor(tokenizer=__lowerCamelCase					,      image_processor=__lowerCamelCase   )
			processor_slow.save_pretrained(self.tmpdirname   )
			__lowercase											=     CLIPProcessor.from_pretrained(self.tmpdirname					,      use_fast=__lowerCamelCase   )

			__lowercase											=     CLIPProcessor(tokenizer=__lowerCamelCase					,      image_processor=__lowerCamelCase   )
			processor_fast.save_pretrained(self.tmpdirname   )
			__lowercase											=     CLIPProcessor.from_pretrained(self.tmpdirname   )

			self.assertEqual(processor_slow.tokenizer.get_vocab()					,      tokenizer_slow.get_vocab()   )
			self.assertEqual(processor_fast.tokenizer.get_vocab()					,      tokenizer_fast.get_vocab()   )
			self.assertEqual(tokenizer_slow.get_vocab()					,      tokenizer_fast.get_vocab()   )
			self.assertIsInstance(processor_slow.tokenizer					,      __lowerCamelCase   )
			self.assertIsInstance(processor_fast.tokenizer					,      __lowerCamelCase   )

			self.assertEqual(processor_slow.image_processor.to_json_string()					,      image_processor.to_json_string()   )
			self.assertEqual(processor_fast.image_processor.to_json_string()					,      image_processor.to_json_string()   )
			self.assertIsInstance(processor_slow.image_processor					,      __lowerCamelCase   )
			self.assertIsInstance(processor_fast.image_processor					,      __lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Any   )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase											=     CLIPProcessor(tokenizer=self.get_tokenizer()					,      image_processor=self.get_image_processor()   )
			processor.save_pretrained(self.tmpdirname   )

			__lowercase											=     self.get_tokenizer(bos_token='(BOS)'					,      eos_token='(EOS)'   )
			__lowercase											=     self.get_image_processor(do_normalize=__lowerCamelCase					,      padding_value=1.0   )

			__lowercase											=     CLIPProcessor.from_pretrained(
			    self.tmpdirname					,      bos_token='(BOS)'					,      eos_token='(EOS)'					,      do_normalize=__lowerCamelCase					,      padding_value=1.0   )

			self.assertEqual(processor.tokenizer.get_vocab()					,      tokenizer_add_kwargs.get_vocab()   )
			self.assertIsInstance(processor.tokenizer					,      __lowerCamelCase   )

			self.assertEqual(processor.image_processor.to_json_string()					,      image_processor_add_kwargs.to_json_string()   )
			self.assertIsInstance(processor.image_processor					,      __lowerCamelCase   )




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

			'''simple docstring'''




			__lowercase											=     self.get_image_processor()
			__lowercase											=     self.get_tokenizer()

			__lowercase											=     CLIPProcessor(tokenizer=__lowerCamelCase					,      image_processor=__lowerCamelCase   )

			__lowercase											=     self.prepare_image_inputs()

			__lowercase											=     image_processor(__lowerCamelCase					,      return_tensors='np'   )
			__lowercase											=     processor(images=__lowerCamelCase					,      return_tensors='np'   )

			for key in input_image_proc.keys():
					self.assertAlmostEqual(input_image_proc[key].sum()					,      input_processor[key].sum()					,      delta=1E-2   )




	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase											=     self.get_image_processor()
			__lowercase											=     self.get_tokenizer()

			__lowercase											=     CLIPProcessor(tokenizer=__lowerCamelCase					,      image_processor=__lowerCamelCase   )

			__lowercase											=     'lower newer'

			__lowercase											=     processor(text=__lowerCamelCase   )

			__lowercase											=     tokenizer(__lowerCamelCase   )

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]					,      encoded_processor[key]   )




	def    UpperCAmelCase			(						self							:     List[Any]   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     self.get_image_processor()
			__lowercase											=     self.get_tokenizer()

			__lowercase											=     CLIPProcessor(tokenizer=__lowerCamelCase					,      image_processor=__lowerCamelCase   )

			__lowercase											=     'lower newer'
			__lowercase											=     self.prepare_image_inputs()

			__lowercase											=     processor(text=__lowerCamelCase					,      images=__lowerCamelCase   )

			self.assertListEqual(list(inputs.keys()   )					,      ['input_ids', 'attention_mask', 'pixel_values']   )

			# test if it raises when no input is passed
			with pytest.raises(__lowerCamelCase   ):
					processor()




	def    UpperCAmelCase			(						self							:     str   )       -> str:

			'''simple docstring'''




			__lowercase											=     self.get_image_processor()
			__lowercase											=     self.get_tokenizer()

			__lowercase											=     CLIPProcessor(tokenizer=__lowerCamelCase					,      image_processor=__lowerCamelCase   )

			__lowercase											=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			__lowercase											=     processor.batch_decode(__lowerCamelCase   )
			__lowercase											=     tokenizer.batch_decode(__lowerCamelCase   )

			self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase   )






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

			'''simple docstring'''




			__lowercase											=     self.get_image_processor()
			__lowercase											=     self.get_tokenizer()

			__lowercase											=     CLIPProcessor(tokenizer=__lowerCamelCase					,      image_processor=__lowerCamelCase   )

			__lowercase											=     'lower newer'
			__lowercase											=     self.prepare_image_inputs()

			__lowercase											=     processor(text=__lowerCamelCase					,      images=__lowerCamelCase   )

			self.assertListEqual(list(inputs.keys()   )					,      processor.model_input_names   )











import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Optional[int]:
		__lowercase											=     model.config

		__lowercase											=     DonutSwinConfig(
		    image_size=original_config.input_size						,   patch_size=4						,   depths=original_config.encoder_layer						,   num_heads=[4, 8, 16, 32]						,   window_size=original_config.window_size						,   embed_dim=128						,   )
		__lowercase											=     MBartConfig(
		    is_decoder=snake_case						,   is_encoder_decoder=snake_case						,   add_cross_attention=snake_case						,   decoder_layers=original_config.decoder_layer						,   max_position_embeddings=original_config.max_position_embeddings						,   vocab_size=len(
		        model.decoder.tokenizer							)						,   scale_embedding=snake_case						,   add_final_layer_norm=snake_case						,   )

		return encoder_config, decoder_config



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		if "encoder.model" in name:
				__lowercase											=     name.replace('encoder.model'						,   'encoder'							)
		if "decoder.model" in name:
				__lowercase											=     name.replace('decoder.model'						,   'decoder'							)
		if "patch_embed.proj" in name:
				__lowercase											=     name.replace('patch_embed.proj'						,   'embeddings.patch_embeddings.projection'							)
		if "patch_embed.norm" in name:
				__lowercase											=     name.replace('patch_embed.norm'						,   'embeddings.norm'							)
		if name.startswith('encoder'							):
				if "layers" in name:
						__lowercase											=     'encoder.' + name
				if "attn.proj" in name:
						__lowercase											=     name.replace('attn.proj'						,   'attention.output.dense'							)
				if "attn" in name and "mask" not in name:
						__lowercase											=     name.replace('attn'						,   'attention.self'							)
				if "norm1" in name:
						__lowercase											=     name.replace('norm1'						,   'layernorm_before'							)
				if "norm2" in name:
						__lowercase											=     name.replace('norm2'						,   'layernorm_after'							)
				if "mlp.fc1" in name:
						__lowercase											=     name.replace('mlp.fc1'						,   'intermediate.dense'							)
				if "mlp.fc2" in name:
						__lowercase											=     name.replace('mlp.fc2'						,   'output.dense'							)

				if name == "encoder.norm.weight":
						__lowercase											=     'encoder.layernorm.weight'
				if name == "encoder.norm.bias":
						__lowercase											=     'encoder.layernorm.bias'

		return name



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    List[Any]:
		for key in orig_state_dict.copy().keys():
				__lowercase											=     orig_state_dict.pop(snake_case							)

				if "qkv" in key:
						__lowercase											=     key.split('.'							)
						__lowercase											=     int(key_split[3]							)
						__lowercase											=     int(key_split[5]							)
						__lowercase											=     model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								__lowercase											=     val[:dim, :]
								__lowercase											=     val[dim : dim * 2, :]
								__lowercase											=     val[-dim:, :]
						else:
								__lowercase											=     val[:dim]
								__lowercase											=     val[dim : dim * 2]
								__lowercase											=     val[-dim:]
				elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
						# HuggingFace implementation doesn't use attn_mask buffer
						# and model doesn't use final LayerNorms for the encoder
						pass
				else:
						__lowercase											=     val

		return orig_state_dict



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=None						,   snake_case=False							)		->    Any:
		# load original model
		__lowercase											=     DonutModel.from_pretrained(snake_case							).eval()

		# load HuggingFace model
		__lowercase						,      __lowercase											=     get_configs(snake_case							)
		__lowercase											=     DonutSwinModel(snake_case							)
		__lowercase											=     MBartForCausalLM(snake_case							)
		__lowercase											=     VisionEncoderDecoderModel(encoder=snake_case						,   decoder=snake_case							)
		model.eval()

		__lowercase											=     original_model.state_dict()
		__lowercase											=     convert_state_dict(snake_case						,   snake_case							)
		model.load_state_dict(snake_case							)

		# verify results on scanned document
		__lowercase											=     load_dataset('hf-internal-testing/example-documents'							)
		__lowercase											=     dataset['test'][0]['image'].convert('RGB'							)

		__lowercase											=     XLMRobertaTokenizerFast.from_pretrained(snake_case						,   from_slow=snake_case							)
		__lowercase											=     DonutImageProcessor(
		    do_align_long_axis=original_model.config.align_long_axis						,   size=original_model.config.input_size[::-1]							)
		__lowercase											=     DonutProcessor(snake_case						,   snake_case							)
		__lowercase											=     processor(snake_case						,   return_tensors='pt'							).pixel_values

		if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
				__lowercase											=     '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
				__lowercase											=     'When is the coffee break?'
				__lowercase											=     task_prompt.replace('{user_input}'						,   snake_case							)
		elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
				__lowercase											=     '<s_rvlcdip>'
		elif model_name in [
		    "naver-clova-ix/donut-base-finetuned-cord-v1",
		    "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
		]:
				__lowercase											=     '<s_cord>'
		elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
				__lowercase											=     's_cord-v2>'
		elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
				__lowercase											=     '<s_zhtrainticket>'
		elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
				# use a random prompt
				__lowercase											=     'hello world'
		else:
				raise ValueError('Model name not supported'							)
		__lowercase											=     original_model.decoder.tokenizer(snake_case						,   add_special_tokens=snake_case						,   return_tensors='pt'							)[
		    'input_ids'
		]

		__lowercase											=     original_model.encoder.model.patch_embed(snake_case							)
		__lowercase						,      __lowercase											=     model.encoder.embeddings(snake_case							)
		assert torch.allclose(snake_case						,   snake_case						,   atol=1E-3							)

		# verify encoder hidden states
		__lowercase											=     original_model.encoder(snake_case							)
		__lowercase											=     model.encoder(snake_case							).last_hidden_state
		assert torch.allclose(snake_case						,   snake_case						,   atol=1E-2							)

		# verify decoder hidden states
		__lowercase											=     original_model(snake_case						,   snake_case						,   snake_case							).logits
		__lowercase											=     model(snake_case						,   decoder_input_ids=snake_case							).logits
		assert torch.allclose(snake_case						,   snake_case						,   atol=1E-3							)
		print('Looks ok!'							)

		if pytorch_dump_folder_path is not None:
				print(F"Saving model and processor to {pytorch_dump_folder_path}"							)
				model.save_pretrained(snake_case							)
				processor.save_pretrained(snake_case							)

		if push_to_hub:
				model.push_to_hub('nielsr/' + model_name.split('/'							)[-1]						,   commit_message='Update model'							)
				processor.push_to_hub('nielsr/' + model_name.split('/'							)[-1]						,   commit_message='Update model'							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Dict			=   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''naver-clova-ix/donut-base-finetuned-docvqa''',
		    required=False,
		    type=str,
		    help='''Name of the original model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    required=False,
		    type=str,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument(
		    '''--push_to_hub''',
		    action='''store_true''',
		    help='''Whether or not to push the converted model and processor to the 🤗 hub.''',
		)

		SCREAMING_SNAKE_CASE_   :      Tuple			=   parser.parse_args()
		convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




