






'''simple docstring'''







import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD





def 		_UpperCAmelCase (				):
				a_		:	Optional[int]									=   '''https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'''
				a_		:	Union[str, Any]									=   Image.open(requests.get(__A    ,		stream=__A   ).raw   ).convert('''RGB'''   )

				return image





def 		_UpperCAmelCase (				__A				: int   ):
				a_		:	List[str]									=   []
				# fmt: off

				# vision encoder
				rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''')   )
				rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''')   )
				rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''')   )
				rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''')   )
				rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''')   )
				rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''')   )

				for i in range(config.vision_config.num_hidden_layers   ):
								rename_keys.append((f'visual_encoder.blocks.{i}.norm1.weight', f'vision_model.encoder.layers.{i}.layer_norm1.weight')   )
								rename_keys.append((f'visual_encoder.blocks.{i}.norm1.bias', f'vision_model.encoder.layers.{i}.layer_norm1.bias')   )
								rename_keys.append((f'visual_encoder.blocks.{i}.norm2.weight', f'vision_model.encoder.layers.{i}.layer_norm2.weight')   )
								rename_keys.append((f'visual_encoder.blocks.{i}.norm2.bias', f'vision_model.encoder.layers.{i}.layer_norm2.bias')   )
								rename_keys.append((f'visual_encoder.blocks.{i}.attn.qkv.weight', f'vision_model.encoder.layers.{i}.self_attn.qkv.weight')   )
								rename_keys.append((f'visual_encoder.blocks.{i}.attn.proj.weight', f'vision_model.encoder.layers.{i}.self_attn.projection.weight',)   )
								rename_keys.append((f'visual_encoder.blocks.{i}.attn.proj.bias', f'vision_model.encoder.layers.{i}.self_attn.projection.bias')   )
								rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc1.weight', f'vision_model.encoder.layers.{i}.mlp.fc1.weight')   )
								rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc1.bias', f'vision_model.encoder.layers.{i}.mlp.fc1.bias')   )
								rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc2.weight', f'vision_model.encoder.layers.{i}.mlp.fc2.weight')   )
								rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc2.bias', f'vision_model.encoder.layers.{i}.mlp.fc2.bias')   )

				# QFormer
				rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.embeddings.layernorm.weight''')   )
				rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.embeddings.layernorm.bias''')   )

				# fmt: on
				return rename_keys





def 		_UpperCAmelCase (				__A				: Any    ,		__A				: Optional[Any]    ,		__A				: Optional[int]   ):
				a_		:	Optional[Any]									=   dct.pop(__A   )
				a_		:	Dict									=   val





def 		_UpperCAmelCase (				__A				: Any    ,		__A				: Optional[int]   ):
				for i in range(config.vision_config.num_hidden_layers   ):
								# read in original q and v biases
								a_		:	str									=   state_dict.pop(f'visual_encoder.blocks.{i}.attn.q_bias'   )
								a_		:	List[Any]									=   state_dict.pop(f'visual_encoder.blocks.{i}.attn.v_bias'   )

								# next, set bias in the state dict
								a_		:	Optional[Any]									=   torch.cat((q_bias, torch.zeros_like(__A    ,		requires_grad=__A   ), v_bias)   )
								a_		:	List[str]									=   qkv_bias





def 		_UpperCAmelCase (				__A				: List[Any]   ):
				a_		:	Any									=   3_64 if '''coco''' in model_name else 2_24
				a_		:	Tuple									=   InstructBlipVisionConfig(image_size=__A   ).to_dict()

				# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
				# seems like flan-T5 models don't have bos_token_id properly set?
				if "t5-xl" in model_name:
								a_		:	Dict									=   TaConfig.from_pretrained('''google/flan-t5-xl'''    ,		dense_act_fn='''gelu'''    ,		bos_token_id=1   ).to_dict()
				elif "t5-xxl" in model_name:
								a_		:	Optional[Any]									=   TaConfig.from_pretrained('''google/flan-t5-xxl'''    ,		dense_act_fn='''gelu'''    ,		bos_token_id=1   ).to_dict()
				elif "vicuna-7b" in model_name:
								a_		:	List[Any]									=   LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf'''    ,		vocab_size=3_20_01   ).to_dict()
				elif "vicuna-13b" in model_name:
								a_		:	int									=   LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf'''    ,		vocab_size=3_20_01   ).to_dict()
				else:
								raise ValueError('''Model name not supported'''   )

				# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
				a_		:	Optional[int]									=   InstructBlipQFormerConfig(vocab_size=3_05_23   ).to_dict()
				a_		:	Optional[int]									=   InstructBlipConfig(vision_config=__A    ,		text_config=__A    ,		qformer_config=__A   )

				return config, image_size





@torch.no_grad()
def 		_UpperCAmelCase (				__A				: Union[str, Any]    ,		__A				: List[str]=None    ,		__A				: Any=False   ):
				a_		:	List[Any]									=   AutoTokenizer.from_pretrained('''bert-base-uncased'''    ,		truncation_side='''left'''   )
				qformer_tokenizer.add_special_tokens({'''bos_token''': '''[DEC]'''}   )

				if "t5" in model_name:
								a_		:	Optional[int]									=   TaTokenizerFast.from_pretrained('''google/flan-t5-xl'''    ,		truncation_side='''left'''   )
				elif "vicuna" in model_name:
								# the following was used in the original implementation:
								# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
								# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
								# tokenizer.add_special_tokens({"bos_token": "</s>"})
								# tokenizer.add_special_tokens({"eos_token": "</s>"})
								# tokenizer.add_special_tokens({"unk_token": "</s>"})
								a_		:	Dict									=   LlamaTokenizerFast.from_pretrained(
								    '''huggyllama/llama-7b'''    ,		truncation_side='''left'''    ,		bos_token='''</s>'''    ,		unk_token='''</s>'''   )
								tokenizer.add_special_tokens({'''pad_token''': '''[PAD]'''}   )

				a_					,      a_		:	Dict									=   get_blipa_config(__A   )
				a_		:	Dict									=   InstructBlipForConditionalGeneration(__A   ).eval()

				a_		:	str									=   {
				    '''instructblip-vicuna-7b''': ('''blip2_vicuna_instruct''', '''vicuna7b'''),
				    '''instructblip-vicuna-13b''': ('''blip2_vicuna_instruct''', '''vicuna13b'''),
				    '''instructblip-flan-t5-xl''': ('''blip2_t5_instruct''', '''flant5xl'''),
				    '''instructblip-flan-t5-xxl''': ('''blip2_t5_instruct''', '''flant5xxl'''),
				}

				a_					,      a_		:	Optional[int]									=   model_name_to_original[model_name]

				# load original model
				print('''Loading original model...'''   )
				a_		:	Any									=   '''cuda:1''' if torch.cuda.is_available() else '''cpu'''
				a_		:	Any									=   '''cuda:2''' if torch.cuda.is_available() else '''cpu'''
				a_					,      a_					,      a_		:	List[Any]									=   load_model_and_preprocess(
				    name=__A    ,		model_type=__A    ,		is_eval=__A    ,		device=__A   )
				original_model.eval()
				print('''Done!'''   )

				# update state dict keys
				a_		:	Any									=   original_model.state_dict()
				a_		:	Tuple									=   create_rename_keys(__A   )
				for src, dest in rename_keys:
								rename_key(__A    ,		__A    ,		__A   )

				# some keys can be renamed efficiently
				for key, val in state_dict.copy().items():
								a_		:	Union[str, Any]									=   state_dict.pop(__A   )
								if key.startswith('''Qformer.bert'''   ):
												a_		:	Union[str, Any]									=   key.replace('''Qformer.bert'''    ,		'''qformer'''   )
								if "attention.self" in key:
												a_		:	str									=   key.replace('''self'''    ,		'''attention'''   )
								if "llm_proj" in key:
												a_		:	List[Any]									=   key.replace('''llm_proj'''    ,		'''language_projection'''   )
								if "t5_proj" in key:
												a_		:	Dict									=   key.replace('''t5_proj'''    ,		'''language_projection'''   )
								if key.startswith('''llm_model'''   ):
												a_		:	Tuple									=   key.replace('''llm_model'''    ,		'''language_model'''   )
								if key.startswith('''t5'''   ):
												a_		:	Any									=   key.replace('''t5'''    ,		'''language'''   )
								a_		:	Tuple									=   val

				# read in qv biases
				read_in_q_v_bias(__A    ,		__A   )

				# note: weights get loaded in torch.float32 by default
				hf_model.load_state_dict(__A    ,		strict=__A   )

				a_		:	str									=   load_demo_image()
				a_		:	List[Any]									=   '''What is unusual about this image?'''

				# create processor
				a_		:	str									=   BlipImageProcessor(
				    size={'''height''': image_size, '''width''': image_size}    ,		image_mean=__A    ,		image_std=__A   )
				a_		:	Optional[Any]									=   InstructBlipProcessor(
				    image_processor=__A    ,		tokenizer=__A    ,		qformer_tokenizer=__A    ,		)
				a_		:	List[Any]									=   processor(images=__A    ,		text=__A    ,		return_tensors='''pt'''   ).to(__A   )

				# make sure processor creates exact same pixel values
				a_		:	Tuple									=   vis_processors['''eval'''](__A   ).unsqueeze(0   ).to(__A   )
				a_		:	Optional[int]									=   inputs.pixel_values
				assert torch.allclose(original_pixel_values.to(pixel_values.device   )    ,		__A   )

				original_model.to(__A   )
				hf_model.to(__A   )
				with torch.no_grad():
								if "vicuna" in model_name:
												a_		:	Any									=   original_model({'''image''': original_pixel_values, '''text_input''': [prompt]}   ).logits
												a_		:	Optional[int]									=   hf_model(**__A   ).logits
								else:
												a_		:	List[str]									=   original_model(
												    {'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']}   ).logits
												a_		:	List[Any]									=   tokenizer('''\n'''    ,		return_tensors='''pt'''   ).input_ids.to(__A   )
												a_		:	Any									=   label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id    ,		-1_00   )
												a_		:	List[str]									=   hf_model(**__A    ,		labels=__A   ).logits

				print('''First values of original logits:'''    ,		original_logits[0, :3, :3]   )
				print('''First values of HF logits:'''    ,		logits[0, :3, :3]   )

				# assert values
				assert original_logits.shape == logits.shape
				a_		:	Optional[Any]									=   1E-4 if '''vicuna''' in model_name else 1E-5
				assert torch.allclose(original_logits.to(logits.device   )    ,		__A    ,		atol=__A   )
				print('''Looks ok!'''   )

				print('''Generating with original model...'''   )
				a_		:	Optional[Any]									=   original_model.generate({'''image''': original_pixel_values, '''prompt''': prompt}    ,		num_beams=5   )

				# important: we need to cast the weights of the HF model to the appropriate type
				print('''Generating with HF model...'''   )
				a_		:	Any									=   hf_model.generate(
				    **__A    ,		do_sample=__A    ,		num_beams=5    ,		max_length=2_56    ,		min_length=1    ,		top_p=0.9    ,		repetition_penalty=1.5    ,		length_penalty=1.0    ,		temperature=1    ,		)
				if "vicuna" in model_name:
								# convert output id 0 to 2 (eos_token_id)
								# TODO add this in the generate method?
								a_		:	Union[str, Any]									=   2
				print('''Original generation:'''    ,		__A   )
				a_		:	str									=   processor.batch_decode(__A    ,		skip_special_tokens=__A   )
				a_		:	Optional[int]									=   [text.strip() for text in output_text]
				print('''HF generation:'''    ,		__A   )

				if pytorch_dump_folder_path is not None:
								processor.save_pretrained(__A   )
								hf_model.save_pretrained(__A   )

				if push_to_hub:
								processor.push_to_hub(f'Salesforce/{model_name}'   )
								hf_model.push_to_hub(f'Salesforce/{model_name}'   )


if __name__ == "__main__":
		__lowerCAmelCase									=   argparse.ArgumentParser()
		__lowerCAmelCase									=   [
		    'instructblip-vicuna-7b',
		    'instructblip-vicuna-13b',
		    'instructblip-flan-t5-xl',
		    'instructblip-flan-t5-xxl',
		]
		parser.add_argument(
		    '--model_name',
		    default='instructblip-flan-t5-xl',
		    choices=choices,
		    type=str,
		    help='Path to hf config.json of model to convert',
		)
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		parser.add_argument(
		    '--push_to_hub',
		    action='store_true',
		    help='Whether to push the model and processor to the hub after converting',
		)

		__lowerCAmelCase									=   parser.parse_args()

		convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
		import torch


if is_vision_available():
		from PIL import Image

		from transformers import MgpstrProcessor, ViTImageProcessor





@require_torch
@require_vision
class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):
			snake_case__	=					ViTImageProcessor if is_vision_available() else None






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      List[str]:
							return self.image_processor_tester.prepare_image_processor_dict()






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Optional[int]:
							a_		:	Optional[Any]									=   (3, 32, 128)
							a_		:	Optional[Any]									=   tempfile.mkdtemp()

							# fmt: off
							a_		:	Union[str, Any]									=   ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
							# fmt: on
							a_		:	List[Any]									=   dict(zip(__SCREAMING_SNAKE_CASE	,    range(len(__SCREAMING_SNAKE_CASE       )       )       )       )

							a_		:	Union[str, Any]									=   os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES['''vocab_file''']       )
							with open(self.vocab_file	,    '''w'''	,    encoding='''utf-8'''       ) as fp:
											fp.write(json.dumps(__SCREAMING_SNAKE_CASE       ) + '''\n'''       )

							a_		:	Union[str, Any]									=   {
							    '''do_normalize''': False,
							    '''do_resize''': True,
							    '''image_processor_type''': '''ViTImageProcessor''',
							    '''resample''': 3,
							    '''size''': {'''height''': 32, '''width''': 128},
							}
							a_		:	Any									=   os.path.join(self.tmpdirname	,    __SCREAMING_SNAKE_CASE       )
							with open(self.image_processor_file	,    '''w'''	,    encoding='''utf-8'''       ) as fp:
											json.dump(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      Dict:
							return MgpstrTokenizer.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      List[Any]:
							return ViTImageProcessor.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Union[str, Any]:
							shutil.rmtree(self.tmpdirname       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      str:
							a_		:	Optional[Any]									=   np.random.randint(255	,    size=(3, 30, 400)	,    dtype=np.uinta       )

							a_		:	Optional[int]									=   Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE	,    0	,    -1       )       )

							return image_input






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							a_		:	str									=   self.get_tokenizer()
							a_		:	List[Any]									=   self.get_image_processor()

							a_		:	Union[str, Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )
							processor.save_pretrained(self.tmpdirname       )
							a_		:	Tuple									=   MgpstrProcessor.from_pretrained(self.tmpdirname	,    use_fast=__SCREAMING_SNAKE_CASE       )

							self.assertEqual(processor.char_tokenizer.get_vocab()	,    tokenizer.get_vocab()       )
							self.assertIsInstance(processor.char_tokenizer	,    __SCREAMING_SNAKE_CASE       )

							self.assertEqual(processor.image_processor.to_json_string()	,    image_processor.to_json_string()       )
							self.assertIsInstance(processor.image_processor	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      int:
							a_		:	Any									=   self.get_tokenizer()
							a_		:	Dict									=   self.get_image_processor()

							a_		:	Optional[Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )
							processor.save_pretrained(self.tmpdirname       )

							a_		:	str									=   self.get_tokenizer(bos_token='''(BOS)'''	,    eos_token='''(EOS)'''       )
							a_		:	str									=   self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE	,    padding_value=1.0       )

							a_		:	Optional[int]									=   MgpstrProcessor.from_pretrained(
							    self.tmpdirname	,    bos_token='''(BOS)'''	,    eos_token='''(EOS)'''	,    do_normalize=__SCREAMING_SNAKE_CASE	,    padding_value=1.0       )

							self.assertEqual(processor.char_tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()       )
							self.assertIsInstance(processor.char_tokenizer	,    __SCREAMING_SNAKE_CASE       )

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






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	Dict									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[Any]									=   self.prepare_image_inputs()

							a_		:	List[Any]									=   image_processor(__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       )
							a_		:	int									=   processor(images=__SCREAMING_SNAKE_CASE	,    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       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	Dict									=   self.get_image_processor()
							a_		:	List[Any]									=   self.get_tokenizer()

							a_		:	int									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   '''test'''

							a_		:	Any									=   processor(text=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[int]									=   tokenizer(__SCREAMING_SNAKE_CASE       )
							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]       )






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							a_		:	Union[str, Any]									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Optional[Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Union[str, Any]									=   '''test'''
							a_		:	str									=   self.prepare_image_inputs()

							a_		:	Tuple									=   processor(text=__SCREAMING_SNAKE_CASE	,    images=__SCREAMING_SNAKE_CASE       )

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

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






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      int:
							a_		:	int									=   self.get_image_processor()
							a_		:	List[Any]									=   self.get_tokenizer()

							a_		:	Dict									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

							a_		:	List[Any]									=   processor.char_decode(__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE       )
							a_		:	Union[str, Any]									=   [seq.replace(''' '''	,    ''''''       ) for seq in decoded_tok]

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      List[Any]:
							a_		:	int									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Dict									=   None
							a_		:	Tuple									=   self.prepare_image_inputs()

							a_		:	Optional[int]									=   processor(text=__SCREAMING_SNAKE_CASE	,    images=__SCREAMING_SNAKE_CASE       )

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







			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Union[str, Any]:
							a_		:	List[str]									=   self.get_image_processor()
							a_		:	str									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	str									=   torch.randn(1	,    27	,    38       )
							a_		:	int									=   torch.randn(1	,    27	,    5_0257       )
							a_		:	Union[str, Any]									=   torch.randn(1	,    27	,    3_0522       )

							a_		:	Tuple									=   processor.batch_decode([char_input, bpe_input, wp_input]       )

							self.assertListEqual(list(results.keys()       )	,    ['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']       )




