






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 				lowerCAmelCase_	(		unittest.TestCase			):
				_UpperCamelCase					:       List[Any]							       = ViTImageProcessor if is_vision_available() else None


				@property
				def 						__a					(					self      ):
											return self.image_processor_tester.prepare_image_processor_dict()


				def 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   (3, 3_2, 1_2_8)
											_lowercase							:   List[Any] 							=   tempfile.mkdtemp()

											# fmt: off
											_lowercase							:   str 							=   ['[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
											_lowercase							:   str 							=   dict(zip(_lowerCAmelCase ,						range(len(_lowerCAmelCase      )      )      )      )

											_lowercase							:   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(_lowerCAmelCase      ) + '\n'      )

											_lowercase							:   Dict 							=   {
											    'do_normalize': False,
											    'do_resize': True,
											    'image_processor_type': 'ViTImageProcessor',
											    'resample': 3,
											    'size': {'height': 3_2, 'width': 1_2_8},
											}
											_lowercase							:   int 							=   os.path.join(self.tmpdirname ,						_lowerCAmelCase      )
											with open(self.image_processor_file ,						'w' ,						encoding='utf-8'      ) as fp:
																		json.dump(_lowerCAmelCase ,						_lowerCAmelCase      )


				def 						__a					(					self ,						**_lowerCAmelCase      ):
											return MgpstrTokenizer.from_pretrained(self.tmpdirname ,						**_lowerCAmelCase      )


				def 						__a					(					self ,						**_lowerCAmelCase      ):
											return ViTImageProcessor.from_pretrained(self.tmpdirname ,						**_lowerCAmelCase      )


				def 						__a					(					self      ):
											shutil.rmtree(self.tmpdirname      )


				def 						__a					(					self      ):
											_lowercase							:   List[str] 							=   np.random.randint(2_5_5 ,						size=(3, 3_0, 4_0_0) ,						dtype=np.uinta      )

											_lowercase							:   int 							=   Image.fromarray(np.moveaxis(_lowerCAmelCase ,						0 ,						-1      )      )

											return image_input


				def 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   self.get_tokenizer()
											_lowercase							:   List[Any] 							=   self.get_image_processor()

											_lowercase							:   str 							=   MgpstrProcessor(tokenizer=_lowerCAmelCase ,						image_processor=_lowerCAmelCase      )
											processor.save_pretrained(self.tmpdirname      )
											_lowercase							:   Any 							=   MgpstrProcessor.from_pretrained(self.tmpdirname ,						use_fast=_lowerCAmelCase      )

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

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


				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   self.get_tokenizer()
											_lowercase							:   int 							=   self.get_image_processor()

											_lowercase							:   List[Any] 							=   MgpstrProcessor(tokenizer=_lowerCAmelCase ,						image_processor=_lowerCAmelCase      )
											processor.save_pretrained(self.tmpdirname      )

											_lowercase							:   str 							=   self.get_tokenizer(bos_token='(BOS)' ,						eos_token='(EOS)'      )
											_lowercase							:   Optional[int] 							=   self.get_image_processor(do_normalize=_lowerCAmelCase ,						padding_value=1.0      )

											_lowercase							:   Any 							=   MgpstrProcessor.from_pretrained(
											    self.tmpdirname ,						bos_token='(BOS)' ,						eos_token='(EOS)' ,						do_normalize=_lowerCAmelCase ,						padding_value=1.0      )

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

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


				def 						__a					(					self      ):
											_lowercase							:   Optional[Any] 							=   self.get_image_processor()
											_lowercase							:   Optional[int] 							=   self.get_tokenizer()

											_lowercase							:   Optional[Any] 							=   MgpstrProcessor(tokenizer=_lowerCAmelCase ,						image_processor=_lowerCAmelCase      )

											_lowercase							:   Tuple 							=   self.prepare_image_inputs()

											_lowercase							:   str 							=   image_processor(_lowerCAmelCase ,						return_tensors='np'      )
											_lowercase							:   Union[str, Any] 							=   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 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   self.get_image_processor()
											_lowercase							:   Dict 							=   self.get_tokenizer()

											_lowercase							:   str 							=   MgpstrProcessor(tokenizer=_lowerCAmelCase ,						image_processor=_lowerCAmelCase      )

											_lowercase							:   Dict 							=   'test'

											_lowercase							:   Optional[int] 							=   processor(text=_lowerCAmelCase      )

											_lowercase							:   Any 							=   tokenizer(_lowerCAmelCase      )
											for key in encoded_tok.keys():
																		self.assertListEqual(encoded_tok[key] ,						encoded_processor[key]      )


				def 						__a					(					self      ):
											_lowercase							:   int 							=   self.get_image_processor()
											_lowercase							:   Tuple 							=   self.get_tokenizer()

											_lowercase							:   int 							=   MgpstrProcessor(tokenizer=_lowerCAmelCase ,						image_processor=_lowerCAmelCase      )

											_lowercase							:   Optional[Any] 							=   'test'
											_lowercase							:   str 							=   self.prepare_image_inputs()

											_lowercase							:   Optional[Any] 							=   processor(text=_lowerCAmelCase ,						images=_lowerCAmelCase      )

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

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


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   self.get_image_processor()
											_lowercase							:   Dict 							=   self.get_tokenizer()

											_lowercase							:   Optional[Any] 							=   MgpstrProcessor(tokenizer=_lowerCAmelCase ,						image_processor=_lowerCAmelCase      )

											_lowercase							:   Any 							=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

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

											self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   Any 							=   self.get_image_processor()
											_lowercase							:   List[str] 							=   self.get_tokenizer()

											_lowercase							:   Union[str, Any] 							=   MgpstrProcessor(tokenizer=_lowerCAmelCase ,						image_processor=_lowerCAmelCase      )

											_lowercase							:   Union[str, Any] 							=   None
											_lowercase							:   Tuple 							=   self.prepare_image_inputs()

											_lowercase							:   str 							=   processor(text=_lowerCAmelCase ,						images=_lowerCAmelCase      )

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


				def 						__a					(					self      ):
											_lowercase							:   Any 							=   self.get_image_processor()
											_lowercase							:   int 							=   self.get_tokenizer()

											_lowercase							:   Any 							=   MgpstrProcessor(tokenizer=_lowerCAmelCase ,						image_processor=_lowerCAmelCase      )

											_lowercase							:   Union[str, Any] 							=   torch.randn(1 ,						2_7 ,						3_8      )
											_lowercase							:   Tuple 							=   torch.randn(1 ,						2_7 ,						5_0_2_5_7      )
											_lowercase							:   List[str] 							=   torch.randn(1 ,						2_7 ,						3_0_5_2_2      )

											_lowercase							:   Union[str, Any] 							=   processor.batch_decode([char_input, bpe_input, wp_input]      )

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








'''simple docstring'''






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer




@dataclass
class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  torch.FloatTensor




class 						a__							(				a__							,							a__    ):





							'''simple docstring'''






							@register_to_config
							def __init__(						self   ,       lowerCamelCase_ = 3   ,       lowerCamelCase_ = 3   ,       lowerCamelCase_ = ("DownEncoderBlock2D",)   ,       lowerCamelCase_ = ("UpDecoderBlock2D",)   ,       lowerCamelCase_ = (64,)   ,       lowerCamelCase_ = 1   ,       lowerCamelCase_ = "silu"   ,       lowerCamelCase_ = 3   ,       lowerCamelCase_ = 32   ,       lowerCamelCase_ = 2_56   ,       lowerCamelCase_ = 32   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = 0.18_215   ,       lowerCamelCase_ = "group"   ,       )			->				Union[str, Any]:
														super().__init__()

														# pass init params to Encoder
														lowerCAmelCase__			  =      Encoder(
														    in_channels=lowerCamelCase_   ,       out_channels=lowerCamelCase_   ,       down_block_types=lowerCamelCase_   ,       block_out_channels=lowerCamelCase_   ,       layers_per_block=lowerCamelCase_   ,       act_fn=lowerCamelCase_   ,       norm_num_groups=lowerCamelCase_   ,       double_z=lowerCamelCase_   ,       )

														lowerCAmelCase__			  =      vq_embed_dim if vq_embed_dim is not None else latent_channels

														lowerCAmelCase__			  =      nn.Convad(lowerCamelCase_   ,       lowerCamelCase_   ,       1  )
														lowerCAmelCase__			  =      VectorQuantizer(lowerCamelCase_   ,       lowerCamelCase_   ,       beta=0.25   ,       remap=lowerCamelCase_   ,       sane_index_shape=lowerCamelCase_  )
														lowerCAmelCase__			  =      nn.Convad(lowerCamelCase_   ,       lowerCamelCase_   ,       1  )

														# pass init params to Decoder
														lowerCAmelCase__			  =      Decoder(
														    in_channels=lowerCamelCase_   ,       out_channels=lowerCamelCase_   ,       up_block_types=lowerCamelCase_   ,       block_out_channels=lowerCamelCase_   ,       layers_per_block=lowerCamelCase_   ,       act_fn=lowerCamelCase_   ,       norm_num_groups=lowerCamelCase_   ,       norm_type=lowerCamelCase_   ,       )







							@apply_forward_hook
							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = True  )			->				VQEncoderOutput:
														lowerCAmelCase__			  =      self.encoder(lowerCamelCase_  )
														lowerCAmelCase__			  =      self.quant_conv(lowerCamelCase_  )

														if not return_dict:
																					return (h,)

														return VQEncoderOutput(latents=lowerCamelCase_  )







							@apply_forward_hook
							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = False   ,       lowerCamelCase_ = True  )			->				Union[DecoderOutput, torch.FloatTensor]:
														# also go through quantization layer
														if not force_not_quantize:
																					lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.quantize(lowerCamelCase_  )
														else:
																					lowerCAmelCase__			  =      h
														lowerCAmelCase__			  =      self.post_quant_conv(lowerCamelCase_  )
														lowerCAmelCase__			  =      self.decoder(lowerCamelCase_   ,       quant if self.config.norm_type == '''spatial''' else None  )

														if not return_dict:
																					return (dec,)

														return DecoderOutput(sample=lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = True  )			->				Union[DecoderOutput, torch.FloatTensor]:
														lowerCAmelCase__			  =      sample
														lowerCAmelCase__			  =      self.encode(lowerCamelCase_  ).latents
														lowerCAmelCase__			  =      self.decode(lowerCamelCase_  ).sample

														if not return_dict:
																					return (dec,)

														return DecoderOutput(sample=lowerCamelCase_  )