





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 				A_				(      unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Optional[Any]      =  ViTImageProcessor if is_vision_available() else None







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







       def    __UpperCAmelCase (    self	:	Dict  )						->					Tuple:
         _lowercase							= (3, 32, 128)
         _lowercase							= tempfile.mkdtemp()

         # fmt: off
         _lowercase							= ["""[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							= dict(zip(lowercase__						,range(len(lowercase__  )  )  )  )

         _lowercase							= 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(lowercase__  ) + '\n'  )

         _lowercase							= {
             """do_normalize""": False,
             """do_resize""": True,
             """image_processor_type""": """ViTImageProcessor""",
             """resample""": 3,
             """size""": {"""height""": 32, """width""": 128},
         }
         _lowercase							= os.path.join(self.tmpdirname						,lowercase__  )
         with open(self.image_processor_file						,'w'						,encoding='utf-8'  ) as fp:
           json.dump(lowercase__						,lowercase__  )







       def    __UpperCAmelCase (    self	:	Dict						,**__A	:	List[Any]  )						->					Dict:
         return MgpstrTokenizer.from_pretrained(self.tmpdirname						,**lowercase__  )







       def    __UpperCAmelCase (    self	:	int						,**__A	:	List[str]  )						->					List[str]:
         return ViTImageProcessor.from_pretrained(self.tmpdirname						,**lowercase__  )







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







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Dict:
         _lowercase							= np.random.randint(255						,size=(3, 30, 400)						,dtype=np.uinta  )

         _lowercase							= Image.fromarray(np.moveaxis(lowercase__						,0						,-1  )  )

         return image_input







       def    __UpperCAmelCase (    self	:	List[str]  )						->					Optional[Any]:
         _lowercase							= self.get_tokenizer()
         _lowercase							= self.get_image_processor()

         _lowercase							= MgpstrProcessor(tokenizer=lowercase__						,image_processor=lowercase__  )
         processor.save_pretrained(self.tmpdirname  )
         _lowercase							= MgpstrProcessor.from_pretrained(self.tmpdirname						,use_fast=lowercase__  )

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

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







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					List[Any]:
         _lowercase							= self.get_tokenizer()
         _lowercase							= self.get_image_processor()

         _lowercase							= MgpstrProcessor(tokenizer=lowercase__						,image_processor=lowercase__  )
         processor.save_pretrained(self.tmpdirname  )

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

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

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

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







       def    __UpperCAmelCase (    self	:	Dict  )						->					Optional[Any]:
         _lowercase							= self.get_image_processor()
         _lowercase							= self.get_tokenizer()

         _lowercase							= MgpstrProcessor(tokenizer=lowercase__						,image_processor=lowercase__  )

         _lowercase							= self.prepare_image_inputs()

         _lowercase							= image_processor(lowercase__						,return_tensors='np'  )
         _lowercase							= processor(images=lowercase__						,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	:	Dict  )						->					Optional[Any]:
         _lowercase							= self.get_image_processor()
         _lowercase							= self.get_tokenizer()

         _lowercase							= MgpstrProcessor(tokenizer=lowercase__						,image_processor=lowercase__  )

         _lowercase							= """test"""

         _lowercase							= processor(text=lowercase__  )

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







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Optional[int]:
         _lowercase							= self.get_image_processor()
         _lowercase							= self.get_tokenizer()

         _lowercase							= MgpstrProcessor(tokenizer=lowercase__						,image_processor=lowercase__  )

         _lowercase							= """test"""
         _lowercase							= self.prepare_image_inputs()

         _lowercase							= processor(text=lowercase__						,images=lowercase__  )

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

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







       def    __UpperCAmelCase (    self	:	List[str]  )						->					int:
         _lowercase							= self.get_image_processor()
         _lowercase							= self.get_tokenizer()

         _lowercase							= MgpstrProcessor(tokenizer=lowercase__						,image_processor=lowercase__  )

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

         _lowercase							= processor.char_decode(lowercase__  )
         _lowercase							= tokenizer.batch_decode(lowercase__  )
         _lowercase							= [seq.replace(' '						,''  ) for seq in decoded_tok]

         self.assertListEqual(lowercase__						,lowercase__  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					str:
         _lowercase							= self.get_image_processor()
         _lowercase							= self.get_tokenizer()

         _lowercase							= MgpstrProcessor(tokenizer=lowercase__						,image_processor=lowercase__  )

         _lowercase							= None
         _lowercase							= self.prepare_image_inputs()

         _lowercase							= processor(text=lowercase__						,images=lowercase__  )

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







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					str:
         _lowercase							= self.get_image_processor()
         _lowercase							= self.get_tokenizer()

         _lowercase							= MgpstrProcessor(tokenizer=lowercase__						,image_processor=lowercase__  )

         _lowercase							= torch.randn(1						,27						,38  )
         _lowercase							= torch.randn(1						,27						,5_0257  )
         _lowercase							= torch.randn(1						,27						,3_0522  )

         _lowercase							= processor.batch_decode([char_input, bpe_input, wp_input]  )

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






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


snake_case 				=       logging.get_logger(__name__)

snake_case 				=       {
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : str      =  '''gptj'''
       SCREAMING_SNAKE_CASE_      : int      =  {
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }







       def __init__(    self	:	str						,__A	:	List[str]=5_0400						,__A	:	Dict=2048						,__A	:	List[str]=4096						,__A	:	Dict=28						,__A	:	Optional[int]=16						,__A	:	Tuple=64						,__A	:	Optional[int]=None						,__A	:	Optional[int]="gelu_new"						,__A	:	Dict=0.0						,__A	:	List[str]=0.0						,__A	:	Optional[int]=0.0						,__A	:	str=1e-5						,__A	:	List[Any]=0.02						,__A	:	str=True						,__A	:	int=5_0256						,__A	:	Union[str, Any]=5_0256						,__A	:	int=False						,**__A	:	List[Any]						,)						->					str:
         _lowercase							= vocab_size
         _lowercase							= n_positions
         _lowercase							= n_embd
         _lowercase							= n_layer
         _lowercase							= n_head
         _lowercase							= n_inner
         _lowercase							= rotary_dim
         _lowercase							= activation_function
         _lowercase							= resid_pdrop
         _lowercase							= embd_pdrop
         _lowercase							= attn_pdrop
         _lowercase							= layer_norm_epsilon
         _lowercase							= initializer_range
         _lowercase							= use_cache

         _lowercase							= bos_token_id
         _lowercase							= eos_token_id

         super().__init__(
             bos_token_id=__A						,eos_token_id=__A						,tie_word_embeddings=__A						,**__A  )





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Tuple						,__A	:	PretrainedConfig						,__A	:	str = "default"						,__A	:	List[PatchingSpec] = None						,__A	:	bool = False						,)						->					Any:
         super().__init__(__A						,task=__A						,patching_specs=__A						,use_past=__A  )
         if not getattr(self._config						,'pad_token_id'						,__A  ):
           # TODO: how to do that better?
           _lowercase							= 0







       @property
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Mapping[str, Mapping[int, str]]:
         _lowercase							= OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}  )
         if self.use_past:
           self.fill_with_past_key_values_(__A						,direction='inputs'  )
           _lowercase							= {0: 'batch', 1: 'past_sequence + sequence'}
         else:
           _lowercase							= {0: 'batch', 1: 'sequence'}

         return common_inputs







       @property
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					int:
         return self._config.n_layer







       @property
       def    __UpperCAmelCase (    self	:	Tuple  )						->					int:
         return self._config.n_head







       def    __UpperCAmelCase (    self	:	Any						,__A	:	PreTrainedTokenizer						,__A	:	int = -1						,__A	:	int = -1						,__A	:	bool = False						,__A	:	Optional[TensorType] = None						,)						->					Mapping[str, Any]:
         _lowercase							= super(__A						,self  ).generate_dummy_inputs(
             __A						,batch_size=__A						,seq_length=__A						,is_pair=__A						,framework=__A  )

         # We need to order the input in the way they appears in the forward()
         _lowercase							= OrderedDict({'input_ids': common_inputs['input_ids']}  )

         # Need to add the past_keys
         if self.use_past:
           if not is_torch_available():
             raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'  )
           else:
             import torch

             _lowercase						,		_lowercase							= common_inputs['input_ids'].shape
             # Not using the same length for past_key_values
             _lowercase							= seqlen + 2
             _lowercase							= (
                 batch,
                 self.num_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // self.num_attention_heads,
             )
             _lowercase							= [
                 (torch.zeros(__A  ), torch.zeros(__A  )) for _ in range(self.num_layers  )
             ]

         _lowercase							= common_inputs['attention_mask']
         if self.use_past:
           _lowercase							= ordered_inputs['attention_mask'].dtype
           _lowercase							= torch.cat(
               [ordered_inputs['attention_mask'], torch.ones(__A						,__A						,dtype=__A  )]						,dim=1  )

         return ordered_inputs







       @property
       def    __UpperCAmelCase (    self	:	str  )						->					int:
         return 13