





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'''




   __lowerCamelCase     :	List[str]          =   ViTImageProcessor if is_vision_available() else None






   @property
   def        a_	(      self       :   Optional[int]       )     ->     Optional[int]:







     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()






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







     """simple docstring"""






     A__  				=       (3, 32, 1_28)
     A__  				=       tempfile.mkdtemp()

     # fmt: off
     A__  				=       ["""[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__  				=       dict(zip(a_							,     range(len(a_       )       )       )       )

     A__  				=       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(a_       ) + """\n"""       )

     A__  				=       {
         """do_normalize""": False,
         """do_resize""": True,
         """image_processor_type""": """ViTImageProcessor""",
         """resample""": 3,
         """size""": {"""height""": 32, """width""": 1_28},
     }
     A__  				=       os.path.join(self.tmpdirname							,     a_       )
     with open(self.image_processor_file							,     """w"""							,     encoding="""utf-8"""       ) as fp:
       json.dump(a_							,     a_       )






   def        a_	(      self       :   int							,     **__lowerCAmelCase       :   int       )     ->     Dict:







     """simple docstring"""






     return MgpstrTokenizer.from_pretrained(self.tmpdirname							,     **a_       )






   def        a_	(      self       :   Optional[int]							,     **__lowerCAmelCase       :   str       )     ->     str:







     """simple docstring"""






     return ViTImageProcessor.from_pretrained(self.tmpdirname							,     **a_       )






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







     """simple docstring"""






     shutil.rmtree(self.tmpdirname       )






   def        a_	(      self       :   Tuple       )     ->     List[Any]:







     """simple docstring"""






     A__  				=       np.random.randint(2_55							,     size=(3, 30, 4_00)							,     dtype=np.uinta       )

     A__  				=       Image.fromarray(np.moveaxis(a_							,     0							,     -1       )       )

     return image_input






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







     """simple docstring"""






     A__  				=       self.get_tokenizer()
     A__  				=       self.get_image_processor()

     A__  				=       MgpstrProcessor(tokenizer=a_							,     image_processor=a_       )
     processor.save_pretrained(self.tmpdirname       )
     A__  				=       MgpstrProcessor.from_pretrained(self.tmpdirname							,     use_fast=a_       )

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

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






   def        a_	(      self       :   Optional[int]       )     ->     Dict:







     """simple docstring"""






     A__  				=       self.get_tokenizer()
     A__  				=       self.get_image_processor()

     A__  				=       MgpstrProcessor(tokenizer=a_							,     image_processor=a_       )
     processor.save_pretrained(self.tmpdirname       )

     A__  				=       self.get_tokenizer(bos_token="""(BOS)"""							,     eos_token="""(EOS)"""       )
     A__  				=       self.get_image_processor(do_normalize=a_							,     padding_value=1.0       )

     A__  				=       MgpstrProcessor.from_pretrained(
         self.tmpdirname							,     bos_token="""(BOS)"""							,     eos_token="""(EOS)"""							,     do_normalize=a_							,     padding_value=1.0       )

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

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






   def        a_	(      self       :   str       )     ->     Dict:







     """simple docstring"""






     A__  				=       self.get_image_processor()
     A__  				=       self.get_tokenizer()

     A__  				=       MgpstrProcessor(tokenizer=a_							,     image_processor=a_       )

     A__  				=       self.prepare_image_inputs()

     A__  				=       image_processor(a_							,     return_tensors="""np"""       )
     A__  				=       processor(images=a_							,     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       :   Dict       )     ->     List[str]:







     """simple docstring"""






     A__  				=       self.get_image_processor()
     A__  				=       self.get_tokenizer()

     A__  				=       MgpstrProcessor(tokenizer=a_							,     image_processor=a_       )

     A__  				=       """test"""

     A__  				=       processor(text=a_       )

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






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







     """simple docstring"""






     A__  				=       self.get_image_processor()
     A__  				=       self.get_tokenizer()

     A__  				=       MgpstrProcessor(tokenizer=a_							,     image_processor=a_       )

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

     A__  				=       processor(text=a_							,     images=a_       )

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

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






   def        a_	(      self       :   List[str]       )     ->     Tuple:







     """simple docstring"""






     A__  				=       self.get_image_processor()
     A__  				=       self.get_tokenizer()

     A__  				=       MgpstrProcessor(tokenizer=a_							,     image_processor=a_       )

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

     A__  				=       processor.char_decode(a_       )
     A__  				=       tokenizer.batch_decode(a_       )
     A__  				=       [seq.replace(""" """							,     """"""       ) for seq in decoded_tok]

     self.assertListEqual(a_							,     a_       )






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







     """simple docstring"""






     A__  				=       self.get_image_processor()
     A__  				=       self.get_tokenizer()

     A__  				=       MgpstrProcessor(tokenizer=a_							,     image_processor=a_       )

     A__  				=       None
     A__  				=       self.prepare_image_inputs()

     A__  				=       processor(text=a_							,     images=a_       )

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







   def        a_	(      self       :   List[str]       )     ->     int:







     """simple docstring"""






     A__  				=       self.get_image_processor()
     A__  				=       self.get_tokenizer()

     A__  				=       MgpstrProcessor(tokenizer=a_							,     image_processor=a_       )

     A__  				=       torch.randn(1							,     27							,     38       )
     A__  				=       torch.randn(1							,     27							,     5_02_57       )
     A__  				=       torch.randn(1							,     27							,     3_05_22       )

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

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










from ...configuration_utils import PretrainedConfig
from ...utils import logging


A   : Dict			=				logging.get_logger(__name__)

A   : Dict			=				{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Tuple          =   '''pegasus'''
			__lowerCamelCase     :	Any          =   ['''past_key_values''']
			__lowerCamelCase     :	List[str]          =   {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






			def __init__(      self       :   List[Any]							,     __lowerCAmelCase       :   str=5_02_65							,     __lowerCAmelCase       :   Dict=10_24							,     __lowerCAmelCase       :   Union[str, Any]=12							,     __lowerCAmelCase       :   Any=40_96							,     __lowerCAmelCase       :   Optional[Any]=16							,     __lowerCAmelCase       :   Dict=12							,     __lowerCAmelCase       :   Optional[int]=40_96							,     __lowerCAmelCase       :   Dict=16							,     __lowerCAmelCase       :   Optional[int]=0.0							,     __lowerCAmelCase       :   Optional[Any]=0.0							,     __lowerCAmelCase       :   Union[str, Any]=True							,     __lowerCAmelCase       :   List[str]=True							,     __lowerCAmelCase       :   str="gelu"							,     __lowerCAmelCase       :   Union[str, Any]=10_24							,     __lowerCAmelCase       :   Union[str, Any]=0.1							,     __lowerCAmelCase       :   Dict=0.0							,     __lowerCAmelCase       :   Union[str, Any]=0.0							,     __lowerCAmelCase       :   Union[str, Any]=0.0_2							,     __lowerCAmelCase       :   List[Any]=0							,     __lowerCAmelCase       :   str=False							,     __lowerCAmelCase       :   int=0							,     __lowerCAmelCase       :   Optional[Any]=1							,     __lowerCAmelCase       :   Optional[int]=1							,     **__lowerCAmelCase       :   Dict							,     )     ->     Dict:







					"""simple docstring"""






					A__  				=       vocab_size
					A__  				=       max_position_embeddings
					A__  				=       d_model
					A__  				=       encoder_ffn_dim
					A__  				=       encoder_layers
					A__  				=       encoder_attention_heads
					A__  				=       decoder_ffn_dim
					A__  				=       decoder_layers
					A__  				=       decoder_attention_heads
					A__  				=       dropout
					A__  				=       attention_dropout
					A__  				=       activation_dropout
					A__  				=       activation_function
					A__  				=       init_std
					A__  				=       encoder_layerdrop
					A__  				=       decoder_layerdrop
					A__  				=       use_cache
					A__  				=       encoder_layers
					A__  				=       scale_embedding  # scale factor will be sqrt(d_model) if True
					super().__init__(
					    pad_token_id=__lowerCAmelCase							,     eos_token_id=__lowerCAmelCase							,     is_encoder_decoder=__lowerCAmelCase							,     decoder_start_token_id=__lowerCAmelCase							,     forced_eos_token_id=__lowerCAmelCase							,     **__lowerCAmelCase							,     )






			@property
			def        a_	(      self       :   Dict       )     ->     int:







					"""simple docstring"""






					return self.encoder_attention_heads







			@property
			def        a_	(      self       :   Optional[Any]       )     ->     int:







					"""simple docstring"""






					return self.d_model



