


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   ):
       lowercase_ :	Optional[Any]     =			ViTImageProcessor if is_vision_available() else None
       @property
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           return self.image_processor_tester.prepare_image_processor_dict()
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							int      		=		(3, 32, 1_28)
           _lowercase       :							str      		=		tempfile.mkdtemp()

           # fmt: off
           _lowercase       :							Tuple      		=		['[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       :							Optional[Any]      		=		dict(zip(lowerCamelCase,      range(len(lowerCamelCase))))

           _lowercase       :							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(lowerCamelCase) + '\n')

           _lowercase       :							List[Any]      		=		{
               'do_normalize': False,
               'do_resize': True,
               'image_processor_type': 'ViTImageProcessor',
               'resample': 3,
               'size': {'height': 32, 'width': 1_28},
           }
           _lowercase       :							Any      		=		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,      **lowerCamelCase)       ->   int:




           """simple docstring"""

           return MgpstrTokenizer.from_pretrained(self.tmpdirname,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      **lowerCamelCase)       ->   Optional[Any]:




           """simple docstring"""

           return ViTImageProcessor.from_pretrained(self.tmpdirname,      **lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           shutil.rmtree(self.tmpdirname)
       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           _lowercase       :							List[str]      		=		np.random.randint(2_55,      size=(3, 30, 4_00),      dtype=np.uinta)

           _lowercase       :							Union[str, Any]      		=		Image.fromarray(np.moveaxis(lowerCamelCase,      0,      -1))

           return image_input
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

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

           _lowercase       :							Any      		=		MgpstrProcessor(tokenizer=lowerCamelCase,      image_processor=lowerCamelCase)
           processor.save_pretrained(self.tmpdirname)
           _lowercase       :							Optional[int]      		=		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 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							str      		=		self.get_tokenizer()
           _lowercase       :							Any      		=		self.get_image_processor()

           _lowercase       :							str      		=		MgpstrProcessor(tokenizer=lowerCamelCase,      image_processor=lowerCamelCase)
           processor.save_pretrained(self.tmpdirname)

           _lowercase       :							List[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       :							Dict      		=		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 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

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

           _lowercase       :							Dict      		=		MgpstrProcessor(tokenizer=lowerCamelCase,      image_processor=lowerCamelCase)

           _lowercase       :							Any      		=		self.prepare_image_inputs()

           _lowercase       :							str      		=		image_processor(lowerCamelCase,      return_tensors='np')
           _lowercase       :							Optional[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 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

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

           _lowercase       :							Dict      		=		MgpstrProcessor(tokenizer=lowerCamelCase,      image_processor=lowerCamelCase)

           _lowercase       :							Tuple      		=		'test'

           _lowercase       :							List[str]      		=		processor(text=lowerCamelCase)

           _lowercase       :							List[str]      		=		tokenizer(lowerCamelCase)
           for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key],      encoded_processor[key])
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

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

           _lowercase       :							List[Any]      		=		MgpstrProcessor(tokenizer=lowerCamelCase,      image_processor=lowerCamelCase)

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

           _lowercase       :							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 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

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

           _lowercase       :							Optional[int]      		=		MgpstrProcessor(tokenizer=lowerCamelCase,      image_processor=lowerCamelCase)

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

           _lowercase       :							Dict      		=		processor.char_decode(lowerCamelCase)
           _lowercase       :							List[str]      		=		tokenizer.batch_decode(lowerCamelCase)
           _lowercase       :							Dict      		=		[seq.replace(' ',      '') for seq in decoded_tok]

           self.assertListEqual(lowerCamelCase,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		self.get_image_processor()
           _lowercase       :							Optional[Any]      		=		self.get_tokenizer()

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

           _lowercase       :							Dict      		=		None
           _lowercase       :							Dict      		=		self.prepare_image_inputs()

           _lowercase       :							int      		=		processor(text=lowerCamelCase,      images=lowerCamelCase)

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




       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           _lowercase       :							Dict      		=		self.get_image_processor()
           _lowercase       :							Optional[Any]      		=		self.get_tokenizer()

           _lowercase       :							List[str]      		=		MgpstrProcessor(tokenizer=lowerCamelCase,      image_processor=lowerCamelCase)

           _lowercase       :							str      		=		torch.randn(1,      27,      38)
           _lowercase       :							List[Any]      		=		torch.randn(1,      27,      5_02_57)
           _lowercase       :							List[Any]      		=		torch.randn(1,      27,      3_05_22)

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

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE       :      str												= {
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Tuple												= ["LlamaTokenizer"]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= ["LlamaTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      int												= [
     "LlamaForCausalLM",
     "LlamaModel",
     "LlamaPreTrainedModel",
     "LlamaForSequenceClassification",
 ]


if TYPE_CHECKING:
 from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_llama import LlamaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_llama_fast import LlamaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
 import sys

 SCREAMING_SNAKE_CASE       :      Optional[int]												= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


