

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast






@require_vision
class 							A_ (		unittest.TestCase					):


     def   UpperCAmelCase  (							self			:		List[str]					)							->	Tuple:
      __lowerCAmelCase:   Optional[int]   	=      tempfile.mkdtemp()

      __lowerCAmelCase:   Union[str, Any]   	=      BlipImageProcessor()
      __lowerCAmelCase:   str   	=      BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'					)

      __lowerCAmelCase:   Tuple   	=      BlipProcessor(UpperCamelCase__			,  UpperCamelCase__					)

      processor.save_pretrained(self.tmpdirname					)


     def   UpperCAmelCase  (							self			:		str			,  **UpperCAmelCase			:		Dict					)							->	List[str]:
      return AutoProcessor.from_pretrained(self.tmpdirname			,  **UpperCamelCase__					).tokenizer


     def   UpperCAmelCase  (							self			:		List[Any]			,  **UpperCAmelCase			:		Optional[int]					)							->	Union[str, Any]:
      return AutoProcessor.from_pretrained(self.tmpdirname			,  **UpperCamelCase__					).image_processor


     def   UpperCAmelCase  (							self			:		str					)							->	Any:
      shutil.rmtree(self.tmpdirname					)


     def   UpperCAmelCase  (							self			:		Optional[int]					)							->	Optional[Any]:
      __lowerCAmelCase:   str   	=      [np.random.randint(2_5_5			,  size=(3, 3_0, 4_0_0)			,  dtype=np.uinta					)]

      __lowerCAmelCase:   List[Any]   	=      [Image.fromarray(np.moveaxis(UpperCamelCase__			,  0			,  -1					)					) for x in image_inputs]

      return image_inputs


     def   UpperCAmelCase  (							self			:		Dict					)							->	Union[str, Any]:
      __lowerCAmelCase:   Union[str, Any]   	=      BlipProcessor(tokenizer=self.get_tokenizer()			,  image_processor=self.get_image_processor()					)
      processor.save_pretrained(self.tmpdirname					)

      __lowerCAmelCase:   Dict   	=      self.get_tokenizer(bos_token='(BOS)'			,  eos_token='(EOS)'					)
      __lowerCAmelCase:   Dict   	=      self.get_image_processor(do_normalize=UpperCamelCase__			,  padding_value=1.0					)

      __lowerCAmelCase:   Optional[Any]   	=      BlipProcessor.from_pretrained(
          self.tmpdirname			,  bos_token='(BOS)'			,  eos_token='(EOS)'			,  do_normalize=UpperCamelCase__			,  padding_value=1.0					)

      self.assertEqual(processor.tokenizer.get_vocab()			,  tokenizer_add_kwargs.get_vocab()					)
      self.assertIsInstance(processor.tokenizer			,  UpperCamelCase__					)

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


     def   UpperCAmelCase  (							self			:		int					)							->	str:
      __lowerCAmelCase:   Any   	=      self.get_image_processor()
      __lowerCAmelCase:   Tuple   	=      self.get_tokenizer()

      __lowerCAmelCase:   List[str]   	=      BlipProcessor(tokenizer=UpperCamelCase__			,  image_processor=UpperCamelCase__					)

      __lowerCAmelCase:   str   	=      self.prepare_image_inputs()

      __lowerCAmelCase:   Any   	=      image_processor(UpperCamelCase__			,  return_tensors='np'					)
      __lowerCAmelCase:   Any   	=      processor(images=UpperCamelCase__			,  return_tensors='np'					)

      for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()			,  input_processor[key].sum()			,  delta=1E-2					)


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

      __lowerCAmelCase:   List[str]   	=      BlipProcessor(tokenizer=UpperCamelCase__			,  image_processor=UpperCamelCase__					)

      __lowerCAmelCase:   Tuple   	=      """lower newer"""

      __lowerCAmelCase:   Union[str, Any]   	=      processor(text=UpperCamelCase__					)

      __lowerCAmelCase:   int   	=      tokenizer(UpperCamelCase__			,  return_token_type_ids=UpperCamelCase__					)

      for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]			,  encoded_processor[key]					)


     def   UpperCAmelCase  (							self			:		Optional[Any]					)							->	int:
      __lowerCAmelCase:   List[str]   	=      self.get_image_processor()
      __lowerCAmelCase:   List[Any]   	=      self.get_tokenizer()

      __lowerCAmelCase:   Tuple   	=      BlipProcessor(tokenizer=UpperCamelCase__			,  image_processor=UpperCamelCase__					)

      __lowerCAmelCase:   Any   	=      """lower newer"""
      __lowerCAmelCase:   Optional[Any]   	=      self.prepare_image_inputs()

      __lowerCAmelCase:   List[str]   	=      processor(text=UpperCamelCase__			,  images=UpperCamelCase__					)

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

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


     def   UpperCAmelCase  (							self			:		Dict					)							->	Dict:
      __lowerCAmelCase:   Optional[Any]   	=      self.get_image_processor()
      __lowerCAmelCase:   Union[str, Any]   	=      self.get_tokenizer()

      __lowerCAmelCase:   Any   	=      BlipProcessor(tokenizer=UpperCamelCase__			,  image_processor=UpperCamelCase__					)

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

      __lowerCAmelCase:   Dict   	=      processor.batch_decode(UpperCamelCase__					)
      __lowerCAmelCase:   List[Any]   	=      tokenizer.batch_decode(UpperCamelCase__					)

      self.assertListEqual(UpperCamelCase__			,  UpperCamelCase__					)


     def   UpperCAmelCase  (							self			:		Optional[int]					)							->	Optional[int]:
      __lowerCAmelCase:   Any   	=      self.get_image_processor()
      __lowerCAmelCase:   str   	=      self.get_tokenizer()

      __lowerCAmelCase:   Union[str, Any]   	=      BlipProcessor(tokenizer=UpperCamelCase__			,  image_processor=UpperCamelCase__					)

      __lowerCAmelCase:   Tuple   	=      """lower newer"""
      __lowerCAmelCase:   int   	=      self.prepare_image_inputs()

      __lowerCAmelCase:   Optional[int]   	=      processor(text=UpperCamelCase__			,  images=UpperCamelCase__					)

      # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
      self.assertListEqual(list(inputs.keys()					)			,  ['pixel_values', 'input_ids', 'attention_mask']					)













'''simple docstring'''







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_A	:			Dict         ='''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A	:			List[str]         =[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
_A	:			str         =dict(zip(vocab, range(len(vocab))))
_A	:			List[str]         =['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
			_A	:			Union[str, Any]         =Path(tmpdirname)
			_A	:			str         =build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			_A	:			int         =build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			_A	:			List[Any]         =build_dir / VOCAB_FILES_NAMES['''merges_file''']
			with open(src_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(tgt_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(merges_file, '''w''') as fp:
						fp.write('''\n'''.join(merges))

			_A	:			int         =FSMTTokenizer(
			    langs=['''en''', '''ru'''],
			    src_vocab_size=len(vocab),
			    tgt_vocab_size=len(vocab),
			    src_vocab_file=src_vocab_file,
			    tgt_vocab_file=tgt_vocab_file,
			    merges_file=merges_file,
			)

_A	:			List[str]         =FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_A	:			Union[str, Any]         =FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

# Test
_A	:			List[str]         =tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A	:			Tuple         =tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-ru

