


'''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

__UpperCAmelCase       							=				'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
__UpperCAmelCase       							=				[
    '''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>''',
]
__UpperCAmelCase       							=				dict(zip(vocab, range(len(vocab))))
__UpperCAmelCase       							=				['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
		__UpperCAmelCase       							=				Path(tmpdirname)
		__UpperCAmelCase       							=				build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
		__UpperCAmelCase       							=				build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
		__UpperCAmelCase       							=				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))

		__UpperCAmelCase       							=				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,
		)

__UpperCAmelCase       							=				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,
)

__UpperCAmelCase       							=				FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__UpperCAmelCase       							=				tokenizer(['''Making tiny model'''], return_tensors='''pt''')
__UpperCAmelCase       							=				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


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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class 					lowercase							(			unittest.TestCase     ):




				def        a				(   self			):
											snake_case_     							=       tempfile.mkdtemp()

											snake_case_     							=       BlipImageProcessor()
											snake_case_     							=       GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'			)

											snake_case_     							=       BlipaProcessor(snake_case ,  snake_case			)

											processor.save_pretrained(self.tmpdirname			)




				def        a				(   self ,  **snake_case			):
											return AutoProcessor.from_pretrained(self.tmpdirname ,  **snake_case			).tokenizer




				def        a				(   self ,  **snake_case			):
											return AutoProcessor.from_pretrained(self.tmpdirname ,  **snake_case			).image_processor




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




				def        a				(   self			):
											snake_case_     							=       [np.random.randint(255 ,  size=(3, 30, 400) ,  dtype=np.uinta			)]

											snake_case_     							=       [Image.fromarray(np.moveaxis(snake_case ,  0 ,  -1			)			) for x in image_inputs]

											return image_inputs




				def        a				(   self			):
											snake_case_     							=       BlipaProcessor(tokenizer=self.get_tokenizer() ,  image_processor=self.get_image_processor()			)
											processor.save_pretrained(self.tmpdirname			)

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

											snake_case_     							=       BlipaProcessor.from_pretrained(
											    self.tmpdirname ,  bos_token='(BOS)' ,  eos_token='(EOS)' ,  do_normalize=snake_case ,  padding_value=1.0			)

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

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




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       self.prepare_image_inputs()

											snake_case_     							=       image_processor(snake_case ,  return_tensors='np'			)
											snake_case_     							=       processor(images=snake_case ,  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        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       'lower newer'

											snake_case_     							=       processor(text=snake_case			)

											snake_case_     							=       tokenizer(snake_case ,  return_token_type_ids=snake_case			)

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




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       'lower newer'
											snake_case_     							=       self.prepare_image_inputs()

											snake_case_     							=       processor(text=snake_case ,  images=snake_case			)

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

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




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

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

											snake_case_     							=       processor.batch_decode(snake_case			)
											snake_case_     							=       tokenizer.batch_decode(snake_case			)

											self.assertListEqual(snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       'lower newer'
											snake_case_     							=       self.prepare_image_inputs()

											snake_case_     							=       processor(text=snake_case ,  images=snake_case			)

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




