


'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
		import jax.numpy as jnp

if is_tf_available():
		import tensorflow as tf

if is_torch_available():
		import torch




class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      {
														    '''task_specific_params''': {
														        '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 1_28, '''min_length''': 12, '''num_beams''': 4},
														        '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 1_42, '''min_length''': 56, '''num_beams''': 4},
														        '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
														    }
														}
														lowerCAmelCase__			  =      {
														    '''task_specific_params.summarization.length_penalty''': 1.0,
														    '''task_specific_params.summarization.max_length''': 1_28,
														    '''task_specific_params.summarization.min_length''': 12,
														    '''task_specific_params.summarization.num_beams''': 4,
														    '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
														    '''task_specific_params.summarization_cnn.max_length''': 1_42,
														    '''task_specific_params.summarization_cnn.min_length''': 56,
														    '''task_specific_params.summarization_cnn.num_beams''': 4,
														    '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
														    '''task_specific_params.summarization_xsum.max_length''': 62,
														    '''task_specific_params.summarization_xsum.min_length''': 11,
														    '''task_specific_params.summarization_xsum.num_beams''': 6,
														}

														self.assertEqual(flatten_dict(lowerCamelCase_  )   ,       lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__			  =      np.random.randn(3   ,       4  )
														self.assertTrue(np.allclose(transpose(lowerCamelCase_  )   ,       x.transpose()  )  )

														lowerCAmelCase__			  =      np.random.randn(3   ,       4   ,       5  )
														self.assertTrue(np.allclose(transpose(lowerCamelCase_   ,       axes=(1, 2, 0)  )   ,       x.transpose((1, 2, 0)  )  )  )







							@require_torch
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      np.random.randn(3   ,       4  )
														lowerCAmelCase__			  =      torch.tensor(lowerCamelCase_  )
														self.assertTrue(np.allclose(transpose(lowerCamelCase_  )   ,       transpose(lowerCamelCase_  ).numpy()  )  )

														lowerCAmelCase__			  =      np.random.randn(3   ,       4   ,       5  )
														lowerCAmelCase__			  =      torch.tensor(lowerCamelCase_  )
														self.assertTrue(np.allclose(transpose(lowerCamelCase_   ,       axes=(1, 2, 0)  )   ,       transpose(lowerCamelCase_   ,       axes=(1, 2, 0)  ).numpy()  )  )







							@require_tf
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__			  =      np.random.randn(3   ,       4  )
														lowerCAmelCase__			  =      tf.constant(lowerCamelCase_  )
														self.assertTrue(np.allclose(transpose(lowerCamelCase_  )   ,       transpose(lowerCamelCase_  ).numpy()  )  )

														lowerCAmelCase__			  =      np.random.randn(3   ,       4   ,       5  )
														lowerCAmelCase__			  =      tf.constant(lowerCamelCase_  )
														self.assertTrue(np.allclose(transpose(lowerCamelCase_   ,       axes=(1, 2, 0)  )   ,       transpose(lowerCamelCase_   ,       axes=(1, 2, 0)  ).numpy()  )  )







							@require_flax
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      np.random.randn(3   ,       4  )
														lowerCAmelCase__			  =      jnp.array(lowerCamelCase_  )
														self.assertTrue(np.allclose(transpose(lowerCamelCase_  )   ,       np.asarray(transpose(lowerCamelCase_  )  )  )  )

														lowerCAmelCase__			  =      np.random.randn(3   ,       4   ,       5  )
														lowerCAmelCase__			  =      jnp.array(lowerCamelCase_  )
														self.assertTrue(np.allclose(transpose(lowerCamelCase_   ,       axes=(1, 2, 0)  )   ,       np.asarray(transpose(lowerCamelCase_   ,       axes=(1, 2, 0)  )  )  )  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      np.random.randn(3   ,       4  )
														self.assertTrue(np.allclose(reshape(lowerCamelCase_   ,       (4, 3)  )   ,       np.reshape(lowerCamelCase_   ,       (4, 3)  )  )  )

														lowerCAmelCase__			  =      np.random.randn(3   ,       4   ,       5  )
														self.assertTrue(np.allclose(reshape(lowerCamelCase_   ,       (12, 5)  )   ,       np.reshape(lowerCamelCase_   ,       (12, 5)  )  )  )







							@require_torch
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      np.random.randn(3   ,       4  )
														lowerCAmelCase__			  =      torch.tensor(lowerCamelCase_  )
														self.assertTrue(np.allclose(reshape(lowerCamelCase_   ,       (4, 3)  )   ,       reshape(lowerCamelCase_   ,       (4, 3)  ).numpy()  )  )

														lowerCAmelCase__			  =      np.random.randn(3   ,       4   ,       5  )
														lowerCAmelCase__			  =      torch.tensor(lowerCamelCase_  )
														self.assertTrue(np.allclose(reshape(lowerCamelCase_   ,       (12, 5)  )   ,       reshape(lowerCamelCase_   ,       (12, 5)  ).numpy()  )  )







							@require_tf
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      np.random.randn(3   ,       4  )
														lowerCAmelCase__			  =      tf.constant(lowerCamelCase_  )
														self.assertTrue(np.allclose(reshape(lowerCamelCase_   ,       (4, 3)  )   ,       reshape(lowerCamelCase_   ,       (4, 3)  ).numpy()  )  )

														lowerCAmelCase__			  =      np.random.randn(3   ,       4   ,       5  )
														lowerCAmelCase__			  =      tf.constant(lowerCamelCase_  )
														self.assertTrue(np.allclose(reshape(lowerCamelCase_   ,       (12, 5)  )   ,       reshape(lowerCamelCase_   ,       (12, 5)  ).numpy()  )  )







							@require_flax
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      np.random.randn(3   ,       4  )
														lowerCAmelCase__			  =      jnp.array(lowerCamelCase_  )
														self.assertTrue(np.allclose(reshape(lowerCamelCase_   ,       (4, 3)  )   ,       np.asarray(reshape(lowerCamelCase_   ,       (4, 3)  )  )  )  )

														lowerCAmelCase__			  =      np.random.randn(3   ,       4   ,       5  )
														lowerCAmelCase__			  =      jnp.array(lowerCamelCase_  )
														self.assertTrue(np.allclose(reshape(lowerCamelCase_   ,       (12, 5)  )   ,       np.asarray(reshape(lowerCamelCase_   ,       (12, 5)  )  )  )  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      np.random.randn(1   ,       3   ,       4  )
														self.assertTrue(np.allclose(squeeze(lowerCamelCase_  )   ,       np.squeeze(lowerCamelCase_  )  )  )

														lowerCAmelCase__			  =      np.random.randn(1   ,       4   ,       1   ,       5  )
														self.assertTrue(np.allclose(squeeze(lowerCamelCase_   ,       axis=2  )   ,       np.squeeze(lowerCamelCase_   ,       axis=2  )  )  )







							@require_torch
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      np.random.randn(1   ,       3   ,       4  )
														lowerCAmelCase__			  =      torch.tensor(lowerCamelCase_  )
														self.assertTrue(np.allclose(squeeze(lowerCamelCase_  )   ,       squeeze(lowerCamelCase_  ).numpy()  )  )

														lowerCAmelCase__			  =      np.random.randn(1   ,       4   ,       1   ,       5  )
														lowerCAmelCase__			  =      torch.tensor(lowerCamelCase_  )
														self.assertTrue(np.allclose(squeeze(lowerCamelCase_   ,       axis=2  )   ,       squeeze(lowerCamelCase_   ,       axis=2  ).numpy()  )  )







							@require_tf
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      np.random.randn(1   ,       3   ,       4  )
														lowerCAmelCase__			  =      tf.constant(lowerCamelCase_  )
														self.assertTrue(np.allclose(squeeze(lowerCamelCase_  )   ,       squeeze(lowerCamelCase_  ).numpy()  )  )

														lowerCAmelCase__			  =      np.random.randn(1   ,       4   ,       1   ,       5  )
														lowerCAmelCase__			  =      tf.constant(lowerCamelCase_  )
														self.assertTrue(np.allclose(squeeze(lowerCamelCase_   ,       axis=2  )   ,       squeeze(lowerCamelCase_   ,       axis=2  ).numpy()  )  )







							@require_flax
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      np.random.randn(1   ,       3   ,       4  )
														lowerCAmelCase__			  =      jnp.array(lowerCamelCase_  )
														self.assertTrue(np.allclose(squeeze(lowerCamelCase_  )   ,       np.asarray(squeeze(lowerCamelCase_  )  )  )  )

														lowerCAmelCase__			  =      np.random.randn(1   ,       4   ,       1   ,       5  )
														lowerCAmelCase__			  =      jnp.array(lowerCamelCase_  )
														self.assertTrue(np.allclose(squeeze(lowerCamelCase_   ,       axis=2  )   ,       np.asarray(squeeze(lowerCamelCase_   ,       axis=2  )  )  )  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      np.random.randn(3   ,       4  )
														self.assertTrue(np.allclose(expand_dims(lowerCamelCase_   ,       axis=1  )   ,       np.expand_dims(lowerCamelCase_   ,       axis=1  )  )  )







							@require_torch
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      np.random.randn(3   ,       4  )
														lowerCAmelCase__			  =      torch.tensor(lowerCamelCase_  )
														self.assertTrue(np.allclose(expand_dims(lowerCamelCase_   ,       axis=1  )   ,       expand_dims(lowerCamelCase_   ,       axis=1  ).numpy()  )  )







							@require_tf
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      np.random.randn(3   ,       4  )
														lowerCAmelCase__			  =      tf.constant(lowerCamelCase_  )
														self.assertTrue(np.allclose(expand_dims(lowerCamelCase_   ,       axis=1  )   ,       expand_dims(lowerCamelCase_   ,       axis=1  ).numpy()  )  )







							@require_flax
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      np.random.randn(3   ,       4  )
														lowerCAmelCase__			  =      jnp.array(lowerCamelCase_  )
														self.assertTrue(np.allclose(expand_dims(lowerCamelCase_   ,       axis=1  )   ,       np.asarray(expand_dims(lowerCamelCase_   ,       axis=1  )  )  )  )



'''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