


'''simple docstring'''






import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
		import os

		import jax.numpy as jnp
		from jax import jit

		from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
		from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

		__UpperCAmelCase       							=				'''0.12'''  # assumed parallelism: 8


if is_torch_available():
		import torch


def    _snake_case  (	A					,       A					,       A=None			)		->       List[str]:
							if rng is None:
														lowerCAmelCase__			  =      random.Random()

							lowerCAmelCase__			  =      1
							for dim in shape:
														total_dims *= dim

							lowerCAmelCase__			  =      []
							for _ in range(A			):
														values.append(rng.randint(0					,       vocab_size - 1			)			)

							lowerCAmelCase__			  =      np.array(A					,       dtype=jnp.intaa			).reshape(A			)

							return output


def    _snake_case  (	A					,       A=None			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      ids_tensor(A					,       vocab_size=2					,       rng=A			)
							# make sure that at least one token is attended to for each batch
							lowerCAmelCase__			  =      1
							return attn_mask




@require_flax
class 						a__							:





							'''simple docstring'''






							lowercase__     :  Optional[Any]      		= None
							lowercase__     :  Union[str, Any]      		= ()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_for_common()

														# cut to half length & take max batch_size 3
														lowerCAmelCase__			  =      2
														lowerCAmelCase__			  =      inputs['''input_ids'''].shape[-1] // 2
														lowerCAmelCase__			  =      inputs['''input_ids'''][:max_batch_size, :sequence_length]

														lowerCAmelCase__			  =      jnp.ones_like(lowerCamelCase_  )
														lowerCAmelCase__			  =      attention_mask[:max_batch_size, :sequence_length]

														# generate max 5 tokens
														lowerCAmelCase__			  =      input_ids.shape[-1] + 5
														if config.eos_token_id is not None and config.pad_token_id is None:
																					# hack to allow generate for models such as GPT2 as is done in `generate()`
																					lowerCAmelCase__			  =      config.eos_token_id
														return config, input_ids, attention_mask, max_length







							@is_pt_flax_cross_test
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      self._get_input_ids_and_config()
														lowerCAmelCase__			  =      False
														lowerCAmelCase__			  =      max_length
														lowerCAmelCase__			  =      0

														for model_class in self.all_generative_model_classes:
																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )

																					lowerCAmelCase__			  =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					lowerCAmelCase__			  =      getattr(lowerCamelCase_   ,       lowerCamelCase_  )
																					lowerCAmelCase__			  =      pt_model_class(lowerCamelCase_  ).eval()
																					lowerCAmelCase__			  =      load_flax_weights_in_pytorch_model(lowerCamelCase_   ,       flax_model.params  )

																					lowerCAmelCase__			  =      flax_model.generate(lowerCamelCase_  ).sequences
																					lowerCAmelCase__			  =      pt_model.generate(torch.tensor(lowerCamelCase_   ,       dtype=torch.long  )  )

																					if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																												lowerCAmelCase__			  =      flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																					self.assertListEqual(pt_generation_outputs.numpy().tolist()   ,       flax_generation_outputs.tolist()  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      self._get_input_ids_and_config()
														lowerCAmelCase__			  =      False
														lowerCAmelCase__			  =      max_length

														for model_class in self.all_generative_model_classes:
																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )

																					lowerCAmelCase__			  =      model.generate(lowerCamelCase_  ).sequences
																					self.assertEqual(generation_outputs.shape[-1]   ,       lowerCamelCase_  )

																					lowerCAmelCase__			  =      jit(model.generate  )
																					lowerCAmelCase__			  =      jit_generate(lowerCamelCase_  ).sequences

																					self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      self._get_input_ids_and_config()
														lowerCAmelCase__			  =      True
														lowerCAmelCase__			  =      max_length

														for model_class in self.all_generative_model_classes:
																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )

																					lowerCAmelCase__			  =      model.generate(lowerCamelCase_  ).sequences
																					self.assertEqual(generation_outputs.shape[-1]   ,       lowerCamelCase_  )

																					lowerCAmelCase__			  =      jit(model.generate  )
																					lowerCAmelCase__			  =      jit_generate(lowerCamelCase_  ).sequences

																					self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      self._get_input_ids_and_config()
														lowerCAmelCase__			  =      False
														lowerCAmelCase__			  =      max_length
														lowerCAmelCase__			  =      2

														for model_class in self.all_generative_model_classes:
																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )

																					lowerCAmelCase__			  =      model.generate(lowerCamelCase_  ).sequences
																					self.assertEqual(generation_outputs.shape[-1]   ,       lowerCamelCase_  )

																					lowerCAmelCase__			  =      jit(model.generate  )
																					lowerCAmelCase__			  =      jit_generate(lowerCamelCase_  ).sequences

																					self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      self._get_input_ids_and_config()
														lowerCAmelCase__			  =      False
														lowerCAmelCase__			  =      max_length
														lowerCAmelCase__			  =      2
														lowerCAmelCase__			  =      2

														for model_class in self.all_generative_model_classes:
																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )

																					lowerCAmelCase__			  =      model.generate(lowerCamelCase_  ).sequences
																					self.assertEqual(generation_outputs.shape[0]   ,       input_ids.shape[0] * config.num_return_sequences  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      self._get_input_ids_and_config()
														lowerCAmelCase__			  =      True
														lowerCAmelCase__			  =      max_length
														lowerCAmelCase__			  =      0.8
														lowerCAmelCase__			  =      10
														lowerCAmelCase__			  =      0.3
														lowerCAmelCase__			  =      1
														lowerCAmelCase__			  =      8
														lowerCAmelCase__			  =      9

														for model_class in self.all_generative_model_classes:
																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )

																					lowerCAmelCase__			  =      model.generate(lowerCamelCase_  ).sequences
																					self.assertEqual(generation_outputs.shape[-1]   ,       lowerCamelCase_  )

																					lowerCAmelCase__			  =      jit(model.generate  )
																					lowerCAmelCase__			  =      jit_generate(lowerCamelCase_  ).sequences

																					self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      self._get_input_ids_and_config()
														lowerCAmelCase__			  =      max_length
														lowerCAmelCase__			  =      1
														lowerCAmelCase__			  =      8
														lowerCAmelCase__			  =      9

														for model_class in self.all_generative_model_classes:
																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )

																					lowerCAmelCase__			  =      model.generate(lowerCamelCase_  ).sequences
																					self.assertEqual(generation_outputs.shape[-1]   ,       lowerCamelCase_  )

																					lowerCAmelCase__			  =      jit(model.generate  )
																					lowerCAmelCase__			  =      jit_generate(lowerCamelCase_  ).sequences

																					self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      self._get_input_ids_and_config()
														lowerCAmelCase__			  =      max_length
														lowerCAmelCase__			  =      2
														lowerCAmelCase__			  =      1
														lowerCAmelCase__			  =      8
														lowerCAmelCase__			  =      9

														for model_class in self.all_generative_model_classes:
																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )

																					lowerCAmelCase__			  =      model.generate(lowerCamelCase_  ).sequences
																					self.assertEqual(generation_outputs.shape[-1]   ,       lowerCamelCase_  )

																					lowerCAmelCase__			  =      jit(model.generate  )
																					lowerCAmelCase__			  =      jit_generate(lowerCamelCase_  ).sequences

																					self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      self._get_input_ids_and_config()

														# pad attention mask on the left
														lowerCAmelCase__			  =      attention_mask.at[(0, 0)].set(0  )

														lowerCAmelCase__			  =      False
														lowerCAmelCase__			  =      max_length

														for model_class in self.all_generative_model_classes:
																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )

																					lowerCAmelCase__			  =      model.generate(lowerCamelCase_   ,       attention_mask=lowerCamelCase_  ).sequences
																					self.assertEqual(generation_outputs.shape[-1]   ,       lowerCamelCase_  )

																					lowerCAmelCase__			  =      jit(model.generate  )
																					lowerCAmelCase__			  =      jit_generate(lowerCamelCase_   ,       attention_mask=lowerCamelCase_  ).sequences

																					self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      self._get_input_ids_and_config()

														# pad attention mask on the left
														lowerCAmelCase__			  =      attention_mask.at[(0, 0)].set(0  )

														lowerCAmelCase__			  =      True
														lowerCAmelCase__			  =      max_length

														for model_class in self.all_generative_model_classes:
																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )

																					lowerCAmelCase__			  =      model.generate(lowerCamelCase_   ,       attention_mask=lowerCamelCase_  ).sequences
																					self.assertEqual(generation_outputs.shape[-1]   ,       lowerCamelCase_  )

																					lowerCAmelCase__			  =      jit(model.generate  )
																					lowerCAmelCase__			  =      jit_generate(lowerCamelCase_   ,       attention_mask=lowerCamelCase_  ).sequences

																					self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      self._get_input_ids_and_config()

														# pad attention mask on the left
														lowerCAmelCase__			  =      attention_mask.at[(0, 0)].set(0  )

														lowerCAmelCase__			  =      2
														lowerCAmelCase__			  =      max_length

														for model_class in self.all_generative_model_classes:
																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )

																					lowerCAmelCase__			  =      model.generate(lowerCamelCase_   ,       attention_mask=lowerCamelCase_  ).sequences
																					self.assertEqual(generation_outputs.shape[-1]   ,       lowerCamelCase_  )

																					lowerCAmelCase__			  =      jit(model.generate  )
																					lowerCAmelCase__			  =      jit_generate(lowerCamelCase_   ,       attention_mask=lowerCamelCase_  ).sequences

																					self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()  )




@require_flax
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''  )
														lowerCAmelCase__			  =      FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''  )

														lowerCAmelCase__			  =      '''Hello world'''
														lowerCAmelCase__			  =      tokenizer(lowerCamelCase_   ,       return_tensors='''np'''  ).input_ids

														# typos are quickly detected (the correct argument is `do_sample`)
														with self.assertRaisesRegex(lowerCamelCase_   ,       '''do_samples'''  ):
																					model.generate(lowerCamelCase_   ,       do_samples=lowerCamelCase_  )

														# arbitrary arguments that will not be used anywhere are also not accepted
														with self.assertRaisesRegex(lowerCamelCase_   ,       '''foo'''  ):
																					lowerCAmelCase__			  =      {'''foo''': '''bar'''}
																					model.generate(lowerCamelCase_   ,       **lowerCamelCase_  )



'''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