






import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.bert.modeling_flax_bert import (
							    FlaxBertForMaskedLM,
							    FlaxBertForMultipleChoice,
							    FlaxBertForNextSentencePrediction,
							    FlaxBertForPreTraining,
							    FlaxBertForQuestionAnswering,
							    FlaxBertForSequenceClassification,
							    FlaxBertForTokenClassification,
							    FlaxBertModel,
							)

class 				lowerCAmelCase_	(		unittest.TestCase			):


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=1_3 ,						_lowerCAmelCase=7 ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=9_9 ,						_lowerCAmelCase=3_2 ,						_lowerCAmelCase=5 ,						_lowerCAmelCase=4 ,						_lowerCAmelCase=3_7 ,						_lowerCAmelCase="gelu" ,						_lowerCAmelCase=0.1 ,						_lowerCAmelCase=0.1 ,						_lowerCAmelCase=5_1_2 ,						_lowerCAmelCase=1_6 ,						_lowerCAmelCase=2 ,						_lowerCAmelCase=0.02 ,						_lowerCAmelCase=4 ,						):
											_lowercase							:   Optional[Any] 							=   parent
											_lowercase							:   Any 							=   batch_size
											_lowercase							:   str 							=   seq_length
											_lowercase							:   Union[str, Any] 							=   is_training
											_lowercase							:   Tuple 							=   use_attention_mask
											_lowercase							:   List[str] 							=   use_token_type_ids
											_lowercase							:   Tuple 							=   use_labels
											_lowercase							:   Tuple 							=   vocab_size
											_lowercase							:   List[Any] 							=   hidden_size
											_lowercase							:   List[Any] 							=   num_hidden_layers
											_lowercase							:   List[Any] 							=   num_attention_heads
											_lowercase							:   Any 							=   intermediate_size
											_lowercase							:   List[str] 							=   hidden_act
											_lowercase							:   Dict 							=   hidden_dropout_prob
											_lowercase							:   int 							=   attention_probs_dropout_prob
											_lowercase							:   str 							=   max_position_embeddings
											_lowercase							:   Tuple 							=   type_vocab_size
											_lowercase							:   Optional[int] 							=   type_sequence_label_size
											_lowercase							:   Optional[int] 							=   initializer_range
											_lowercase							:   List[Any] 							=   num_choices


				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size      )

											_lowercase							:   str 							=   None
											if self.use_attention_mask:
																		_lowercase							:   int 							=   random_attention_mask([self.batch_size, self.seq_length]      )

											_lowercase							:   Optional[Any] 							=   None
											if self.use_token_type_ids:
																		_lowercase							:   Dict 							=   ids_tensor([self.batch_size, self.seq_length] ,						self.type_vocab_size      )

											_lowercase							:   Any 							=   BertConfig(
											    vocab_size=self.vocab_size ,						hidden_size=self.hidden_size ,						num_hidden_layers=self.num_hidden_layers ,						num_attention_heads=self.num_attention_heads ,						intermediate_size=self.intermediate_size ,						hidden_act=self.hidden_act ,						hidden_dropout_prob=self.hidden_dropout_prob ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob ,						max_position_embeddings=self.max_position_embeddings ,						type_vocab_size=self.type_vocab_size ,						is_decoder=_lowerCAmelCase ,						initializer_range=self.initializer_range ,						)

											return config, input_ids, token_type_ids, attention_mask


				def 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   self.prepare_config_and_inputs()
											_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							:   Dict 							=   config_and_inputs
											_lowercase							:   Dict 							=   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
											return config, inputs_dict


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   self.prepare_config_and_inputs()
											_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							:   List[Any] 							=   config_and_inputs

											_lowercase							:   Any 							=   True
											_lowercase							:   str 							=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
											_lowercase							:   Tuple 							=   ids_tensor([self.batch_size, self.seq_length] ,						vocab_size=2      )

											return (
											    config,
											    input_ids,
											    attention_mask,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)




@require_flax
class 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       Optional[Any]							       = True

				_UpperCamelCase					:       int							       = (
				    (
				        FlaxBertModel,
				        FlaxBertForPreTraining,
				        FlaxBertForMaskedLM,
				        FlaxBertForMultipleChoice,
				        FlaxBertForQuestionAnswering,
				        FlaxBertForNextSentencePrediction,
				        FlaxBertForSequenceClassification,
				        FlaxBertForTokenClassification,
				        FlaxBertForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)


				def 						__a					(					self      ):
											_lowercase							:   Optional[Any] 							=   FlaxBertModelTester(self      )


				@slow
				def 						__a					(					self      ):
											# Only check this for base model, not necessary for all model classes.
											# This will also help speed-up tests.
											_lowercase							:   Union[str, Any] 							=   FlaxBertModel.from_pretrained('bert-base-cased'      )
											_lowercase							:   int 							=   model(np.ones((1, 1)      )      )
											self.assertIsNotNone(_lowerCAmelCase      )








# 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			:			Union[str, Any]												=					'''tiny-wmt19-en-ru'''

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
	a			:			Union[str, Any]												=					Path(tmpdirname)
	a			:			Dict												=					build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
	a			:			Optional[int]												=					build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
	a			:			Tuple												=					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			:			Optional[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=1000,
    tgt_vocab_size=1000,
    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			:			Dict												=					tokenizer(['''Making tiny model'''], return_tensors='''pt''')
a			:			Union[str, Any]												=					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


