



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):




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase=13	,__lowerCamelCase=7	,__lowerCamelCase=True	,__lowerCamelCase=True	,__lowerCamelCase=True	,__lowerCamelCase=True	,__lowerCamelCase=99	,__lowerCamelCase=32	,__lowerCamelCase=5	,__lowerCamelCase=4	,__lowerCamelCase=37	,__lowerCamelCase="gelu"	,__lowerCamelCase=0.1	,__lowerCamelCase=0.1	,__lowerCamelCase=5_12	,__lowerCamelCase=16	,__lowerCamelCase=2	,__lowerCamelCase=0.02	,__lowerCamelCase=4	,)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  parent
						lowerCAmelCase__  :       Dict				=  batch_size
						lowerCAmelCase__  :       Dict				=  seq_length
						lowerCAmelCase__  :       Any				=  is_training
						lowerCAmelCase__  :       List[str]				=  use_attention_mask
						lowerCAmelCase__  :       int				=  use_token_type_ids
						lowerCAmelCase__  :       Union[str, Any]				=  use_labels
						lowerCAmelCase__  :       Optional[int]				=  vocab_size
						lowerCAmelCase__  :       Tuple				=  hidden_size
						lowerCAmelCase__  :       Tuple				=  num_hidden_layers
						lowerCAmelCase__  :       Optional[int]				=  num_attention_heads
						lowerCAmelCase__  :       Tuple				=  intermediate_size
						lowerCAmelCase__  :       Any				=  hidden_act
						lowerCAmelCase__  :       Optional[Any]				=  hidden_dropout_prob
						lowerCAmelCase__  :       int				=  attention_probs_dropout_prob
						lowerCAmelCase__  :       Dict				=  max_position_embeddings
						lowerCAmelCase__  :       List[str]				=  type_vocab_size
						lowerCAmelCase__  :       Union[str, Any]				=  type_sequence_label_size
						lowerCAmelCase__  :       Any				=  initializer_range
						lowerCAmelCase__  :       List[Any]				=  num_choices



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size				)

						lowerCAmelCase__  :       List[Any]				=  None
						if self.use_attention_mask:
										lowerCAmelCase__  :       Any				=  random_attention_mask([self.batch_size, self.seq_length]				)

						lowerCAmelCase__  :       Union[str, Any]				=  None
						if self.use_token_type_ids:
										lowerCAmelCase__  :       Optional[Any]				=  ids_tensor([self.batch_size, self.seq_length]	,self.type_vocab_size				)

						lowerCAmelCase__  :       Union[str, 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 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.prepare_config_and_inputs()
						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       List[str]				=  config_and_inputs
						lowerCAmelCase__  :       Tuple				=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
						return config, inputs_dict



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  self.prepare_config_and_inputs()
						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Tuple				=  config_and_inputs

						lowerCAmelCase__  :       Optional[Any]				=  True
						lowerCAmelCase__  :       Dict				=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
						lowerCAmelCase__  :       Dict				=  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__    (							lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=True

		snake_case_			=(
		    (
		        FlaxBertModel,
		        FlaxBertForPreTraining,
		        FlaxBertForMaskedLM,
		        FlaxBertForMultipleChoice,
		        FlaxBertForQuestionAnswering,
		        FlaxBertForNextSentencePrediction,
		        FlaxBertForSequenceClassification,
		        FlaxBertForTokenClassification,
		        FlaxBertForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  FlaxBertModelTester(self				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  FlaxBertModel.from_pretrained('''bert-base-cased'''				)
						lowerCAmelCase__  :       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

__snake_case       :	List[str]            ='tiny-wmt19-en-ru'

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case       :	Optional[Any]            =Path(tmpdirname)
							__snake_case       :	int            =build_dir / VOCAB_FILES_NAMES['src_vocab_file']
							__snake_case       :	str            =build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
							__snake_case       :	Optional[int]            =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))

							__snake_case       :	Union[str, Any]            =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,
							)

__snake_case       :	Dict            =FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    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,
)

__snake_case       :	Optional[Any]            =FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__snake_case       :	Dict            =tokenizer(['Making tiny model'], return_tensors='pt')
__snake_case       :	Dict            =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

