



"""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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

UpperCamelCase     =							"""facebook/wmt19-en-de"""
UpperCamelCase     =							FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
UpperCamelCase     =							FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        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
UpperCamelCase     =							"""tiny-wmt19-en-de"""
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-de









"""simple docstring"""







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class       UpperCamelCase__			(   _lowerCAmelCase       ,		_lowerCAmelCase       ,		_lowerCAmelCase      ):

			"""simple docstring"""


			@register_to_config
			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = False						,	)      ->	Union[str, Any]:
				super().__init__()

				A__											=					nn.Embedding(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				A__											=					nn.Embedding(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				A__											=					False

				A__											=					nn.Dropout(p=SCREAMING_SNAKE_CASE__		)

				A__											=					TaConfig(
				    vocab_size=SCREAMING_SNAKE_CASE__						,	d_model=SCREAMING_SNAKE_CASE__						,	num_heads=SCREAMING_SNAKE_CASE__						,	d_kv=SCREAMING_SNAKE_CASE__						,	d_ff=SCREAMING_SNAKE_CASE__						,	dropout_rate=SCREAMING_SNAKE_CASE__						,	feed_forward_proj=SCREAMING_SNAKE_CASE__						,	is_decoder=SCREAMING_SNAKE_CASE__						,	is_encoder_decoder=SCREAMING_SNAKE_CASE__						,	)

				A__											=					nn.ModuleList()
				for lyr_num in range(SCREAMING_SNAKE_CASE__		):
					A__											=					TaBlock(SCREAMING_SNAKE_CASE__		)
					self.encoders.append(SCREAMING_SNAKE_CASE__		)

				A__											=					TaLayerNorm(SCREAMING_SNAKE_CASE__		)
				A__											=					nn.Dropout(p=SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Any:
				A__											=					self.token_embedder(SCREAMING_SNAKE_CASE__		)

				A__											=					encoder_input_tokens.shape[1]
				A__											=					torch.arange(SCREAMING_SNAKE_CASE__						,	device=encoder_input_tokens.device		)
				x += self.position_encoding(SCREAMING_SNAKE_CASE__		)

				A__											=					self.dropout_pre(SCREAMING_SNAKE_CASE__		)

				# inverted the attention mask
				A__											=					encoder_input_tokens.size()
				A__											=					self.get_extended_attention_mask(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				for lyr in self.encoders:
					A__											=					lyr(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)[0]
				A__											=					self.layer_norm(SCREAMING_SNAKE_CASE__		)

				return self.dropout_post(SCREAMING_SNAKE_CASE__		), encoder_inputs_mask




