

"""simple docstring"""







from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__SCREAMING_SNAKE_CASE								="tiny-wmt19-en-ru"

# Build

# borrowed from a test
__SCREAMING_SNAKE_CASE								=[
    "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>",
]
__SCREAMING_SNAKE_CASE								=dict(zip(vocab, range(len(vocab))))
__SCREAMING_SNAKE_CASE								=["l o 123", "lo w 1456", "e r</w> 1789", ""]

with tempfile.TemporaryDirectory() as tmpdirname:
       __SCREAMING_SNAKE_CASE								=Path(tmpdirname)
       __SCREAMING_SNAKE_CASE								=build_dir / VOCAB_FILES_NAMES["src_vocab_file"]
       __SCREAMING_SNAKE_CASE								=build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"]
       __SCREAMING_SNAKE_CASE								=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))

       __SCREAMING_SNAKE_CASE								=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,
       )

__SCREAMING_SNAKE_CASE								=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,
)

__SCREAMING_SNAKE_CASE								=FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
__SCREAMING_SNAKE_CASE								=tokenizer(["Making tiny model"], return_tensors="pt")
__SCREAMING_SNAKE_CASE								=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


"""simple docstring"""







import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								={
    "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						'wav2vec2'
   def __init__(    self  ,__UpperCamelCase=32  ,__UpperCamelCase=768  ,__UpperCamelCase=12  ,__UpperCamelCase=12  ,__UpperCamelCase=3072  ,__UpperCamelCase="gelu"  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.02  ,__UpperCamelCase=1e-5  ,__UpperCamelCase="group"  ,__UpperCamelCase="gelu"  ,__UpperCamelCase=(512, 512, 512, 512, 512, 512, 512)  ,__UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)  ,__UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)  ,__UpperCamelCase=False  ,__UpperCamelCase=128  ,__UpperCamelCase=16  ,__UpperCamelCase=False  ,__UpperCamelCase=True  ,__UpperCamelCase=0.05  ,__UpperCamelCase=10  ,__UpperCamelCase=2  ,__UpperCamelCase=0.0  ,__UpperCamelCase=10  ,__UpperCamelCase=0  ,__UpperCamelCase=320  ,__UpperCamelCase=2  ,__UpperCamelCase=0.1  ,__UpperCamelCase=100  ,__UpperCamelCase=256  ,__UpperCamelCase=256  ,__UpperCamelCase=0.1  ,__UpperCamelCase="sum"  ,__UpperCamelCase=False  ,__UpperCamelCase=False  ,__UpperCamelCase=256  ,__UpperCamelCase=(512, 512, 512, 512, 1500)  ,__UpperCamelCase=(5, 3, 3, 1, 1)  ,__UpperCamelCase=(1, 2, 3, 1, 1)  ,__UpperCamelCase=512  ,__UpperCamelCase=0  ,__UpperCamelCase=1  ,__UpperCamelCase=2  ,__UpperCamelCase=False  ,__UpperCamelCase=3  ,__UpperCamelCase=2  ,__UpperCamelCase=3  ,__UpperCamelCase=None  ,__UpperCamelCase=None  ,**__UpperCamelCase  ,)			->   Optional[Any]:


        '''simple docstring'''


        super().__init__(**__UpperCamelCase  ,pad_token_id=__UpperCamelCase  ,bos_token_id=__UpperCamelCase  ,eos_token_id=__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			hidden_size
        lowercase_			:       Tuple									=			feat_extract_norm
        lowercase_			:       Dict									=			feat_extract_activation
        lowercase_			:       List[str]									=			list(__UpperCamelCase    )
        lowercase_			:       str									=			list(__UpperCamelCase    )
        lowercase_			:       Dict									=			list(__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			conv_bias
        lowercase_			:       Dict									=			num_conv_pos_embeddings
        lowercase_			:       List[str]									=			num_conv_pos_embedding_groups
        lowercase_			:       Optional[Any]									=			len(self.conv_dim    )
        lowercase_			:       Any									=			num_hidden_layers
        lowercase_			:       List[Any]									=			intermediate_size
        lowercase_			:       List[Any]									=			hidden_act
        lowercase_			:       Optional[int]									=			num_attention_heads
        lowercase_			:       int									=			hidden_dropout
        lowercase_			:       Dict									=			attention_dropout
        lowercase_			:       Union[str, Any]									=			activation_dropout
        lowercase_			:       Tuple									=			feat_proj_dropout
        lowercase_			:       List[str]									=			final_dropout
        lowercase_			:       Union[str, Any]									=			layerdrop
        lowercase_			:       List[str]									=			layer_norm_eps
        lowercase_			:       Optional[int]									=			initializer_range
        lowercase_			:       List[Any]									=			vocab_size
        lowercase_			:       Optional[int]									=			do_stable_layer_norm
        lowercase_			:       Union[str, Any]									=			use_weighted_layer_sum

        if (
            (len(self.conv_stride    ) != self.num_feat_extract_layers)
            or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
            or (len(self.conv_dim    ) != self.num_feat_extract_layers)
        ):
             raise ValueError(
                 'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
                 ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
                 f''' {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,'''
                 f''' `len(config.conv_kernel) = {len(self.conv_kernel    )}`.'''    )

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        lowercase_			:       Dict									=			apply_spec_augment
        lowercase_			:       Optional[int]									=			mask_time_prob
        lowercase_			:       Union[str, Any]									=			mask_time_length
        lowercase_			:       List[str]									=			mask_time_min_masks
        lowercase_			:       List[str]									=			mask_feature_prob
        lowercase_			:       Any									=			mask_feature_length
        lowercase_			:       List[Any]									=			mask_feature_min_masks

        # parameters for pretraining with codevector quantized representations
        lowercase_			:       List[Any]									=			num_codevectors_per_group
        lowercase_			:       Optional[int]									=			num_codevector_groups
        lowercase_			:       Dict									=			contrastive_logits_temperature
        lowercase_			:       int									=			feat_quantizer_dropout
        lowercase_			:       Optional[int]									=			num_negatives
        lowercase_			:       str									=			codevector_dim
        lowercase_			:       str									=			proj_codevector_dim
        lowercase_			:       Optional[Any]									=			diversity_loss_weight

        # ctc loss
        lowercase_			:       Tuple									=			ctc_loss_reduction
        lowercase_			:       int									=			ctc_zero_infinity

        # adapter
        lowercase_			:       int									=			add_adapter
        lowercase_			:       Dict									=			adapter_kernel_size
        lowercase_			:       List[str]									=			adapter_stride
        lowercase_			:       Dict									=			num_adapter_layers
        lowercase_			:       Dict									=			output_hidden_size or hidden_size
        lowercase_			:       Optional[Any]									=			adapter_attn_dim

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        lowercase_			:       Dict									=			classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        lowercase_			:       Any									=			list(__UpperCamelCase    )
        lowercase_			:       str									=			list(__UpperCamelCase    )
        lowercase_			:       Any									=			list(__UpperCamelCase    )
        lowercase_			:       Tuple									=			xvector_output_dim







   @property
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        return functools.reduce(operator.mul  ,self.conv_stride  ,1    )

