






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 							_lowerCAmelCase(			UpperCAmelCase_				,      UpperCAmelCase_				,      UpperCAmelCase_   ):





       """simple docstring"""







       @register_to_config
       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = False    ,     ):
         super().__init__()

         UpperCamelCase_:   Optional[int]        =							nn.Embedding(_lowerCamelCase    ,     _lowerCamelCase				)

         UpperCamelCase_:   Optional[int]        =							nn.Embedding(_lowerCamelCase    ,     _lowerCamelCase				)
         UpperCamelCase_:   Optional[Any]        =							False

         UpperCamelCase_:   Optional[int]        =							nn.Dropout(p=_lowerCamelCase				)

         UpperCamelCase_:   Dict        =							TaConfig(
             vocab_size=_lowerCamelCase    ,     d_model=_lowerCamelCase    ,     num_heads=_lowerCamelCase    ,     d_kv=_lowerCamelCase    ,     d_ff=_lowerCamelCase    ,     dropout_rate=_lowerCamelCase    ,     feed_forward_proj=_lowerCamelCase    ,     is_decoder=_lowerCamelCase    ,     is_encoder_decoder=_lowerCamelCase    ,     )

         UpperCamelCase_:   List[str]        =							nn.ModuleList()
         for lyr_num in range(_lowerCamelCase				):
           UpperCamelCase_:   Tuple        =							TaBlock(_lowerCamelCase				)
           self.encoders.append(_lowerCamelCase				)

         UpperCamelCase_:   Tuple        =							TaLayerNorm(_lowerCamelCase				)
         UpperCamelCase_:   Dict        =							nn.Dropout(p=_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   List[Any]        =							self.token_embedder(_lowerCamelCase				)

         UpperCamelCase_:   Tuple        =							encoder_input_tokens.shape[1]
         UpperCamelCase_:   Optional[Any]        =							torch.arange(_lowerCamelCase    ,     device=encoder_input_tokens.device				)
         x += self.position_encoding(_lowerCamelCase				)

         UpperCamelCase_:   Optional[Any]        =							self.dropout_pre(_lowerCamelCase				)

         # inverted the attention mask
         UpperCamelCase_:   Dict        =							encoder_input_tokens.size()
         UpperCamelCase_:   str        =							self.get_extended_attention_mask(_lowerCamelCase    ,     _lowerCamelCase				)

         for lyr in self.encoders:
           UpperCamelCase_:   Optional[Any]        =							lyr(_lowerCamelCase    ,     _lowerCamelCase				)[0]
         UpperCamelCase_:   Union[str, Any]        =							self.layer_norm(_lowerCamelCase				)

         return self.dropout_post(_lowerCamelCase				), encoder_inputs_mask







import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
			import optax
			from flax.training.common_utils import onehot

			from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
			from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						@slow
						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     List[str]:




											'''simple docstring'''



											__a					:						Optional[Any]      =					FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"   )
											__a					:						Dict      =					AutoTokenizer.from_pretrained("google/mt5-small"   )

											__a					:						Any      =					tokenizer("Hello there" ,					return_tensors="np"   ).input_ids
											__a					:						Union[str, Any]      =					tokenizer("Hi I am" ,					return_tensors="np"   ).input_ids

											__a					:						Union[str, Any]      =					shift_tokens_right(__UpperCAmelCase ,					model.config.pad_token_id ,					model.config.decoder_start_token_id   )

											__a					:						Tuple      =					model(__UpperCAmelCase ,					decoder_input_ids=__UpperCAmelCase   ).logits
											__a					:						str      =					optax.softmax_cross_entropy(__UpperCAmelCase ,					onehot(__UpperCAmelCase ,					logits.shape[-1]   )   ).mean()

											__a					:						List[str]      =					-(labels.shape[-1] * loss.item())

											__a					:						Optional[int]      =					-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )





