



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 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"     )
							UpperCAmelCase_						=				AutoTokenizer.from_pretrained("google/mt5-small"     )

							UpperCAmelCase_						=				tokenizer("Hello there"    ,	return_tensors="np"     ).input_ids
							UpperCAmelCase_						=				tokenizer("Hi I am"    ,	return_tensors="np"     ).input_ids

							UpperCAmelCase_						=				shift_tokens_right(__lowerCAmelCase    ,	model.config.pad_token_id    ,	model.config.decoder_start_token_id     )

							UpperCAmelCase_						=				model(__lowerCAmelCase    ,	decoder_input_ids=__lowerCAmelCase     ).logits
							UpperCAmelCase_						=				optax.softmax_cross_entropy(__lowerCAmelCase    ,	onehot(__lowerCAmelCase    ,	logits.shape[-1]     )     ).mean()

							UpperCAmelCase_						=				-(labels.shape[-1] * loss.item())

							UpperCAmelCase_						=				-84.9127
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1e-4     )






from typing import Optional, Union

import torch
from torch import nn

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



class 	lowerCamelCase      (						lowercase__,     lowercase__  ):







					'''simple docstring'''







					@register_to_config
					def __init__(			self    ,	lowerCAmelCase = 768    ,	):
							super().__init__()

							UpperCAmelCase_						=				nn.Parameter(torch.zeros(1    ,	lowerCAmelCase     )     )
							UpperCAmelCase_						=				nn.Parameter(torch.ones(1    ,	lowerCAmelCase     )     )



					def    A__		(			self    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	):
							UpperCAmelCase_						=				nn.Parameter(self.mean.to(lowerCAmelCase     ).to(lowerCAmelCase     )     )
							UpperCAmelCase_						=				nn.Parameter(self.std.to(lowerCAmelCase     ).to(lowerCAmelCase     )     )
							return self



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				(embeds - self.mean) * 1.0 / self.std
							return embeds



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				(embeds * self.std) + self.mean
							return embeds

