
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        lowercase      (	unittest.TestCase     ):



		@slow
		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				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(A_       ,   model.config.pad_token_id       ,   model.config.decoder_start_token_id )

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

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

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


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase      :				Tuple          = logging.get_logger(__name__)

_UpperCAmelCase      :				Union[str, Any]          = {
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       "data2vec-text"



		def __init__(				self       ,   A_=30_522       ,   A_=768       ,   A_=12       ,   A_=12       ,   A_=3_072       ,   A_="gelu"       ,   A_=0.1       ,   A_=0.1       ,   A_=512       ,   A_=2       ,   A_=0.02       ,   A_=1e-12       ,   A_=1       ,   A_=0       ,   A_=2       ,   A_="absolute"       ,   A_=True       ,   A_=None       ,   **A_       ,   )						->      Any:
				"""simple docstring"""
				super().__init__(pad_token_id=A_       ,   bos_token_id=A_       ,   eos_token_id=A_       ,   **A_ )

				UpperCamelCase						=  vocab_size
				UpperCamelCase						=  hidden_size
				UpperCamelCase						=  num_hidden_layers
				UpperCamelCase						=  num_attention_heads
				UpperCamelCase						=  hidden_act
				UpperCamelCase						=  intermediate_size
				UpperCamelCase						=  hidden_dropout_prob
				UpperCamelCase						=  attention_probs_dropout_prob
				UpperCamelCase						=  max_position_embeddings
				UpperCamelCase						=  type_vocab_size
				UpperCamelCase						=  initializer_range
				UpperCamelCase						=  layer_norm_eps
				UpperCamelCase						=  position_embedding_type
				UpperCamelCase						=  use_cache
				UpperCamelCase						=  classifier_dropout






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		@property
		def __UpperCamelCase				(				self )						->      Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task == "multiple-choice":
						UpperCamelCase						=  {0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
						UpperCamelCase						=  {0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				    ] )
