
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 )						->      Tuple:
				"""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 )


import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset


@pytest.mark.parametrize('dataset_size'					, [None, 400 * 2**20, 600 * 2**20]				)
@pytest.mark.parametrize('input_in_memory_max_size'					, ['default', 0, 100 * 2**20, 900 * 2**20]				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		if input_in_memory_max_size != "default":
				monkeypatch.setattr(datasets.config					, 'IN_MEMORY_MAX_SIZE'					, lowercase				)
		UpperCamelCase						=  datasets.config.IN_MEMORY_MAX_SIZE
		if input_in_memory_max_size == "default":
				assert in_memory_max_size == 0
		else:
				assert in_memory_max_size == input_in_memory_max_size
		if dataset_size and in_memory_max_size:
				UpperCamelCase						=  dataset_size < in_memory_max_size
		else:
				UpperCamelCase						=  False
		UpperCamelCase						=  is_small_dataset(lowercase				)
		assert result == expected
