


'''simple docstring'''

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       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             lowercase__      			= FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''' )
             lowercase__      			= AutoTokenizer.from_pretrained('''google/mt5-small''' )

             lowercase__      			= tokenizer('''Hello there'''   ,						return_tensors='''np''' ).input_ids
             lowercase__      			= tokenizer('''Hi I am'''   ,						return_tensors='''np''' ).input_ids

             lowercase__      			= shift_tokens_right(UpperCamelCase   ,						model.config.pad_token_id   ,						model.config.decoder_start_token_id )

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

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

             lowercase__      			= -84.91_27
             self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )





# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a  :     Any       =  {
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     List[Any]       =  [
		    """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """CpmAntForCausalLM""",
		    """CpmAntModel""",
		    """CpmAntPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
		from .tokenization_cpmant import CpmAntTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_cpmant import (
				    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CpmAntForCausalLM,
				    CpmAntModel,
				    CpmAntPreTrainedModel,
				)


else:
		import sys

		__a  :     Any       =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)