






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    snake_case    				=		"platform"

    import jax
    import jax.numpy as jnp

    from transformers import BlenderbotTokenizer
    from transformers.models.blenderbot.modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        shift_tokens_right,
    )


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase=None						,     lowercase=None						,     lowercase=None						,     lowercase=None						,     lowercase=None						,     lowercase=None						,     ):

  """simple docstring"""






  if attention_mask is None:
    SCREAMING_SNAKE_CASE       :      List[str]							=    np.where(input_ids != config.pad_token_id						,     1						,     0				)
  if decoder_attention_mask is None:
    SCREAMING_SNAKE_CASE       :      List[str]							=    np.where(decoder_input_ids != config.pad_token_id						,     1						,     0				)
  if head_mask is None:
    SCREAMING_SNAKE_CASE       :      int							=    np.ones((config.encoder_layers, config.encoder_attention_heads)				)
  if decoder_head_mask is None:
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    np.ones((config.decoder_layers, config.decoder_attention_heads)				)
  if cross_attn_head_mask is None:
    SCREAMING_SNAKE_CASE       :      List[str]							=    np.ones((config.decoder_layers, config.decoder_attention_heads)				)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }


class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	Any					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Tuple=13					, UpperCAmelCase_       :	Tuple=7					, UpperCAmelCase_       :	Any=True					, UpperCAmelCase_       :	Union[str, Any]=False					, UpperCAmelCase_       :	Optional[int]=99					, UpperCAmelCase_       :	Dict=16					, UpperCAmelCase_       :	Any=2					, UpperCAmelCase_       :	Dict=4					, UpperCAmelCase_       :	str=4					, UpperCAmelCase_       :	int="gelu"					, UpperCAmelCase_       :	Tuple=0.1					, UpperCAmelCase_       :	List[str]=0.1					, UpperCAmelCase_       :	List[Any]=32					, UpperCAmelCase_       :	Union[str, Any]=2					, UpperCAmelCase_       :	Dict=1					, UpperCAmelCase_       :	Optional[int]=0					, UpperCAmelCase_       :	Union[str, Any]=0.02					, ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    parent
       SCREAMING_SNAKE_CASE       :      int							=    batch_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    seq_length
       SCREAMING_SNAKE_CASE       :      Tuple							=    is_training
       SCREAMING_SNAKE_CASE       :      str							=    use_labels
       SCREAMING_SNAKE_CASE       :      List[Any]							=    vocab_size
       SCREAMING_SNAKE_CASE       :      int							=    hidden_size
       SCREAMING_SNAKE_CASE       :      Tuple							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Dict							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      List[str]							=    intermediate_size
       SCREAMING_SNAKE_CASE       :      int							=    hidden_act
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      List[Any]							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      List[Any]							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    eos_token_id
       SCREAMING_SNAKE_CASE       :      Dict							=    pad_token_id
       SCREAMING_SNAKE_CASE       :      Any							=    bos_token_id
       SCREAMING_SNAKE_CASE       :      Any							=    initializer_range


     def 				_A       (				self       :	int      ):
       SCREAMING_SNAKE_CASE       :      Any							=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					, self.vocab_size      )					, 3					, self.vocab_size      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					, dtype=np.intaa      ))					, -1      )

       SCREAMING_SNAKE_CASE       :      Any							=    shift_tokens_right(UpperCAmelCase__					, 1					, 2      )

       SCREAMING_SNAKE_CASE       :      Dict							=    BlenderbotConfig(
           vocab_size=self.vocab_size					, d_model=self.hidden_size					, encoder_layers=self.num_hidden_layers					, decoder_layers=self.num_hidden_layers					, encoder_attention_heads=self.num_attention_heads					, decoder_attention_heads=self.num_attention_heads					, encoder_ffn_dim=self.intermediate_size					, decoder_ffn_dim=self.intermediate_size					, dropout=self.hidden_dropout_prob					, attention_dropout=self.attention_probs_dropout_prob					, max_position_embeddings=self.max_position_embeddings					, eos_token_id=self.eos_token_id					, bos_token_id=self.bos_token_id					, pad_token_id=self.pad_token_id					, initializer_range=self.initializer_range					, use_cache=UpperCAmelCase__					, )
       SCREAMING_SNAKE_CASE       :      Tuple							=    prepare_blenderbot_inputs_dict(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__      )
       return config, inputs_dict


     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Dict							=    self.prepare_config_and_inputs()
       return config, inputs_dict


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    20
       SCREAMING_SNAKE_CASE       :      int							=    model_class_name(UpperCAmelCase__      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    model.encode(inputs_dict["input_ids"]      )

       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Tuple							=    (
           inputs_dict["decoder_input_ids"],
           inputs_dict["decoder_attention_mask"],
       )

       SCREAMING_SNAKE_CASE       :      str							=    model.init_cache(decoder_input_ids.shape[0]					, UpperCAmelCase__					, UpperCAmelCase__      )
       SCREAMING_SNAKE_CASE       :      Dict							=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					, dtype="i4"      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]					, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					, )
       SCREAMING_SNAKE_CASE       :      str							=    model.decode(
           decoder_input_ids[:, :-1]					, UpperCAmelCase__					, decoder_attention_mask=UpperCAmelCase__					, past_key_values=UpperCAmelCase__					, decoder_position_ids=UpperCAmelCase__					, )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					, dtype="i4"      )
       SCREAMING_SNAKE_CASE       :      Any							=    model.decode(
           decoder_input_ids[:, -1:]					, UpperCAmelCase__					, decoder_attention_mask=UpperCAmelCase__					, past_key_values=outputs_cache.past_key_values					, decoder_position_ids=UpperCAmelCase__					, )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    model.decode(UpperCAmelCase__					, UpperCAmelCase__      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
       self.parent.assertTrue(diff < 1E-3					, msg=f'''Max diff is {diff}'''      )






     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Any      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    20
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model_class_name(UpperCAmelCase__      )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model.encode(inputs_dict["input_ids"]      )

       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      List[Any]							=    (
           inputs_dict["decoder_input_ids"],
           inputs_dict["decoder_attention_mask"],
       )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])      ),
           ]					, axis=-1					, )

       SCREAMING_SNAKE_CASE       :      Tuple							=    model.init_cache(decoder_input_ids.shape[0]					, UpperCAmelCase__					, UpperCAmelCase__      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]					, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					, )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    model.decode(
           decoder_input_ids[:, :-1]					, UpperCAmelCase__					, decoder_attention_mask=UpperCAmelCase__					, past_key_values=UpperCAmelCase__					, decoder_position_ids=UpperCAmelCase__					, )
       SCREAMING_SNAKE_CASE       :      str							=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					, dtype="i4"      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    model.decode(
           decoder_input_ids[:, -1:]					, UpperCAmelCase__					, past_key_values=outputs_cache.past_key_values					, decoder_attention_mask=UpperCAmelCase__					, decoder_position_ids=UpperCAmelCase__					, )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model.decode(UpperCAmelCase__					, UpperCAmelCase__					, decoder_attention_mask=UpperCAmelCase__      )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
       self.parent.assertTrue(diff < 1E-3					, msg=f'''Max diff is {diff}'''      )





@require_flax
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		List[str]			=	9_9


     def 				_A       (				self       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    np.array(
           [
               [71, 82, 18, 33, 46, 91, 2],
               [68, 34, 26, 58, 30, 82, 2],
               [5, 97, 17, 39, 94, 40, 2],
               [76, 83, 94, 25, 70, 78, 2],
               [87, 59, 41, 35, 48, 66, 2],
               [55, 13, 16, 58, 5, 2, 1],  # note padding
               [64, 27, 31, 51, 12, 75, 2],
               [52, 64, 86, 17, 83, 39, 2],
               [48, 61, 9, 24, 71, 82, 2],
               [26, 1, 60, 48, 22, 13, 2],
               [21, 5, 62, 28, 14, 76, 2],
               [45, 98, 37, 86, 59, 48, 2],
               [70, 70, 50, 9, 28, 0, 2],
           ]					, dtype=np.intaa					, )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    input_ids.shape[0]
       SCREAMING_SNAKE_CASE       :      List[str]							=    BlenderbotConfig(
           vocab_size=self.vocab_size					, d_model=24					, encoder_layers=2					, decoder_layers=2					, encoder_attention_heads=2					, decoder_attention_heads=2					, encoder_ffn_dim=32					, decoder_ffn_dim=32					, max_position_embeddings=48					, eos_token_id=2					, pad_token_id=1					, bos_token_id=0					, )
       return config, input_ids, batch_size


     def 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      List[str]							=    self._get_config_and_data()
       SCREAMING_SNAKE_CASE       :      List[str]							=    FlaxBlenderbotForConditionalGeneration(UpperCAmelCase__      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    lm_model(input_ids=UpperCAmelCase__      )
       SCREAMING_SNAKE_CASE       :      int							=    (batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs["logits"].shape					, UpperCAmelCase__      )


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      str							=    BlenderbotConfig(
           vocab_size=self.vocab_size					, d_model=14					, encoder_layers=2					, decoder_layers=2					, encoder_attention_heads=2					, decoder_attention_heads=2					, encoder_ffn_dim=8					, decoder_ffn_dim=8					, max_position_embeddings=48					, )
       SCREAMING_SNAKE_CASE       :      int							=    FlaxBlenderbotForConditionalGeneration(UpperCAmelCase__      )
       SCREAMING_SNAKE_CASE       :      Dict							=    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					, dtype=np.intaa      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					, dtype=np.intaa      )
       SCREAMING_SNAKE_CASE       :      str							=    lm_model(input_ids=UpperCAmelCase__					, decoder_input_ids=UpperCAmelCase__      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    (*summary.shape, config.vocab_size)
       self.assertEqual(outputs["logits"].shape					, UpperCAmelCase__      )






     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					, dtype=np.intaa      )
       SCREAMING_SNAKE_CASE       :      str							=    shift_tokens_right(UpperCAmelCase__					, 1					, 2      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    np.equal(UpperCAmelCase__					, 1      ).astype(np.floataa      ).sum()
       SCREAMING_SNAKE_CASE       :      Dict							=    np.equal(UpperCAmelCase__					, 1      ).astype(np.floataa      ).sum()
       self.assertEqual(shifted.shape					, input_ids.shape      )
       self.assertEqual(UpperCAmelCase__					, n_pad_before - 1      )
       self.assertTrue(np.equal(shifted[:, 0]					, 2      ).all()      )





@require_flax
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		unittest.TestCase      ,		lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[int]			=	True
     UpperCamelCase_							:		Any			=	(
         (
             FlaxBlenderbotModel,
             FlaxBlenderbotForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     UpperCamelCase_							:		Optional[Any]			=	(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    FlaxBlenderbotModelTester(self      )


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      str							=    self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__      )


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      int							=    self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward_with_attn_mask(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__      )


     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__      ):
           SCREAMING_SNAKE_CASE       :      Any							=    self._prepare_for_class(UpperCAmelCase__					, UpperCAmelCase__      )
           SCREAMING_SNAKE_CASE       :      int							=    model_class(UpperCAmelCase__      )

           @jax.jit
           def encode_jitted(UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Optional[Any]=None					, **UpperCAmelCase_       :	Union[str, Any]      ):
             return model.encode(input_ids=UpperCAmelCase__					, attention_mask=UpperCAmelCase__      )

           with self.subTest("JIT Enabled"      ):
             SCREAMING_SNAKE_CASE       :      Any							=    encode_jitted(**UpperCAmelCase__      ).to_tuple()

           with self.subTest("JIT Disabled"      ):
             with jax.disable_jit():
               SCREAMING_SNAKE_CASE       :      Tuple							=    encode_jitted(**UpperCAmelCase__      ).to_tuple()

           self.assertEqual(len(UpperCAmelCase__      )					, len(UpperCAmelCase__      )      )
           for jitted_output, output in zip(UpperCAmelCase__					, UpperCAmelCase__      ):
             self.assertEqual(jitted_output.shape					, output.shape      )


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      str							=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__      ):
           SCREAMING_SNAKE_CASE       :      List[str]							=    model_class(UpperCAmelCase__      )
           SCREAMING_SNAKE_CASE       :      List[Any]							=    model.encode(inputs_dict["input_ids"]					, inputs_dict["attention_mask"]      )

           SCREAMING_SNAKE_CASE       :      Tuple							=    {
               "decoder_input_ids": inputs_dict["decoder_input_ids"],
               "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
               "encoder_outputs": encoder_outputs,
           }

           @jax.jit
           def decode_jitted(UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	int      ):
             return model.decode(
                 decoder_input_ids=UpperCAmelCase__					, decoder_attention_mask=UpperCAmelCase__					, encoder_outputs=UpperCAmelCase__					, )

           with self.subTest("JIT Enabled"      ):
             SCREAMING_SNAKE_CASE       :      Optional[int]							=    decode_jitted(**UpperCAmelCase__      ).to_tuple()

           with self.subTest("JIT Disabled"      ):
             with jax.disable_jit():
               SCREAMING_SNAKE_CASE       :      Tuple							=    decode_jitted(**UpperCAmelCase__      ).to_tuple()

           self.assertEqual(len(UpperCAmelCase__      )					, len(UpperCAmelCase__      )      )
           for jitted_output, output in zip(UpperCAmelCase__					, UpperCAmelCase__      ):
             self.assertEqual(jitted_output.shape					, output.shape      )


     @slow
     def 				_A       (				self       :	int      ):
       for model_class_name in self.all_model_classes:
         SCREAMING_SNAKE_CASE       :      Any							=    model_class_name.from_pretrained("facebook/blenderbot-400M-distill"      )
         # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
         SCREAMING_SNAKE_CASE       :      List[Any]							=    np.ones((1, 1)      ) * model.config.eos_token_id
         SCREAMING_SNAKE_CASE       :      Any							=    model(UpperCAmelCase__      )
         self.assertIsNotNone(UpperCAmelCase__      )






     @unittest.skipUnless(jax_device != "cpu"					, "3B test too slow on CPU."      )
     @slow
     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
       SCREAMING_SNAKE_CASE       :      Dict							=    {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

       SCREAMING_SNAKE_CASE       :      List[str]							=    FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"					, from_pt=UpperCAmelCase__      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"      )

       SCREAMING_SNAKE_CASE       :      Dict							=    ["Sam"]
       SCREAMING_SNAKE_CASE       :      List[Any]							=    tokenizer(UpperCAmelCase__					, return_tensors="jax"      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model.generate(**UpperCAmelCase__					, **UpperCAmelCase__      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    "Sam is a great name. It means \"sun\" in Gaelic."

       SCREAMING_SNAKE_CASE       :      List[str]							=    tokenizer.batch_decode(UpperCAmelCase__					, **UpperCAmelCase__      )
       assert generated_txt[0].strip() == tgt_text














# 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


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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """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

   snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






