






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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
       a		:	List[str]      					=			"platform"

       import jax
       import jax.numpy as jnp

       from transformers import AutoTokenizer
       from transformers.models.mbart.modeling_flax_mbart import (
           FlaxMBartForConditionalGeneration,
           FlaxMBartForQuestionAnswering,
           FlaxMBartForSequenceClassification,
           FlaxMBartModel,
           shift_tokens_right,
       )






def     lowerCamelCase__				(   __lowerCamelCase      :					Dict  ,		__lowerCamelCase      :					str  ,		__lowerCamelCase      :					int=None  ,		__lowerCamelCase      :					List[str]=None  ,		__lowerCamelCase      :					Union[str, Any]=None  ,		__lowerCamelCase      :					Union[str, Any]=None  ,		__lowerCamelCase      :					Any=None  ,		__lowerCamelCase      :					Optional[Any]=None  ,		):
  if attention_mask is None:
    __UpperCAmelCase							:     str									=  np.where(input_ids != config.pad_token_id  ,		1  ,		0		)
  if decoder_attention_mask is None:
    __UpperCAmelCase							:     int									=  np.where(decoder_input_ids != config.pad_token_id  ,		1  ,		0		)
  if head_mask is None:
    __UpperCAmelCase							:     int									=  np.ones((config.encoder_layers, config.encoder_attention_heads)		)
  if decoder_head_mask is None:
    __UpperCAmelCase							:     List[Any]									=  np.ones((config.decoder_layers, config.decoder_attention_heads)		)
  if cross_attn_head_mask is None:
    __UpperCAmelCase							:     Dict									=  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": decoder_attention_mask,
  }
class    a   (			unittest.TestCase       ):



      """simple docstring"""


      def __init__(				self :      Dict		,				__lowercase :      Optional[int]		,				__lowercase :      Optional[int]=13		,				__lowercase :      Optional[Any]=7		,				__lowercase :      Union[str, Any]=True		,				__lowercase :      List[Any]=False		,				__lowercase :      Dict=99		,				__lowercase :      Union[str, Any]=16		,				__lowercase :      Any=2		,				__lowercase :      List[str]=4		,				__lowercase :      int=4		,				__lowercase :      str="gelu"		,				__lowercase :      Optional[int]=0.1		,				__lowercase :      List[Any]=0.1		,				__lowercase :      str=32		,				__lowercase :      Optional[int]=2		,				__lowercase :      Tuple=1		,				__lowercase :      Dict=0		,				__lowercase :      List[str]=2		,				__lowercase :      Optional[int]=0.02		,				)							->				List[str]:
        __UpperCAmelCase							:     Optional[Any]									=  parent
        __UpperCAmelCase							:     str									=  batch_size
        __UpperCAmelCase							:     List[Any]									=  seq_length
        __UpperCAmelCase							:     int									=  is_training
        __UpperCAmelCase							:     Tuple									=  use_labels
        __UpperCAmelCase							:     str									=  vocab_size
        __UpperCAmelCase							:     Union[str, Any]									=  hidden_size
        __UpperCAmelCase							:     Union[str, Any]									=  num_hidden_layers
        __UpperCAmelCase							:     int									=  num_attention_heads
        __UpperCAmelCase							:     Dict									=  intermediate_size
        __UpperCAmelCase							:     List[str]									=  hidden_act
        __UpperCAmelCase							:     str									=  hidden_dropout_prob
        __UpperCAmelCase							:     int									=  attention_probs_dropout_prob
        __UpperCAmelCase							:     Optional[int]									=  max_position_embeddings
        __UpperCAmelCase							:     int									=  eos_token_id
        __UpperCAmelCase							:     Optional[Any]									=  pad_token_id
        __UpperCAmelCase							:     List[Any]									=  bos_token_id
        __UpperCAmelCase							:     List[Any]									=  decoder_start_token_id
        __UpperCAmelCase							:     Union[str, Any]									=  initializer_range
      def      UpperCAmelCase				(				self :      Optional[int] )							->				Union[str, Any]:
        __UpperCAmelCase							:     int									=  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,				self.vocab_size )		,				3		,				self.vocab_size )
        __UpperCAmelCase							:     Optional[int]									=  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,				dtype=np.intaa ))		,				-1 )

        __UpperCAmelCase							:     Tuple									=  shift_tokens_right(__lowercase		,				1 )

        __UpperCAmelCase							:     Optional[Any]									=  MBartConfig(
            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		,				decoder_start_token_id=self.decoder_start_token_id		,				initializer_range=self.initializer_range		,				use_cache=__lowercase		,				)
        __UpperCAmelCase							:     Optional[Any]									=  prepare_mbart_inputs_dict(__lowercase		,				__lowercase		,				__lowercase )
        return config, inputs_dict
      def      UpperCAmelCase				(				self :      List[Any] )							->				List[str]:
        __UpperCAmelCase							:     List[Any]									=  self.prepare_config_and_inputs()
        return config, inputs_dict
      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      Tuple		,				__lowercase :      Optional[int]		,				__lowercase :      Optional[int] )							->				Optional[int]:
        __UpperCAmelCase							:     int									=  20
        __UpperCAmelCase							:     List[Any]									=  model_class_name(__lowercase )

        __UpperCAmelCase							:     Dict									=  model.encode(inputs_dict["""input_ids"""] )

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

        __UpperCAmelCase							:     Optional[Any]									=  model.init_cache(decoder_input_ids.shape[0]		,				__lowercase		,				__lowercase )
        __UpperCAmelCase							:     Dict									=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,				dtype="""i4""" )

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

        __UpperCAmelCase							:     Optional[int]									=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,				dtype="""i4""" )
        __UpperCAmelCase							:     Dict									=  model.decode(
            decoder_input_ids[:, -1:]		,				__lowercase		,				decoder_attention_mask=__lowercase		,				past_key_values=outputs_cache.past_key_values		,				decoder_position_ids=__lowercase		,				)

        __UpperCAmelCase							:     List[Any]									=  model.decode(__lowercase		,				__lowercase )

        __UpperCAmelCase							:     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}""" )




      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      Any		,				__lowercase :      str		,				__lowercase :      List[Any] )							->				Optional[int]:
        __UpperCAmelCase							:     int									=  20
        __UpperCAmelCase							:     List[Any]									=  model_class_name(__lowercase )

        __UpperCAmelCase							:     Tuple									=  model.encode(inputs_dict["""input_ids"""] )

        __UpperCAmelCase							:     List[str]									=  (
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        __UpperCAmelCase							:     Tuple									=  jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
            ]		,				axis=-1		,				)

        __UpperCAmelCase							:     Optional[int]									=  model.init_cache(decoder_input_ids.shape[0]		,				__lowercase		,				__lowercase )
        __UpperCAmelCase							:     List[str]									=  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]		,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,				)

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

        __UpperCAmelCase							:     int									=  model.decode(__lowercase		,				__lowercase		,				decoder_attention_mask=__lowercase )

        __UpperCAmelCase							:     Dict									=  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    a   (			unittest.TestCase       ):



      """simple docstring"""


      a  :			Tuple								=     99
      def      UpperCAmelCase				(				self :      Any )							->				Optional[int]:
        __UpperCAmelCase							:     List[str]									=  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		,				)

        __UpperCAmelCase							:     Optional[Any]									=  input_ids.shape[0]
        __UpperCAmelCase							:     str									=  MBartConfig(
            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      UpperCAmelCase				(				self :      Union[str, Any] )							->				Optional[int]:
        __UpperCAmelCase							:     List[Any]									=  self._get_config_and_data()
        __UpperCAmelCase							:     Tuple									=  FlaxMBartForSequenceClassification(__lowercase )
        __UpperCAmelCase							:     Optional[Any]									=  model(input_ids=__lowercase		,				decoder_input_ids=__lowercase )
        __UpperCAmelCase							:     List[str]									=  (batch_size, config.num_labels)
        self.assertEqual(outputs["""logits"""].shape		,				__lowercase )
      def      UpperCAmelCase				(				self :      Optional[int] )							->				List[Any]:
        __UpperCAmelCase							:     Optional[int]									=  self._get_config_and_data()
        __UpperCAmelCase							:     Optional[Any]									=  FlaxMBartForQuestionAnswering(__lowercase )
        __UpperCAmelCase							:     Any									=  model(input_ids=__lowercase )

        self.assertEqual(outputs["""start_logits"""].shape		,				input_ids.shape )
        self.assertEqual(outputs["""end_logits"""].shape		,				input_ids.shape )
      def      UpperCAmelCase				(				self :      Any )							->				Tuple:
        __UpperCAmelCase							:     Any									=  self._get_config_and_data()
        __UpperCAmelCase							:     Optional[Any]									=  FlaxMBartForConditionalGeneration(__lowercase )
        __UpperCAmelCase							:     int									=  lm_model(input_ids=__lowercase )
        __UpperCAmelCase							:     List[Any]									=  (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["""logits"""].shape		,				__lowercase )
      def      UpperCAmelCase				(				self :      List[Any] )							->				Optional[Any]:
        __UpperCAmelCase							:     str									=  MBartConfig(
            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		,				)
        __UpperCAmelCase							:     Optional[Any]									=  FlaxMBartForConditionalGeneration(__lowercase )
        __UpperCAmelCase							:     str									=  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,				dtype=np.intaa )
        __UpperCAmelCase							:     Optional[int]									=  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,				dtype=np.intaa )
        __UpperCAmelCase							:     Optional[int]									=  lm_model(input_ids=__lowercase		,				decoder_input_ids=__lowercase )
        __UpperCAmelCase							:     Optional[Any]									=  (*summary.shape, config.vocab_size)
        self.assertEqual(outputs["""logits"""].shape		,				__lowercase )




      def      UpperCAmelCase				(				self :      Any )							->				str:
        __UpperCAmelCase							:     List[Any]									=  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,				dtype=np.intaa )
        __UpperCAmelCase							:     str									=  shift_tokens_right(__lowercase		,				1 )
        __UpperCAmelCase							:     Optional[int]									=  np.equal(__lowercase		,				1 ).astype(np.floataa ).sum()
        __UpperCAmelCase							:     Tuple									=  np.equal(__lowercase		,				1 ).astype(np.floataa ).sum()
        self.assertEqual(shifted.shape		,				input_ids.shape )
        self.assertEqual(__lowercase		,				n_pad_before - 1 )
        self.assertTrue(np.equal(shifted[:, 0]		,				2 ).all() )


@require_flax
class    a   (			lowercase__							,	unittest.TestCase							,	lowercase__       ):



      """simple docstring"""


      a  :			Tuple								=     True
      a  :			Optional[int]								=     (
          (
              FlaxMBartModel,
              FlaxMBartForConditionalGeneration,
              FlaxMBartForSequenceClassification,
              FlaxMBartForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      a  :			str								=     (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
      def      UpperCAmelCase				(				self :      List[Any] )							->				int:
        __UpperCAmelCase							:     Any									=  FlaxMBartModelTester(self )
      def      UpperCAmelCase				(				self :      Dict )							->				Any:
        __UpperCAmelCase							:     List[str]									=  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(__lowercase		,				__lowercase		,				__lowercase )
      def      UpperCAmelCase				(				self :      int )							->				Union[str, Any]:
        __UpperCAmelCase							:     List[Any]									=  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(__lowercase		,				__lowercase		,				__lowercase )
      def      UpperCAmelCase				(				self :      List[str] )							->				Tuple:
        __UpperCAmelCase							:     Any									=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
            __UpperCAmelCase							:     Dict									=  self._prepare_for_class(__lowercase		,				__lowercase )
            __UpperCAmelCase							:     List[str]									=  model_class(__lowercase )

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

            with self.subTest("""JIT Enabled""" ):
              __UpperCAmelCase							:     Tuple									=  encode_jitted(**__lowercase ).to_tuple()

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

            self.assertEqual(len(__lowercase )		,				len(__lowercase ) )
            for jitted_output, output in zip(__lowercase		,				__lowercase ):
              self.assertEqual(jitted_output.shape		,				output.shape )
      def      UpperCAmelCase				(				self :      str )							->				Optional[Any]:
        __UpperCAmelCase							:     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__ ):
            __UpperCAmelCase							:     List[str]									=  model_class(__lowercase )
            __UpperCAmelCase							:     Optional[int]									=  model.encode(inputs_dict["""input_ids"""]		,				inputs_dict["""attention_mask"""] )

            __UpperCAmelCase							:     str									=  {
                """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(__lowercase :      int		,				__lowercase :      Dict		,				__lowercase :      int ):
              return model.decode(
                  decoder_input_ids=__lowercase		,				decoder_attention_mask=__lowercase		,				encoder_outputs=__lowercase		,				)

            with self.subTest("""JIT Enabled""" ):
              __UpperCAmelCase							:     str									=  decode_jitted(**__lowercase ).to_tuple()

            with self.subTest("""JIT Disabled""" ):
              with jax.disable_jit():
                __UpperCAmelCase							:     str									=  decode_jitted(**__lowercase ).to_tuple()

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




      @slow
      def      UpperCAmelCase				(				self :      Any )							->				List[str]:
        for model_class_name in self.all_model_classes:
          __UpperCAmelCase							:     Optional[int]									=  model_class_name.from_pretrained("""facebook/mbart-large-cc25"""		,				from_pt=__lowercase )
          # FlaxMBartForSequenceClassification expects eos token in input_ids
          __UpperCAmelCase							:     List[Any]									=  np.ones((1, 1) ) * model.config.eos_token_id
          __UpperCAmelCase							:     Optional[Any]									=  model(__lowercase )
          self.assertIsNotNone(__lowercase )


@require_flax
@require_sentencepiece
@require_tokenizers
class    a   (			unittest.TestCase       ):



      """simple docstring"""


      a  :			Union[str, Any]								=     [
          ' UN Chief Says There Is No Military Solution in Syria',
      ]
      a  :			Tuple								=     [
          'Şeful ONU declară că nu există o soluţie militară în Siria',
      ]
      a  :			List[Any]								=     'facebook/mbart-large-en-ro'
      @cached_property
      def      UpperCAmelCase				(				self :      int )							->				Dict:
        return AutoTokenizer.from_pretrained(self.model_name )
      @cached_property
      def      UpperCAmelCase				(				self :      Tuple )							->				List[str]:
        __UpperCAmelCase							:     Optional[Any]									=  FlaxMBartForConditionalGeneration.from_pretrained(self.model_name		,				from_pt=__lowercase )
        return model
      def      UpperCAmelCase				(				self :      Optional[int]		,				**__lowercase :      List[Any] )							->				str:
        __UpperCAmelCase							:     Any									=  self.translate_src_text(**__lowercase )
        self.assertListEqual(self.expected_text		,				__lowercase )
      def      UpperCAmelCase				(				self :      int		,				**__lowercase :      Optional[Any] )							->				Any:
        __UpperCAmelCase							:     Any									=  self.tokenizer(self.src_text		,				**__lowercase		,				return_tensors="""np""" )
        __UpperCAmelCase							:     Union[str, Any]									=  self.model.generate(
            model_inputs.input_ids		,				attention_mask=model_inputs.attention_mask		,				decoder_start_token_id=self.tokenizer.lang_code_to_id["""ro_RO"""]		,				early_stopping=__lowercase		,				num_beams=2		,				).sequences
        __UpperCAmelCase							:     int									=  self.tokenizer.batch_decode(__lowercase		,				skip_special_tokens=__lowercase )
        return generated_words




      @slow
      def      UpperCAmelCase				(				self :      Tuple )							->				List[Any]:
        self._assert_generated_batch_equal_expected()










import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           CpmAntConfig,
           CpmAntForCausalLM,
           CpmAntModel,
           CpmAntTokenizer,
       )
@require_torch
class    a   :



      """simple docstring"""


      def __init__(				self :      List[str]		,				__lowercase :      List[str]		,				__lowercase :      Tuple=2		,				__lowercase :      Dict=8		,				__lowercase :      int=True		,				__lowercase :      Dict=False		,				__lowercase :      List[Any]=False		,				__lowercase :      List[str]=False		,				__lowercase :      Any=False		,				__lowercase :      List[Any]=99		,				__lowercase :      int=32		,				__lowercase :      Optional[Any]=3		,				__lowercase :      Any=4		,				__lowercase :      List[Any]=37		,				__lowercase :      Dict=32		,				__lowercase :      Any=128		,				__lowercase :      Optional[Any]=8		,				__lowercase :      str=8		,				__lowercase :      str=8		,				__lowercase :      Optional[int]=1.0		,				__lowercase :      int=True		,				)							->				Optional[int]:
        __UpperCAmelCase							:     Optional[Any]									=  parent
        __UpperCAmelCase							:     Optional[int]									=  batch_size
        __UpperCAmelCase							:     List[Any]									=  seq_length
        __UpperCAmelCase							:     Optional[int]									=  is_training
        __UpperCAmelCase							:     Optional[Any]									=  use_token_type_ids
        __UpperCAmelCase							:     Optional[int]									=  use_input_mask
        __UpperCAmelCase							:     Optional[int]									=  use_labels
        __UpperCAmelCase							:     str									=  use_mc_token_ids
        __UpperCAmelCase							:     Any									=  vocab_size
        __UpperCAmelCase							:     str									=  hidden_size
        __UpperCAmelCase							:     str									=  num_hidden_layers
        __UpperCAmelCase							:     Union[str, Any]									=  num_attention_heads
        __UpperCAmelCase							:     Optional[int]									=  intermediate_size
        __UpperCAmelCase							:     Tuple									=  num_buckets
        __UpperCAmelCase							:     Any									=  max_distance
        __UpperCAmelCase							:     Any									=  prompt_length
        __UpperCAmelCase							:     int									=  prompt_types
        __UpperCAmelCase							:     List[Any]									=  segment_types
        __UpperCAmelCase							:     Any									=  init_std
        __UpperCAmelCase							:     int									=  return_dict
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				List[Any]:
        __UpperCAmelCase							:     Optional[Any]									=  {}
        __UpperCAmelCase							:     Dict									=  ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size ).type(torch.intaa )
        __UpperCAmelCase							:     List[str]									=  False

        __UpperCAmelCase							:     List[str]									=  self.get_config()

        return (config, input_ids)
      def      UpperCAmelCase				(				self :      Union[str, Any] )							->				Optional[int]:
        return CpmAntConfig(
            vocab_size=self.vocab_size		,				hidden_size=self.hidden_size		,				num_hidden_layers=self.num_hidden_layers		,				num_attention_heads=self.num_attention_heads		,				dim_ff=self.intermediate_size		,				position_bias_num_buckets=self.num_buckets		,				position_bias_max_distance=self.max_distance		,				prompt_types=self.prompt_types		,				prompt_length=self.prompt_length		,				segment_types=self.segment_types		,				use_cache=__lowercase		,				init_std=self.init_std		,				return_dict=self.return_dict		,				)
      def      UpperCAmelCase				(				self :      Tuple		,				__lowercase :      Union[str, Any]		,				__lowercase :      List[str]		,				*__lowercase :      Optional[int] )							->				List[Any]:
        __UpperCAmelCase							:     int									=  CpmAntModel(config=__lowercase )
        model.to(__lowercase )
        model.eval()

        __UpperCAmelCase							:     str									=  model(**__lowercase ).last_hidden_state

        self.parent.assertEqual(hidden_states.shape		,				(self.batch_size, self.seq_length, config.hidden_size) )
      def      UpperCAmelCase				(				self :      List[str]		,				__lowercase :      List[str]		,				__lowercase :      Optional[Any]		,				*__lowercase :      Optional[int] )							->				List[str]:
        __UpperCAmelCase							:     List[Any]									=  CpmAntForCausalLM(__lowercase )
        model.to(__lowercase )
        __UpperCAmelCase							:     Optional[Any]									=  input_ids["""input_ids"""].to(__lowercase )
        model.eval()

        __UpperCAmelCase							:     int									=  model(**__lowercase )
        self.parent.assertEqual(
            model_output.logits.shape		,				(self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)		,				)




      def      UpperCAmelCase				(				self :      List[Any] )							->				Dict:
        __UpperCAmelCase				,			__UpperCAmelCase							:     Optional[int]									=  self.prepare_config_and_inputs()
        return config, inputs_dict


@require_torch
class    a   (			lowercase__							,	lowercase__							,	unittest.TestCase       ):



      """simple docstring"""


      a  :			List[Any]								=     (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
      a  :			Optional[int]								=     (
          {'feature-extraction': CpmAntModel, 'text-generation': CpmAntForCausalLM} if is_torch_available() else {}
      )

      a  :			List[Any]								=     False
      a  :			Any								=     False
      a  :			List[Any]								=     False
      a  :			Tuple								=     False
      a  :			Any								=     False
      def      UpperCAmelCase				(				self :      Optional[int] )							->				Optional[Any]:
        __UpperCAmelCase							:     List[Any]									=  CpmAntModelTester(self )
        __UpperCAmelCase							:     Dict									=  ConfigTester(self		,				config_class=__lowercase )
      def      UpperCAmelCase				(				self :      str )							->				Optional[int]:
        self.config_tester.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()
      def      UpperCAmelCase				(				self :      List[str] )							->				List[str]:
        unittest.skip("""CPMAnt doesn't support input_embeds.""" )(self.test_inputs_embeds )
      def      UpperCAmelCase				(				self :      Any )							->				Union[str, Any]:
        unittest.skip(
            """CPMAnt doesn't support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model.""" )(self.test_retain_grad_hidden_states_attentions )
      def      UpperCAmelCase				(				self :      str )							->				List[Any]:
        __UpperCAmelCase				,			__UpperCAmelCase							:     Optional[Any]									=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_cpmant_model(__lowercase		,				__lowercase )




      def      UpperCAmelCase				(				self :      Optional[Any] )							->				str:
        __UpperCAmelCase				,			__UpperCAmelCase							:     Dict									=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(__lowercase		,				__lowercase )


@require_torch
class    a   (			unittest.TestCase       ):



      """simple docstring"""


      @tooslow
      def      UpperCAmelCase				(				self :      str )							->				Optional[Any]:
        __UpperCAmelCase							:     List[Any]									=  """今天天气真好！"""
        __UpperCAmelCase							:     int									=  """openbmb/cpm-ant-10b"""
        __UpperCAmelCase							:     Tuple									=  CpmAntModel.from_pretrained(__lowercase )
        __UpperCAmelCase							:     Optional[int]									=  CpmAntTokenizer.from_pretrained(__lowercase )
        __UpperCAmelCase							:     Union[str, Any]									=  tokenizer(__lowercase		,				return_tensors="""pt""" )
        __UpperCAmelCase							:     List[Any]									=  model(**__lowercase ).last_hidden_state

        __UpperCAmelCase							:     str									=  torch.tensor(
            [[[6.1_708, 5.9_244, 1.0_835], [6.5_207, 6.2_893, -11.3_324], [-1.0_107, -0.0_576, -5.9_577]]]		,				)
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3]		,				__lowercase		,				atol=1e-2 ) )


@require_torch
class    a   (			unittest.TestCase       ):



      """simple docstring"""


      @tooslow
      def      UpperCAmelCase				(				self :      List[Any] )							->				str:
        __UpperCAmelCase							:     List[Any]									=  """今天天气真好！"""
        __UpperCAmelCase							:     Optional[Any]									=  """openbmb/cpm-ant-10b"""
        __UpperCAmelCase							:     Tuple									=  CpmAntForCausalLM.from_pretrained(__lowercase )
        __UpperCAmelCase							:     Tuple									=  CpmAntTokenizer.from_pretrained(__lowercase )
        __UpperCAmelCase							:     List[Any]									=  tokenizer(__lowercase		,				return_tensors="""pt""" )
        __UpperCAmelCase							:     Tuple									=  model(**__lowercase ).logits

        __UpperCAmelCase							:     int									=  torch.tensor(
            [[[-6.4_267, -6.4_083, -6.3_958], [-5.8_802, -5.9_447, -5.7_811], [-5.3_896, -5.4_820, -5.4_295]]]		,				)
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3]		,				__lowercase		,				atol=1e-2 ) )
      @tooslow
      def      UpperCAmelCase				(				self :      Dict )							->				Dict:
        __UpperCAmelCase							:     List[str]									=  """openbmb/cpm-ant-10b"""
        __UpperCAmelCase							:     Dict									=  CpmAntForCausalLM.from_pretrained(__lowercase )
        __UpperCAmelCase							:     int									=  CpmAntTokenizer.from_pretrained(__lowercase )
        __UpperCAmelCase							:     List[Any]									=  """今天天气不错，"""
        __UpperCAmelCase							:     Optional[int]									=  """今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的"""
        __UpperCAmelCase							:     Optional[int]									=  tokenizer(__lowercase		,				return_tensors="""pt""" )
        __UpperCAmelCase							:     Tuple									=  model.generate(**__lowercase )
        __UpperCAmelCase							:     List[Any]									=  tokenizer.batch_decode(__lowercase )
        self.assertEqual(__lowercase		,				__lowercase )




      @tooslow
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				Any:
        __UpperCAmelCase							:     int									=  """openbmb/cpm-ant-10b"""
        __UpperCAmelCase							:     Any									=  CpmAntForCausalLM.from_pretrained(__lowercase )
        __UpperCAmelCase							:     Optional[Any]									=  CpmAntTokenizer.from_pretrained(__lowercase )
        __UpperCAmelCase							:     Optional[Any]									=  ["""今天天气不错，""", """新年快乐，万事如意！"""]
        __UpperCAmelCase							:     str									=  [
            """今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的""",
            """新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的""",
        ]
        __UpperCAmelCase							:     Union[str, Any]									=  tokenizer(__lowercase		,				return_tensors="""pt"""		,				padding=__lowercase )
        __UpperCAmelCase							:     int									=  model.generate(**__lowercase )
        __UpperCAmelCase							:     Optional[int]									=  tokenizer.batch_decode(__lowercase )
        self.assertEqual(__lowercase		,				__lowercase )


