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
      A   :     Optional[int]       =				"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    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase=None   ,					__UpperCamelCase=None   ,					__UpperCamelCase=None   ,					__UpperCamelCase=None   ,					__UpperCamelCase=None   ,					__UpperCamelCase=None   ,					):
      if attention_mask is None:
            SCREAMING_SNAKE_CASE_      =     np.where(input_ids != config.pad_token_id   ,					1   ,					0 )
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE_      =     np.where(decoder_input_ids != config.pad_token_id   ,					1   ,					0 )
      if head_mask is None:
            SCREAMING_SNAKE_CASE_      =     np.ones((config.encoder_layers, config.encoder_attention_heads) )
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE_      =     np.ones((config.decoder_layers, config.decoder_attention_heads) )
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE_      =     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      lowerCamelCase    :
  """simple docstring"""







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







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

        SCREAMING_SNAKE_CASE_      =     shift_tokens_right(__magic_name__     ,   1     ,   2      )

        SCREAMING_SNAKE_CASE_      =     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=__magic_name__     ,   )
        SCREAMING_SNAKE_CASE_      =     prepare_blenderbot_inputs_dict(__magic_name__     ,   __magic_name__     ,   __magic_name__      )
        return config, inputs_dict







  def       __A   (							self       :				List[str]      )     ->		Dict:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self.prepare_config_and_inputs()
        return config, inputs_dict







  def       __A   (							self       :				Optional[Any]     ,   __magic_name__       :				Optional[Any]     ,   __magic_name__       :				List[str]     ,   __magic_name__       :				Optional[int]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     20
        SCREAMING_SNAKE_CASE_      =     model_class_name(__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     model.encode(inputs_dict["input_ids"]      )

        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        SCREAMING_SNAKE_CASE_      =     model.init_cache(decoder_input_ids.shape[0]     ,   __magic_name__     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,   dtype="i4"      )

        SCREAMING_SNAKE_CASE_      =     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_      =     model.decode(
            decoder_input_ids[:, :-1]     ,   __magic_name__     ,   decoder_attention_mask=__magic_name__     ,   past_key_values=__magic_name__     ,   decoder_position_ids=__magic_name__     ,   )

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

        SCREAMING_SNAKE_CASE_      =     model.decode(__magic_name__     ,   __magic_name__      )

        SCREAMING_SNAKE_CASE_      =     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       :				int     ,   __magic_name__       :				Any     ,   __magic_name__       :				List[Any]     ,   __magic_name__       :				Any      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     20
        SCREAMING_SNAKE_CASE_      =     model_class_name(__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     model.encode(inputs_dict["input_ids"]      )

        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        SCREAMING_SNAKE_CASE_      =     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_      =     model.init_cache(decoder_input_ids.shape[0]     ,   __magic_name__     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     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_      =     model.decode(
            decoder_input_ids[:, :-1]     ,   __magic_name__     ,   decoder_attention_mask=__magic_name__     ,   past_key_values=__magic_name__     ,   decoder_position_ids=__magic_name__     ,   )
        SCREAMING_SNAKE_CASE_      =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,   dtype="i4"      )
        SCREAMING_SNAKE_CASE_      =     model.decode(
            decoder_input_ids[:, -1:]     ,   __magic_name__     ,   past_key_values=outputs_cache.past_key_values     ,   decoder_attention_mask=__magic_name__     ,   decoder_position_ids=__magic_name__     ,   )

        SCREAMING_SNAKE_CASE_      =     model.decode(__magic_name__     ,   __magic_name__     ,   decoder_attention_mask=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     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      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  9_9







  def       __A   (							self       :				Dict      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     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_      =     input_ids.shape[0]
        SCREAMING_SNAKE_CASE_      =     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       :				Union[str, Any]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self._get_config_and_data()
        SCREAMING_SNAKE_CASE_      =     FlaxBlenderbotForConditionalGeneration(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     lm_model(input_ids=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["logits"].shape     ,   __magic_name__      )







  def       __A   (							self       :				Optional[Any]      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     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_      =     FlaxBlenderbotForConditionalGeneration(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,   dtype=np.intaa      )
        SCREAMING_SNAKE_CASE_      =     np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,   dtype=np.intaa      )
        SCREAMING_SNAKE_CASE_      =     lm_model(input_ids=__magic_name__     ,   decoder_input_ids=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     (*summary.shape, config.vocab_size)
        self.assertEqual(outputs["logits"].shape     ,   __magic_name__      )







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



@require_flax
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase     ,					SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  True
  lowerCamelCase__   =  (
      (
          FlaxBlenderbotModel,
          FlaxBlenderbotForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  lowerCamelCase__   =  (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







  def       __A   (							self       :				Union[str, Any]      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     FlaxBlenderbotModelTester(self      )







  def       __A   (							self       :				Optional[int]      )     ->		int:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(__magic_name__     ,   __magic_name__     ,   __magic_name__      )







  def       __A   (							self       :				Optional[Any]      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     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(__magic_name__     ,   __magic_name__     ,   __magic_name__      )







  def       __A   (							self       :				int      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     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_      =     self._prepare_for_class(__magic_name__     ,   __magic_name__      )
                    SCREAMING_SNAKE_CASE_      =     model_class(__magic_name__      )

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

                    with self.subTest("JIT Enabled"      ):
                          SCREAMING_SNAKE_CASE_      =     encode_jitted(**__magic_name__      ).to_tuple()

                    with self.subTest("JIT Disabled"      ):
                          with jax.disable_jit():
                                SCREAMING_SNAKE_CASE_      =     encode_jitted(**__magic_name__      ).to_tuple()

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







  def       __A   (							self       :				int      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     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_      =     model_class(__magic_name__      )
                    SCREAMING_SNAKE_CASE_      =     model.encode(inputs_dict["input_ids"]     ,   inputs_dict["attention_mask"]      )

                    SCREAMING_SNAKE_CASE_      =     {
                        "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(__magic_name__       :				Optional[int]     ,   __magic_name__       :				Dict     ,   __magic_name__       :				Any      ):
                          return model.decode(
                              decoder_input_ids=__magic_name__     ,   decoder_attention_mask=__magic_name__     ,   encoder_outputs=__magic_name__     ,   )

                    with self.subTest("JIT Enabled"      ):
                          SCREAMING_SNAKE_CASE_      =     decode_jitted(**__magic_name__      ).to_tuple()

                    with self.subTest("JIT Disabled"      ):
                          with jax.disable_jit():
                                SCREAMING_SNAKE_CASE_      =     decode_jitted(**__magic_name__      ).to_tuple()

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







  @slow
  def       __A   (							self       :				Union[str, Any]      )     ->		Any:
        for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_      =     model_class_name.from_pretrained("facebook/blenderbot-400M-distill"      )
              # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
              SCREAMING_SNAKE_CASE_      =     np.ones((1, 1)      ) * model.config.eos_token_id
              SCREAMING_SNAKE_CASE_      =     model(__magic_name__      )
              self.assertIsNotNone(__magic_name__      )







  @unittest.skipUnless(jax_device != "cpu"     ,   "3B test too slow on CPU."      )
  @slow
  def       __A   (							self       :				Tuple      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
        SCREAMING_SNAKE_CASE_      =     {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

        SCREAMING_SNAKE_CASE_      =     FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"     ,   from_pt=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"      )

        SCREAMING_SNAKE_CASE_      =     ["Sam"]
        SCREAMING_SNAKE_CASE_      =     tokenizer(__magic_name__     ,   return_tensors="jax"      )

        SCREAMING_SNAKE_CASE_      =     model.generate(**__magic_name__     ,   **__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     "Sam is a great name. It means \"sun\" in Gaelic."

        SCREAMING_SNAKE_CASE_      =     tokenizer.batch_decode(__magic_name__     ,   **__magic_name__      )
        assert generated_txt[0].strip() == tgt_text



import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin


@require_jieba
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  CpmAntTokenizer
  lowerCamelCase__   =  False







  def       __A   (							self       :				List[str]      )     ->		str:
        super().setUp()

        SCREAMING_SNAKE_CASE_      =     [
            "<d>",
            "</d>",
            "<s>",
            "</s>",
            "</_>",
            "<unk>",
            "<pad>",
            "</n>",
            "我",
            "是",
            "C",
            "P",
            "M",
            "A",
            "n",
            "t",
        ]
        SCREAMING_SNAKE_CASE_      =     os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]      )
        with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"      ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )







  @tooslow
  def       __A   (							self       :				int      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"      )
        SCREAMING_SNAKE_CASE_      =     "今天天气真好！"
        SCREAMING_SNAKE_CASE_      =     ["今天", "天气", "真", "好", "！"]
        SCREAMING_SNAKE_CASE_      =     tokenizer.tokenize(__magic_name__      )
        self.assertListEqual(__magic_name__     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     "今天天气真好！"
        SCREAMING_SNAKE_CASE_      =     [tokenizer.bos_token] + tokens

        SCREAMING_SNAKE_CASE_      =     [6, 9_802, 14_962, 2_082, 831, 244]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__      )     ,   __magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokenizer.decode(__magic_name__      )
        self.assertEqual(__magic_name__     ,   __magic_name__      )


