





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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
       _lowerCamelCase			       =      'platform'

       import jax
       import jax.numpy as jnp

       from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
           FlaxBlenderbotSmallForConditionalGeneration,
           FlaxBlenderbotSmallModel,
           shift_tokens_right,
       )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_=None ,    lowercase_=None ,    lowercase_=None ,    lowercase_=None ,    lowercase_=None ,    lowercase_=None ,    ):
       if attention_mask is None:
              _lowerCamelCase							:     Dict	=		np.where(input_ids != config.pad_token_id ,    1 ,    0      )
       if decoder_attention_mask is None:
              _lowerCamelCase							:     Optional[Any]	=		np.where(decoder_input_ids != config.pad_token_id ,    1 ,    0      )
       if head_mask is None:
              _lowerCamelCase							:     List[str]	=		np.ones((config.encoder_layers, config.encoder_attention_heads)      )
       if decoder_head_mask is None:
              _lowerCamelCase							:     Union[str, Any]	=		np.ones((config.decoder_layers, config.decoder_attention_heads)      )
       if cross_attn_head_mask is None:
              _lowerCamelCase							:     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 			__A       :




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__=13      ,							a__=7      ,							a__=True      ,							a__=False      ,							a__=99      ,							a__=16      ,							a__=2      ,							a__=4      ,							a__=4      ,							a__="gelu"      ,							a__=0.1      ,							a__=0.1      ,							a__=32      ,							a__=2      ,							a__=1      ,							a__=0      ,							a__=0.02      ,							):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		parent
             _lowerCamelCase							:     List[str]	=		batch_size
             _lowerCamelCase							:     Optional[Any]	=		seq_length
             _lowerCamelCase							:     Dict	=		is_training
             _lowerCamelCase							:     str	=		use_labels
             _lowerCamelCase							:     Any	=		vocab_size
             _lowerCamelCase							:     List[str]	=		hidden_size
             _lowerCamelCase							:     List[Any]	=		num_hidden_layers
             _lowerCamelCase							:     Optional[int]	=		num_attention_heads
             _lowerCamelCase							:     str	=		intermediate_size
             _lowerCamelCase							:     Optional[Any]	=		hidden_act
             _lowerCamelCase							:     List[str]	=		hidden_dropout_prob
             _lowerCamelCase							:     int	=		attention_probs_dropout_prob
             _lowerCamelCase							:     List[str]	=		max_position_embeddings
             _lowerCamelCase							:     int	=		eos_token_id
             _lowerCamelCase							:     str	=		pad_token_id
             _lowerCamelCase							:     List[Any]	=		bos_token_id
             _lowerCamelCase							:     str	=		initializer_range


      def 				__snake_case			(		self):



             """simple docstring"""





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

             _lowerCamelCase							:     Tuple	=		shift_tokens_right(a__      ,							1      ,							2)

             _lowerCamelCase							:     Any	=		BlenderbotSmallConfig(
                 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=a__      ,							)
             _lowerCamelCase							:     Union[str, Any]	=		prepare_blenderbot_inputs_dict(a__      ,							a__      ,							a__)
             return config, inputs_dict


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		self.prepare_config_and_inputs()
             return config, inputs_dict


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		20
             _lowerCamelCase							:     Any	=		model_class_name(a__)

             _lowerCamelCase							:     Optional[int]	=		model.encode(inputs_dict['''input_ids'''])

             _lowerCamelCase,  _lowerCamelCase							:     List[str]	=		(
                 inputs_dict['''decoder_input_ids'''],
                 inputs_dict['''decoder_attention_mask'''],
             )

             _lowerCamelCase							:     List[str]	=		model.init_cache(decoder_input_ids.shape[0]      ,							a__      ,							a__)
             _lowerCamelCase							:     Any	=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,							dtype='''i4''')

             _lowerCamelCase							:     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)      ,							)
             _lowerCamelCase							:     List[str]	=		model.decode(
                 decoder_input_ids[:, :-1]      ,							a__      ,							decoder_attention_mask=a__      ,							past_key_values=a__      ,							decoder_position_ids=a__      ,							)

             _lowerCamelCase							:     Optional[int]	=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,							dtype='''i4''')
             _lowerCamelCase							:     Union[str, Any]	=		model.decode(
                 decoder_input_ids[:, -1:]      ,							a__      ,							decoder_attention_mask=a__      ,							past_key_values=outputs_cache.past_key_values      ,							decoder_position_ids=a__      ,							)

             _lowerCamelCase							:     Dict	=		model.decode(a__      ,							a__)

             _lowerCamelCase							:     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 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     int	=		20
             _lowerCamelCase							:     List[Any]	=		model_class_name(a__)

             _lowerCamelCase							:     Dict	=		model.encode(inputs_dict['''input_ids'''])

             _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		(
                 inputs_dict['''decoder_input_ids'''],
                 inputs_dict['''decoder_attention_mask'''],
             )

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

             _lowerCamelCase							:     List[Any]	=		model.init_cache(decoder_input_ids.shape[0]      ,							a__      ,							a__)
             _lowerCamelCase							:     Any	=		jnp.broadcast_to(
                 jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]      ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,							)

             _lowerCamelCase							:     Dict	=		model.decode(
                 decoder_input_ids[:, :-1]      ,							a__      ,							decoder_attention_mask=a__      ,							past_key_values=a__      ,							decoder_position_ids=a__      ,							)
             _lowerCamelCase							:     Tuple	=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,							dtype='''i4''')
             _lowerCamelCase							:     Any	=		model.decode(
                 decoder_input_ids[:, -1:]      ,							a__      ,							past_key_values=outputs_cache.past_key_values      ,							decoder_attention_mask=a__      ,							decoder_position_ids=a__      ,							)

             _lowerCamelCase							:     Tuple	=		model.decode(a__      ,							a__      ,							decoder_attention_mask=a__)

             _lowerCamelCase							:     str	=		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"""







      UpperCAmelCase__						=      99


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		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      ,							)

             _lowerCamelCase							:     Dict	=		input_ids.shape[0]
             _lowerCamelCase							:     Union[str, Any]	=		BlenderbotSmallConfig(
                 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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     List[str]	=		self._get_config_and_data()
             _lowerCamelCase							:     Any	=		FlaxBlenderbotSmallForConditionalGeneration(a__)
             _lowerCamelCase							:     Any	=		lm_model(input_ids=a__)
             _lowerCamelCase							:     List[Any]	=		(batch_size, input_ids.shape[1], config.vocab_size)
             self.assertEqual(outputs['''logits'''].shape      ,							a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     int	=		BlenderbotSmallConfig(
                 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      ,							)
             _lowerCamelCase							:     Tuple	=		FlaxBlenderbotSmallForConditionalGeneration(a__)
             _lowerCamelCase							:     Any	=		np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]      ,							dtype=np.intaa)
             _lowerCamelCase							:     Dict	=		np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]      ,							dtype=np.intaa)
             _lowerCamelCase							:     Optional[int]	=		lm_model(input_ids=a__      ,							decoder_input_ids=a__)
             _lowerCamelCase							:     Tuple	=		(*summary.shape, config.vocab_size)
             self.assertEqual(outputs['''logits'''].shape      ,							a__)



      def 				__snake_case			(		self):



             """simple docstring"""





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







@require_flax
class 			__A       (      lowerCamelCase__ ,unittest.TestCase ,lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      True
      UpperCAmelCase__						=      (
          (
              FlaxBlenderbotSmallModel,
              FlaxBlenderbotSmallForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      UpperCAmelCase__						=      (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		FlaxBlenderbotSmallModelTester(self)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     List[str]	=		self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                    self.model_tester.check_use_cache_forward(a__      ,							a__      ,							a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     List[str]	=		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(a__      ,							a__      ,							a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     List[str]	=		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__):
                           _lowerCamelCase							:     str	=		self._prepare_for_class(a__      ,							a__)
                           _lowerCamelCase							:     List[Any]	=		model_class(a__)

                           @jax.jit
                           def encode_jitted(a__      ,							a__=None      ,							**a__):
                                  return model.encode(input_ids=a__      ,							attention_mask=a__)

                           with self.subTest('''JIT Enabled'''):
                                  _lowerCamelCase							:     Union[str, Any]	=		encode_jitted(**a__).to_tuple()

                           with self.subTest('''JIT Disabled'''):
                                  with jax.disable_jit():
                                         _lowerCamelCase							:     Dict	=		encode_jitted(**a__).to_tuple()

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__):
                           _lowerCamelCase							:     Optional[Any]	=		model_class(a__)
                           _lowerCamelCase							:     Tuple	=		model.encode(inputs_dict['''input_ids''']      ,							inputs_dict['''attention_mask'''])

                           _lowerCamelCase							:     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(a__      ,							a__      ,							a__):
                                  return model.decode(
                                      decoder_input_ids=a__      ,							decoder_attention_mask=a__      ,							encoder_outputs=a__      ,							)

                           with self.subTest('''JIT Enabled'''):
                                  _lowerCamelCase							:     str	=		decode_jitted(**a__).to_tuple()

                           with self.subTest('''JIT Disabled'''):
                                  with jax.disable_jit():
                                         _lowerCamelCase							:     Union[str, Any]	=		decode_jitted(**a__).to_tuple()

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



      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             for model_class_name in self.all_model_classes:
                    _lowerCamelCase							:     Optional[int]	=		model_class_name.from_pretrained('''facebook/blenderbot_small-90M''')
                    # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                    _lowerCamelCase							:     Optional[Any]	=		np.ones((1, 1)) * model.config.eos_token_id
                    _lowerCamelCase							:     Dict	=		model(a__)
                    self.assertIsNotNone(a__)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase			       =      {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'OPTForCausalLM',
           'OPTModel',
           'OPTPreTrainedModel',
           'OPTForSequenceClassification',
           'OPTForQuestionAnswering',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      ['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'FlaxOPTForCausalLM',
           'FlaxOPTModel',
           'FlaxOPTPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_opt import (
                  OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OPTForCausalLM,
                  OPTForQuestionAnswering,
                  OPTForSequenceClassification,
                  OPTModel,
                  OPTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
       import sys

       _lowerCamelCase			       =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

