


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
    __UpperCamelCase    :						int     =							'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       (     UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Dict=None						,	UpperCAmelCase      :			Tuple=None						,	UpperCAmelCase      :			Optional[int]=None						,	UpperCAmelCase      :			Any=None						,	UpperCAmelCase      :			int=None						,	UpperCAmelCase      :			Any=None						,	):
      """simple docstring"""




      if attention_mask is None:
            __lowerCamelCase     :      Dict       						=						np.where(input_ids != config.pad_token_id						,	1						,	0      )
      if decoder_attention_mask is None:
            __lowerCamelCase     :      Dict       						=						np.where(decoder_input_ids != config.pad_token_id						,	1						,	0      )
      if head_mask is None:
            __lowerCamelCase     :      Dict       						=						np.ones((config.encoder_layers, config.encoder_attention_heads)      )
      if decoder_head_mask is None:
            __lowerCamelCase     :      List[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 							_UpperCamelCase :

   '''simple docstring'''







   def __init__(  self  : Tuple       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : str=1_3       ,  _lowerCamelCase  : int=7       ,  _lowerCamelCase  : Optional[Any]=True       ,  _lowerCamelCase  : Tuple=False       ,  _lowerCamelCase  : Dict=9_9       ,  _lowerCamelCase  : Union[str, Any]=1_6       ,  _lowerCamelCase  : Optional[int]=2       ,  _lowerCamelCase  : str=4       ,  _lowerCamelCase  : Optional[Any]=4       ,  _lowerCamelCase  : Tuple="gelu"       ,  _lowerCamelCase  : Dict=0.1       ,  _lowerCamelCase  : List[str]=0.1       ,  _lowerCamelCase  : Optional[Any]=3_2       ,  _lowerCamelCase  : List[Any]=2       ,  _lowerCamelCase  : List[str]=1       ,  _lowerCamelCase  : Tuple=0       ,  _lowerCamelCase  : Optional[int]=0.02       ,  ):






         '''simple docstring'''



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

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,  self.vocab_size							)       ,  3       ,  self.vocab_size							)
         __lowerCamelCase     :      Tuple       						=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,  dtype=np.intaa							))       ,  -1							)

         __lowerCamelCase     :      List[str]       						=						shift_tokens_right(_lowerCamelCase       ,  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=_lowerCamelCase       ,  )
         __lowerCamelCase     :      Any       						=						prepare_blenderbot_inputs_dict(_lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase							)
         return config, inputs_dict

   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase     :      List[str]       						=						self.prepare_config_and_inputs()
         return config, inputs_dict

   def   _snake_case    (  self  : List[Any]       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						2_0
         __lowerCamelCase     :      Dict       						=						model_class_name(_lowerCamelCase							)

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

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

         __lowerCamelCase     :      Tuple       						=						model.init_cache(decoder_input_ids.shape[0]       ,  _lowerCamelCase       ,  _lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,  dtype="""i4"""							)

         __lowerCamelCase     :      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)       ,  )
         __lowerCamelCase     :      Union[str, Any]       						=						model.decode(
             decoder_input_ids[:, :-1]       ,  _lowerCamelCase       ,  decoder_attention_mask=_lowerCamelCase       ,  past_key_values=_lowerCamelCase       ,  decoder_position_ids=_lowerCamelCase       ,  )

         __lowerCamelCase     :      List[Any]       						=						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:]       ,  _lowerCamelCase       ,  decoder_attention_mask=_lowerCamelCase       ,  past_key_values=outputs_cache.past_key_values       ,  decoder_position_ids=_lowerCamelCase       ,  )

         __lowerCamelCase     :      Optional[int]       						=						model.decode(_lowerCamelCase       ,  _lowerCamelCase							)

         __lowerCamelCase     :      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   _snake_case    (  self  : int       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						2_0
         __lowerCamelCase     :      Optional[int]       						=						model_class_name(_lowerCamelCase							)

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

         __lowerCamelCase						, __lowerCamelCase     :      int       						=						(
             inputs_dict["""decoder_input_ids"""],
             inputs_dict["""decoder_attention_mask"""],
         )

         __lowerCamelCase     :      Union[str, Any]       						=						jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
             ]       ,  axis=-1       ,  )

         __lowerCamelCase     :      str       						=						model.init_cache(decoder_input_ids.shape[0]       ,  _lowerCamelCase       ,  _lowerCamelCase							)
         __lowerCamelCase     :      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)       ,  )

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

         __lowerCamelCase     :      int       						=						model.decode(_lowerCamelCase       ,  _lowerCamelCase       ,  decoder_attention_mask=_lowerCamelCase							)

         __lowerCamelCase     :      Optional[int]       						=						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 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   a_				:							int    = 99

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						np.array(
             [
                 [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                 [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                 [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                 [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                 [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                 [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                 [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                 [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                 [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                 [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                 [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                 [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                 [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
             ]       ,  dtype=np.intaa       ,  )

         __lowerCamelCase     :      List[str]       						=						input_ids.shape[0]
         __lowerCamelCase     :      Any       						=						BlenderbotSmallConfig(
             vocab_size=self.vocab_size       ,  d_model=2_4       ,  encoder_layers=2       ,  decoder_layers=2       ,  encoder_attention_heads=2       ,  decoder_attention_heads=2       ,  encoder_ffn_dim=3_2       ,  decoder_ffn_dim=3_2       ,  max_position_embeddings=4_8       ,  eos_token_id=2       ,  pad_token_id=1       ,  bos_token_id=0       ,  )
         return config, input_ids, batch_size

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      str       						=						self._get_config_and_data()
         __lowerCamelCase     :      Tuple       						=						FlaxBlenderbotSmallForConditionalGeneration(_lowerCamelCase							)
         __lowerCamelCase     :      Optional[int]       						=						lm_model(input_ids=_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs["""logits"""].shape       ,  _lowerCamelCase							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						BlenderbotSmallConfig(
             vocab_size=self.vocab_size       ,  d_model=1_4       ,  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=4_8       ,  )
         __lowerCamelCase     :      Union[str, Any]       						=						FlaxBlenderbotSmallForConditionalGeneration(_lowerCamelCase							)
         __lowerCamelCase     :      Any       						=						np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]       ,  dtype=np.intaa							)
         __lowerCamelCase     :      Union[str, Any]       						=						np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]       ,  dtype=np.intaa							)
         __lowerCamelCase     :      int       						=						lm_model(input_ids=_lowerCamelCase       ,  decoder_input_ids=_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						(*summary.shape, config.vocab_size)
         self.assertEqual(outputs["""logits"""].shape       ,  _lowerCamelCase							)


   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]       ,  dtype=np.intaa							)
         __lowerCamelCase     :      Optional[Any]       						=						shift_tokens_right(_lowerCamelCase       ,  1       ,  2							)
         __lowerCamelCase     :      int       						=						np.equal(_lowerCamelCase       ,  1							).astype(np.floataa							).sum()
         __lowerCamelCase     :      Any       						=						np.equal(_lowerCamelCase       ,  1							).astype(np.floataa							).sum()
         self.assertEqual(shifted.shape       ,  input_ids.shape							)
         self.assertEqual(_lowerCamelCase       ,  n_pad_before - 1							)
         self.assertTrue(np.equal(shifted[:, 0]       ,  2							).all()							)



@require_flax
class 							_UpperCamelCase (						A,unittest.TestCase,A						):

   '''simple docstring'''







   a_				:							Union[str, Any]    = True
   a_				:							Dict    = (
       (
           FlaxBlenderbotSmallModel,
           FlaxBlenderbotSmallForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   a_				:							List[str]    = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						FlaxBlenderbotSmallModelTester(self							)

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase     :      Dict       						=						self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(_lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase							)

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase     :      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(_lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase							)

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase     :      List[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     :      Union[str, Any]       						=						self._prepare_for_class(_lowerCamelCase       ,  _lowerCamelCase							)
                     __lowerCamelCase     :      Dict       						=						model_class(_lowerCamelCase							)

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

                     with self.subTest("""JIT Enabled"""							):
                           __lowerCamelCase     :      List[str]       						=						encode_jitted(**_lowerCamelCase							).to_tuple()

                     with self.subTest("""JIT Disabled"""							):
                           with jax.disable_jit():
                                 __lowerCamelCase     :      str       						=						encode_jitted(**_lowerCamelCase							).to_tuple()

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

   def   _snake_case    (  self  : str							):






         '''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     :      Any       						=						model_class(_lowerCamelCase							)
                     __lowerCamelCase     :      Optional[Any]       						=						model.encode(inputs_dict["""input_ids"""]       ,  inputs_dict["""attention_mask"""]							)

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

                     with self.subTest("""JIT Enabled"""							):
                           __lowerCamelCase     :      Dict       						=						decode_jitted(**_lowerCamelCase							).to_tuple()

                     with self.subTest("""JIT Disabled"""							):
                           with jax.disable_jit():
                                 __lowerCamelCase     :      Any       						=						decode_jitted(**_lowerCamelCase							).to_tuple()

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


   @slow
   def   _snake_case    (  self  : Union[str, Any]							):






         '''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     :      List[Any]       						=						np.ones((1, 1)							) * model.config.eos_token_id
               __lowerCamelCase     :      Dict       						=						model(_lowerCamelCase							)
               self.assertIsNotNone(_lowerCamelCase							)







# Copyright 2023 The HuggingFace 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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    :						Any     =							{
    'configuration_mgp_str': ['MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MgpstrConfig'],
    'processing_mgp_str': ['MgpstrProcessor'],
    'tokenization_mgp_str': ['MgpstrTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						Tuple     =							[
        'MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MgpstrModel',
        'MgpstrPreTrainedModel',
        'MgpstrForSceneTextRecognition',
    ]

if TYPE_CHECKING:
    from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
    from .processing_mgp_str import MgpstrProcessor
    from .tokenization_mgp_str import MgpstrTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mgp_str import (
            MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
            MgpstrForSceneTextRecognition,
            MgpstrModel,
            MgpstrPreTrainedModel,
        )
else:
    import sys

    __UpperCamelCase    :						Optional[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



