





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							=			'platform'

     import jax
     import jax.numpy as jnp

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







def    lowerCAmelCase__(						lowercase							:     Optional[Any] ,  lowercase							:     List[str] ,  lowercase							:     Tuple=None ,  lowercase							:     Tuple=None ,  lowercase							:     int=None ,  lowercase							:     Optional[int]=None ,  lowercase							:     Any=None ,  lowercase							:     Optional[int]=None ,  )			->     Any:

  if attention_mask is None:
    __snake_case :					Tuple          =							np.where(input_ids != config.pad_token_id ,  1 ,  0					)
  if decoder_attention_mask is None:
    __snake_case :					Optional[Any]          =							np.where(decoder_input_ids != config.pad_token_id ,  1 ,  0					)
  if head_mask is None:
    __snake_case :					str          =							np.ones((config.encoder_layers, config.encoder_attention_heads)					)
  if decoder_head_mask is None:
    __snake_case :					Union[str, Any]          =							np.ones((config.decoder_layers, config.decoder_attention_heads)					)
  if cross_attn_head_mask is None:
    __snake_case :					Union[str, Any]          =							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					,    UpperCAmelCase					,    UpperCAmelCase=13					,    UpperCAmelCase=7					,    UpperCAmelCase=True					,    UpperCAmelCase=False					,    UpperCAmelCase=99					,    UpperCAmelCase=16					,    UpperCAmelCase=2					,    UpperCAmelCase=4					,    UpperCAmelCase=4					,    UpperCAmelCase="gelu"					,    UpperCAmelCase=0.1					,    UpperCAmelCase=0.1					,    UpperCAmelCase=32					,    UpperCAmelCase=2					,    UpperCAmelCase=1					,    UpperCAmelCase=0					,    UpperCAmelCase=0.02					,    )							-> int:






        '''simple docstring'''






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







      def     UpperCAmelCase		(			self )							-> Union[str, Any]:






        '''simple docstring'''






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

        __snake_case :					int          =							shift_tokens_right(lowerCAmelCase__					,    1					,    2 )

        __snake_case :					List[str]          =							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__					,    )
        __snake_case :					Union[str, Any]          =							prepare_blenderbot_inputs_dict(lowerCAmelCase__					,    lowerCAmelCase__					,    lowerCAmelCase__ )
        return config, inputs_dict







      def     UpperCAmelCase		(			self )							-> List[str]:






        '''simple docstring'''






        __snake_case     , __snake_case :					Dict          =							self.prepare_config_and_inputs()
        return config, inputs_dict







      def     UpperCAmelCase		(			self					,    UpperCAmelCase					,    UpperCAmelCase					,    UpperCAmelCase )							-> Dict:






        '''simple docstring'''






        __snake_case :					Any          =							20
        __snake_case :					str          =							model_class_name(lowerCAmelCase__ )

        __snake_case :					Any          =							model.encode(inputs_dict["input_ids"] )

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

        __snake_case :					List[str]          =							model.init_cache(decoder_input_ids.shape[0]					,    lowerCAmelCase__					,    lowerCAmelCase__ )
        __snake_case :					int          =							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,    dtype="i4" )

        __snake_case :					Dict          =							jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]					,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,    )
        __snake_case :					int          =							model.decode(
            decoder_input_ids[:, :-1]					,    lowerCAmelCase__					,    decoder_attention_mask=lowerCAmelCase__					,    past_key_values=lowerCAmelCase__					,    decoder_position_ids=lowerCAmelCase__					,    )

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

        __snake_case :					Any          =							model.decode(lowerCAmelCase__					,    lowerCAmelCase__ )

        __snake_case :					List[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					,    UpperCAmelCase					,    UpperCAmelCase					,    UpperCAmelCase )							-> Tuple:






        '''simple docstring'''






        __snake_case :					Optional[int]          =							20
        __snake_case :					Dict          =							model_class_name(lowerCAmelCase__ )

        __snake_case :					str          =							model.encode(inputs_dict["input_ids"] )

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

        __snake_case :					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					,    )

        __snake_case :					str          =							model.init_cache(decoder_input_ids.shape[0]					,    lowerCAmelCase__					,    lowerCAmelCase__ )
        __snake_case :					str          =							jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]					,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,    )

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

        __snake_case :					List[str]          =							model.decode(lowerCAmelCase__					,    lowerCAmelCase__					,    decoder_attention_mask=lowerCAmelCase__ )

        __snake_case :					List[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 					_lowerCamelCase			(     unittest.TestCase     ):



      """simple docstring"""






      UpperCAmelCase_    :   List[Any]      =99







      def     UpperCAmelCase		(			self )							-> Dict:






        '''simple docstring'''






        __snake_case :					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					,    )

        __snake_case :					Any          =							input_ids.shape[0]
        __snake_case :					str          =							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     UpperCAmelCase		(			self )							-> Optional[Any]:






        '''simple docstring'''






        __snake_case     , __snake_case     , __snake_case :					Optional[int]          =							self._get_config_and_data()
        __snake_case :					Any          =							FlaxBlenderbotSmallForConditionalGeneration(lowerCAmelCase__ )
        __snake_case :					List[Any]          =							lm_model(input_ids=lowerCAmelCase__ )
        __snake_case :					Dict          =							(batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["logits"].shape					,    lowerCAmelCase__ )







      def     UpperCAmelCase		(			self )							-> Union[str, Any]:






        '''simple docstring'''






        __snake_case :					Optional[Any]          =							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					,    )
        __snake_case :					List[str]          =							FlaxBlenderbotSmallForConditionalGeneration(lowerCAmelCase__ )
        __snake_case :					Optional[int]          =							np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					,    dtype=np.intaa )
        __snake_case :					int          =							np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					,    dtype=np.intaa )
        __snake_case :					Union[str, Any]          =							lm_model(input_ids=lowerCAmelCase__					,    decoder_input_ids=lowerCAmelCase__ )
        __snake_case :					Dict          =							(*summary.shape, config.vocab_size)
        self.assertEqual(outputs["logits"].shape					,    lowerCAmelCase__ )







      def     UpperCAmelCase		(			self )							-> Dict:






        '''simple docstring'''






        __snake_case :					Any          =							np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					,    dtype=np.intaa )
        __snake_case :					Optional[Any]          =							shift_tokens_right(lowerCAmelCase__					,    1					,    2 )
        __snake_case :					str          =							np.equal(lowerCAmelCase__					,    1 ).astype(np.floataa ).sum()
        __snake_case :					Tuple          =							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 					_lowerCamelCase			(     a			, unittest.TestCase			, a     ):



      """simple docstring"""






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







      def     UpperCAmelCase		(			self )							-> Dict:






        '''simple docstring'''






        __snake_case :					Tuple          =							FlaxBlenderbotSmallModelTester(self )







      def     UpperCAmelCase		(			self )							-> List[Any]:






        '''simple docstring'''






        __snake_case     , __snake_case :					List[str]          =							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     UpperCAmelCase		(			self )							-> Dict:






        '''simple docstring'''






        __snake_case     , __snake_case :					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(lowerCAmelCase__					,    lowerCAmelCase__					,    lowerCAmelCase__ )







      def     UpperCAmelCase		(			self )							-> List[Any]:






        '''simple docstring'''






        __snake_case     , __snake_case :					Tuple          =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
            __snake_case :					str          =							self._prepare_for_class(lowerCAmelCase__					,    lowerCAmelCase__ )
            __snake_case :					List[str]          =							model_class(lowerCAmelCase__ )

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

            with self.subTest("JIT Enabled" ):
              __snake_case :					Optional[Any]          =							encode_jitted(**lowerCAmelCase__ ).to_tuple()

            with self.subTest("JIT Disabled" ):
              with jax.disable_jit():
                __snake_case :					Tuple          =							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     UpperCAmelCase		(			self )							-> Any:






        '''simple docstring'''






        __snake_case     , __snake_case :					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__ ):
            __snake_case :					Dict          =							model_class(lowerCAmelCase__ )
            __snake_case :					Union[str, Any]          =							model.encode(inputs_dict["input_ids"]					,    inputs_dict["attention_mask"] )

            __snake_case :					int          =							{
                "decoder_input_ids": inputs_dict["decoder_input_ids"],
                "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                "encoder_outputs": encoder_outputs,
            }

            @jax.jit
            def decode_jitted(UpperCAmelCase					,    UpperCAmelCase					,    UpperCAmelCase ):
              return model.decode(
                  decoder_input_ids=lowerCAmelCase__					,    decoder_attention_mask=lowerCAmelCase__					,    encoder_outputs=lowerCAmelCase__					,    )

            with self.subTest("JIT Enabled" ):
              __snake_case :					List[Any]          =							decode_jitted(**lowerCAmelCase__ ).to_tuple()

            with self.subTest("JIT Disabled" ):
              with jax.disable_jit():
                __snake_case :					Union[str, 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     UpperCAmelCase		(			self )							-> Optional[int]:






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
          __snake_case :					str          =							model_class_name.from_pretrained("facebook/blenderbot_small-90M" )
          # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
          __snake_case :					Tuple          =							np.ones((1, 1) ) * model.config.eos_token_id
          __snake_case :					List[str]          =							model(lowerCAmelCase__ )
          self.assertIsNotNone(lowerCAmelCase__ )








'''simple docstring'''

def    a__							(							lowercase   :	Dict,   lowercase   :	Optional[Any] )						->						int:





     """simple docstring"""

     _UpperCamelCase							       =			0
     _UpperCamelCase							       =			len(lowercase ) - 1

     while left <= right:
          # avoid divided by 0 during interpolation
          if sorted_collection[left] == sorted_collection[right]:
               if sorted_collection[left] == item:
                    return left
               else:
                    return None

          _UpperCamelCase							       =			left + ((item - sorted_collection[left]) * (right - left)) // (
              sorted_collection[right] - sorted_collection[left]
          )

          # out of range check
          if point < 0 or point >= len(lowercase ):
               return None

          _UpperCamelCase							       =			sorted_collection[point]
          if current_item == item:
               return point
          else:
               if point < left:
                    _UpperCamelCase							       =			left
                    _UpperCamelCase							       =			point
               elif point > right:
                    _UpperCamelCase							       =			right
                    _UpperCamelCase							       =			point
               else:
                    if item < current_item:
                         _UpperCamelCase							       =			point - 1
                    else:
                         _UpperCamelCase							       =			point + 1
     return None







def    a__							(							lowercase   :	int,   lowercase   :	Optional[int],   lowercase   :	List[Any],   lowercase   :	List[Any] )						->						Dict:





     """simple docstring"""

     if sorted_collection[left] == sorted_collection[right]:
          if sorted_collection[left] == item:
               return left
          else:
               return None

     _UpperCamelCase							       =			left + ((item - sorted_collection[left]) * (right - left)) // (
         sorted_collection[right] - sorted_collection[left]
     )

     # out of range check
     if point < 0 or point >= len(lowercase ):
          return None

     if sorted_collection[point] == item:
          return point
     elif point < left:
          return interpolation_search_by_recursion(lowercase,   lowercase,   lowercase,   lowercase )
     elif point > right:
          return interpolation_search_by_recursion(lowercase,   lowercase,   lowercase,   lowercase )
     else:
          if sorted_collection[point] > item:
               return interpolation_search_by_recursion(
                   lowercase,   lowercase,   lowercase,   point - 1 )
          else:
               return interpolation_search_by_recursion(
                   lowercase,   lowercase,   point + 1,   lowercase )







def    a__							(							lowercase   :	Any )						->						List[Any]:





     """simple docstring"""

     if collection != sorted(lowercase ):
          raise ValueError('''Collection must be ascending sorted''' )
     return True


if __name__ == "__main__":
   import sys
   lowercase__		:     int			=							0
   if debug == 1:
      lowercase__		:     Optional[Any]			=							[10, 30, 40, 45, 50, 66, 77, 93]
      try:
         __assert_sorted(collection)
      except ValueError:
         sys.exit('Sequence must be ascending sorted to apply interpolation search')
      lowercase__		:     List[str]			=							67

   lowercase__		:     Optional[int]			=							interpolation_search(collection, target)
   if result is not None:
      print(F"""{target} found at positions: {result}""")
   else:
      print('Not found')



