


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
 _SCREAMING_SNAKE_CASE      :			Optional[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_							,UpperCamelCase_							,UpperCamelCase_=None							,UpperCamelCase_=None							,UpperCamelCase_=None							,UpperCamelCase_=None							,UpperCamelCase_=None							,UpperCamelCase_=None							,):



     """simple docstring"""






     if attention_mask is None:
          snake_case					=    np.where(input_ids != config.pad_token_id							,1							,0    )
     if decoder_attention_mask is None:
          snake_case					=    np.where(decoder_input_ids != config.pad_token_id							,1							,0    )
     if head_mask is None:
          snake_case					=    np.ones((config.encoder_layers, config.encoder_attention_heads)    )
     if decoder_head_mask is None:
          snake_case					=    np.ones((config.decoder_layers, config.decoder_attention_heads)    )
     if cross_attn_head_mask is None:
          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   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case=1_3      , __snake_case=7      , __snake_case=True      , __snake_case=False      , __snake_case=9_9      , __snake_case=1_6      , __snake_case=2      , __snake_case=4      , __snake_case=4      , __snake_case="gelu"      , __snake_case=0.1      , __snake_case=0.1      , __snake_case=3_2      , __snake_case=2      , __snake_case=1      , __snake_case=0      , __snake_case=0.02      , ):
         snake_case					=    parent
         snake_case					=    batch_size
         snake_case					=    seq_length
         snake_case					=    is_training
         snake_case					=    use_labels
         snake_case					=    vocab_size
         snake_case					=    hidden_size
         snake_case					=    num_hidden_layers
         snake_case					=    num_attention_heads
         snake_case					=    intermediate_size
         snake_case					=    hidden_act
         snake_case					=    hidden_dropout_prob
         snake_case					=    attention_probs_dropout_prob
         snake_case					=    max_position_embeddings
         snake_case					=    eos_token_id
         snake_case					=    pad_token_id
         snake_case					=    bos_token_id
         snake_case					=    initializer_range
    def 		a_      (	self   ):
         snake_case					=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      , self.vocab_size   )      , 3      , self.vocab_size   )
         snake_case					=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      , dtype=np.intaa   ))      , -1   )

         snake_case					=    shift_tokens_right(__snake_case      , 1      , 2   )

         snake_case					=    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=__snake_case      , )
         snake_case					=    prepare_blenderbot_inputs_dict(__snake_case      , __snake_case      , __snake_case   )
         return config, inputs_dict
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.prepare_config_and_inputs()
         return config, inputs_dict
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    2_0
         snake_case					=    model_class_name(__snake_case   )

         snake_case					=    model.encode(inputs_dict['''input_ids''']   )

         snake_case			,  snake_case					=    (
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

         snake_case					=    model.init_cache(decoder_input_ids.shape[0]      , __snake_case      , __snake_case   )
         snake_case					=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      , dtype='''i4'''   )

         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)      , )
         snake_case					=    model.decode(
             decoder_input_ids[:, :-1]      , __snake_case      , decoder_attention_mask=__snake_case      , past_key_values=__snake_case      , decoder_position_ids=__snake_case      , )

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

         snake_case					=    model.decode(__snake_case      , __snake_case   )

         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      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    2_0
         snake_case					=    model_class_name(__snake_case   )

         snake_case					=    model.encode(inputs_dict['''input_ids''']   )

         snake_case			,  snake_case					=    (
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

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

         snake_case					=    model.init_cache(decoder_input_ids.shape[0]      , __snake_case      , __snake_case   )
         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)      , )

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

         snake_case					=    model.decode(__snake_case      , __snake_case      , decoder_attention_mask=__snake_case   )

         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   A__ (   unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		99
    def 		a_      (	self   ):
         snake_case					=    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      , )

         snake_case					=    input_ids.shape[0]
         snake_case					=    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 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case					=    self._get_config_and_data()
         snake_case					=    FlaxBlenderbotSmallForConditionalGeneration(__snake_case   )
         snake_case					=    lm_model(input_ids=__snake_case   )
         snake_case					=    (batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape      , __snake_case   )
    def 		a_      (	self   ):
         snake_case					=    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      , )
         snake_case					=    FlaxBlenderbotSmallForConditionalGeneration(__snake_case   )
         snake_case					=    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   )
         snake_case					=    np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]      , dtype=np.intaa   )
         snake_case					=    lm_model(input_ids=__snake_case      , decoder_input_ids=__snake_case   )
         snake_case					=    (*summary.shape, config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape      , __snake_case   )




    def 		a_      (	self   ):
         snake_case					=    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   )
         snake_case					=    shift_tokens_right(__snake_case      , 1      , 2   )
         snake_case					=    np.equal(__snake_case      , 1   ).astype(np.floataa   ).sum()
         snake_case					=    np.equal(__snake_case      , 1   ).astype(np.floataa   ).sum()
         self.assertEqual(shifted.shape      , input_ids.shape   )
         self.assertEqual(__snake_case      , n_pad_before - 1   )
         self.assertTrue(np.equal(shifted[:, 0]      , 2   ).all()   )






@require_flax
class   A__ (   snake_case__	, unittest.TestCase	, snake_case__     ):







    """simple docstring"""

    __magic_name__        =		True
    __magic_name__        =		(
        (
            FlaxBlenderbotSmallModel,
            FlaxBlenderbotSmallForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    __magic_name__        =		(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
    def 		a_      (	self   ):
         snake_case					=    FlaxBlenderbotSmallModelTester(self   )
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case			,  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(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case			,  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__   ):
                   snake_case					=    self._prepare_for_class(__snake_case      , __snake_case   )
                   snake_case					=    model_class(__snake_case   )

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

                   with self.subTest('''JIT Enabled'''   ):
                        snake_case					=    encode_jitted(**__snake_case   ).to_tuple()

                   with self.subTest('''JIT Disabled'''   ):
                        with jax.disable_jit():
                             snake_case					=    encode_jitted(**__snake_case   ).to_tuple()

                   self.assertEqual(len(__snake_case   )      , len(__snake_case   )   )
                   for jitted_output, output in zip(__snake_case      , __snake_case   ):
                        self.assertEqual(jitted_output.shape      , output.shape   )
    def 		a_      (	self   ):
         snake_case			,  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__   ):
                   snake_case					=    model_class(__snake_case   )
                   snake_case					=    model.encode(inputs_dict['''input_ids''']      , inputs_dict['''attention_mask''']   )

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

                   with self.subTest('''JIT Enabled'''   ):
                        snake_case					=    decode_jitted(**__snake_case   ).to_tuple()

                   with self.subTest('''JIT Disabled'''   ):
                        with jax.disable_jit():
                             snake_case					=    decode_jitted(**__snake_case   ).to_tuple()

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




    @slow
    def 		a_      (	self   ):
         for model_class_name in self.all_model_classes:
              snake_case					=    model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''   )
              # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
              snake_case					=    np.ones((1, 1)   ) * model.config.eos_token_id
              snake_case					=    model(__snake_case   )
              self.assertIsNotNone(__snake_case   )








import math

class   A__ :







    """simple docstring"""

    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    0.0
         snake_case					=    0.0
         for i in range(len(__snake_case   )   ):
              da += math.pow((sample[i] - weights[0][i])      , 2   )
              da += math.pow((sample[i] - weights[1][i])      , 2   )
              return 0 if da > da else 1
         return 0




    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         for i in range(len(__snake_case   )   ):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
         return weights






def       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

     # weight initialization ( n, C )
     snake_case					=    [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

     # training
     snake_case					=    SelfOrganizingMap()
     snake_case					=    3
     snake_case					=    0.5

     for _ in range(UpperCamelCase_    ):
          for j in range(len(UpperCamelCase_    )    ):
               # training sample
               snake_case					=    training_samples[j]

               # Compute the winning vector
               snake_case					=    self_organizing_map.get_winner(UpperCamelCase_							,UpperCamelCase_    )

               # Update the winning vector
               snake_case					=    self_organizing_map.update(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )

    # classify test sample
     snake_case					=    [0, 0, 0, 1]
     snake_case					=    self_organizing_map.get_winner(UpperCamelCase_							,UpperCamelCase_    )

     # results
     print(F'''Clusters that the test sample belongs to : {winner}'''    )
     print(F'''Weights that have been trained : {weights}'''    )


# running the main() function
if __name__ == "__main__":
 main()




