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






  def __init__(self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_=13 ,					SCREAMING_SNAKE_CASE_=7 ,					SCREAMING_SNAKE_CASE_=True ,					SCREAMING_SNAKE_CASE_=False ,					SCREAMING_SNAKE_CASE_=99 ,					SCREAMING_SNAKE_CASE_=16 ,					SCREAMING_SNAKE_CASE_=2 ,					SCREAMING_SNAKE_CASE_=4 ,					SCREAMING_SNAKE_CASE_=4 ,					SCREAMING_SNAKE_CASE_="gelu" ,					SCREAMING_SNAKE_CASE_=0.1 ,					SCREAMING_SNAKE_CASE_=0.1 ,					SCREAMING_SNAKE_CASE_=32 ,					SCREAMING_SNAKE_CASE_=2 ,					SCREAMING_SNAKE_CASE_=1 ,					SCREAMING_SNAKE_CASE_=0 ,					SCREAMING_SNAKE_CASE_=0.02 ,					)		->   List[Any]:





       '''simple docstring'''





       UpperCamelCase__					    =   parent
       UpperCamelCase__					    =   batch_size
       UpperCamelCase__					    =   seq_length
       UpperCamelCase__					    =   is_training
       UpperCamelCase__					    =   use_labels
       UpperCamelCase__					    =   vocab_size
       UpperCamelCase__					    =   hidden_size
       UpperCamelCase__					    =   num_hidden_layers
       UpperCamelCase__					    =   num_attention_heads
       UpperCamelCase__					    =   intermediate_size
       UpperCamelCase__					    =   hidden_act
       UpperCamelCase__					    =   hidden_dropout_prob
       UpperCamelCase__					    =   attention_probs_dropout_prob
       UpperCamelCase__					    =   max_position_embeddings
       UpperCamelCase__					    =   eos_token_id
       UpperCamelCase__					    =   pad_token_id
       UpperCamelCase__					    =   bos_token_id
       UpperCamelCase__					    =   initializer_range






  def 		_a       (self						)		->   str:





       '''simple docstring'''





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

       UpperCamelCase__					    =   shift_tokens_right(__UpperCamelCase ,					1 ,					2						)

       UpperCamelCase__					    =   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=__UpperCamelCase ,					)
       UpperCamelCase__					    =   prepare_blenderbot_inputs_dict(__UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase						)
       return config, inputs_dict






  def 		_a       (self						)		->   Optional[int]:





       '''simple docstring'''





       UpperCamelCase__    ,							UpperCamelCase__					    =   self.prepare_config_and_inputs()
       return config, inputs_dict






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_						)		->   List[str]:





       '''simple docstring'''





       UpperCamelCase__					    =   20
       UpperCamelCase__					    =   model_class_name(__UpperCamelCase						)

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

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

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

       UpperCamelCase__					    =   jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :] ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,					)
       UpperCamelCase__					    =   model.decode(
           decoder_input_ids[:, :-1] ,					__UpperCamelCase ,					decoder_attention_mask=__UpperCamelCase ,					past_key_values=__UpperCamelCase ,					decoder_position_ids=__UpperCamelCase ,					)

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

       UpperCamelCase__					    =   model.decode(__UpperCamelCase ,					__UpperCamelCase						)

       UpperCamelCase__					    =   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 ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_						)		->   Dict:





       '''simple docstring'''





       UpperCamelCase__					    =   20
       UpperCamelCase__					    =   model_class_name(__UpperCamelCase						)

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

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

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

       UpperCamelCase__					    =   model.init_cache(decoder_input_ids.shape[0] ,					__UpperCamelCase ,					__UpperCamelCase						)
       UpperCamelCase__					    =   jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :] ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,					)

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

       UpperCamelCase__					    =   model.decode(__UpperCamelCase ,					__UpperCamelCase ,					decoder_attention_mask=__UpperCamelCase						)

       UpperCamelCase__					    =   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				):
  SCREAMING_SNAKE_CASE_						:						Optional[Any]    =99






  def 		_a       (self						)		->   int:





       '''simple docstring'''





       UpperCamelCase__					    =   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 ,					)

       UpperCamelCase__					    =   input_ids.shape[0]
       UpperCamelCase__					    =   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 		_a       (self						)		->   Any:





       '''simple docstring'''





       UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__					    =   self._get_config_and_data()
       UpperCamelCase__					    =   FlaxBlenderbotSmallForConditionalGeneration(__UpperCamelCase						)
       UpperCamelCase__					    =   lm_model(input_ids=__UpperCamelCase						)
       UpperCamelCase__					    =   (batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape ,					__UpperCamelCase						)






  def 		_a       (self						)		->   Dict:





       '''simple docstring'''





       UpperCamelCase__					    =   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 ,					)
       UpperCamelCase__					    =   FlaxBlenderbotSmallForConditionalGeneration(__UpperCamelCase						)
       UpperCamelCase__					    =   np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,					dtype=np.intaa						)
       UpperCamelCase__					    =   np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,					dtype=np.intaa						)
       UpperCamelCase__					    =   lm_model(input_ids=__UpperCamelCase ,					decoder_input_ids=__UpperCamelCase						)
       UpperCamelCase__					    =   (*summary.shape, config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape ,					__UpperCamelCase						)






  def 		_a       (self						)		->   Tuple:





       '''simple docstring'''





       UpperCamelCase__					    =   np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] ,					dtype=np.intaa						)
       UpperCamelCase__					    =   shift_tokens_right(__UpperCamelCase ,					1 ,					2						)
       UpperCamelCase__					    =   np.equal(__UpperCamelCase ,					1						).astype(np.floataa						).sum()
       UpperCamelCase__					    =   np.equal(__UpperCamelCase ,					1						).astype(np.floataa						).sum()
       self.assertEqual(shifted.shape ,					input_ids.shape						)
       self.assertEqual(__UpperCamelCase ,					n_pad_before - 1						)
       self.assertTrue(np.equal(shifted[:, 0] ,					2						).all()						)



@require_flax
class      _A (     a_						,			unittest.TestCase						,			a_				):
  SCREAMING_SNAKE_CASE_						:						Any    =True
  SCREAMING_SNAKE_CASE_						:						Union[str, Any]    =(
      (
          FlaxBlenderbotSmallModel,
          FlaxBlenderbotSmallForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  SCREAMING_SNAKE_CASE_						:						int    =(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()






  def 		_a       (self						)		->   int:





       '''simple docstring'''





       UpperCamelCase__					    =   FlaxBlenderbotSmallModelTester(self						)






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





       '''simple docstring'''





       UpperCamelCase__    ,							UpperCamelCase__					    =   self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(__UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase						)






  def 		_a       (self						)		->   Optional[int]:





       '''simple docstring'''





       UpperCamelCase__    ,							UpperCamelCase__					    =   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(__UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase						)






  def 		_a       (self						)		->   Dict:





       '''simple docstring'''





       UpperCamelCase__    ,							UpperCamelCase__					    =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__						):
                 UpperCamelCase__					    =   self._prepare_for_class(__UpperCamelCase ,					__UpperCamelCase						)
                 UpperCamelCase__					    =   model_class(__UpperCamelCase						)

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

                 with self.subTest('''JIT Enabled'''						):
                      UpperCamelCase__					    =   encode_jitted(**__UpperCamelCase						).to_tuple()

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

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






  def 		_a       (self						)		->   Any:





       '''simple docstring'''





       UpperCamelCase__    ,							UpperCamelCase__					    =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__						):
                 UpperCamelCase__					    =   model_class(__UpperCamelCase						)
                 UpperCamelCase__					    =   model.encode(inputs_dict['''input_ids'''] ,					inputs_dict['''attention_mask''']						)

                 UpperCamelCase__					    =   {
                     '''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(SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_						):
                      return model.decode(
                          decoder_input_ids=__UpperCamelCase ,					decoder_attention_mask=__UpperCamelCase ,					encoder_outputs=__UpperCamelCase ,					)

                 with self.subTest('''JIT Enabled'''						):
                      UpperCamelCase__					    =   decode_jitted(**__UpperCamelCase						).to_tuple()

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

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






  @slow
  def 		_a       (self						)		->   str:





       '''simple docstring'''





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











import itertools
import string
from collections.abc import Generator, Iterable





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       iter(__snake_case							)
     while True:
          __lowerCAmelCase             =       tuple(itertools.islice(__snake_case       ,			__snake_case							)							)
          if not chunk:
               return
          yield chunk





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       "".join([c.upper() for c in dirty if c in string.ascii_letters]							)
     __lowerCAmelCase             =       ""

     if len(__snake_case							) < 2:
          return dirty

     for i in range(len(__snake_case							) - 1							):
          clean += dirty[i]

          if dirty[i] == dirty[i + 1]:
               clean += "X"

     clean += dirty[-1]

     if len(__snake_case							) & 1:
          clean += "X"

     return clean





def    __lowerCAmelCase   (    __snake_case							):
     # I and J are used interchangeably to allow
     # us to use a 5x5 table (25 letters)
     __lowerCAmelCase             =       "ABCDEFGHIKLMNOPQRSTUVWXYZ"
     # we're using a list instead of a '2d' array because it makes the math
     # for setting up the table and doing the actual encoding/decoding simpler
     __lowerCAmelCase             =       []

     # copy key chars into the table if they are in `alphabet` ignoring duplicates
     for char in key.upper():
          if char not in table and char in alphabet:
               table.append(__snake_case							)

    # fill the rest of the table in with the remaining alphabet chars
     for char in alphabet:
          if char not in table:
               table.append(__snake_case							)

     return table





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       generate_table(__snake_case							)
     __lowerCAmelCase             =       prepare_input(__snake_case							)
     __lowerCAmelCase             =       ""

     # https://en.wikipedia.org/wiki/Playfair_cipher#Description
     for chara, chara in chunker(__snake_case       ,			2							):
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)

          if rowa == rowa:
               ciphertext += table[rowa * 5 + (cola + 1) % 5]
               ciphertext += table[rowa * 5 + (cola + 1) % 5]
          elif cola == cola:
               ciphertext += table[((rowa + 1) % 5) * 5 + cola]
               ciphertext += table[((rowa + 1) % 5) * 5 + cola]
          else:  # rectangle
               ciphertext += table[rowa * 5 + cola]
               ciphertext += table[rowa * 5 + cola]

     return ciphertext





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       generate_table(__snake_case							)
     __lowerCAmelCase             =       ""

     # https://en.wikipedia.org/wiki/Playfair_cipher#Description
     for chara, chara in chunker(__snake_case       ,			2							):
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)

          if rowa == rowa:
               plaintext += table[rowa * 5 + (cola - 1) % 5]
               plaintext += table[rowa * 5 + (cola - 1) % 5]
          elif cola == cola:
               plaintext += table[((rowa - 1) % 5) * 5 + cola]
               plaintext += table[((rowa - 1) % 5) * 5 + cola]
          else:  # rectangle
               plaintext += table[rowa * 5 + cola]
               plaintext += table[rowa * 5 + cola]

     return plaintext






