


'''simple docstring'''





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, 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
 __a     =			"platform"

 import jax
 import jax.numpy as jnp

 from transformers import BlenderbotTokenizer
 from transformers.models.blenderbot.modeling_flax_blenderbot import (
     FlaxBlenderbotForConditionalGeneration,
     FlaxBlenderbotModel,
     shift_tokens_right,
 )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						)						->Optional[int]:


   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   __lowercase				:





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






         """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        _lowercase				(		self						:					int       )    ->   Any:






         """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							=     BlenderbotConfig(
             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        _lowercase				(		self						:					Union[str, Any]       )    ->   List[str]:






         """simple docstring"""







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





      def        _lowercase				(		self						:					Optional[int]					,       __lowerCamelCase						:					Any					,       __lowerCamelCase						:					List[str]					,       __lowerCamelCase						:					Tuple       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     2_0
         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        _lowercase				(		self						:					List[str]					,       __lowerCamelCase						:					Any					,       __lowerCamelCase						:					List[Any]					,       __lowerCamelCase						:					Any       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     2_0
         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   __lowercase				(  unittest.TestCase  ):
      UpperCamelCase								=       99





      def        _lowercase				(		self						:					List[str]       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     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					,       )

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






         """simple docstring"""







         UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase							=     self._get_config_and_data()
         UpperCAmelCase							=     FlaxBlenderbotForConditionalGeneration(UpperCAmelCase__       )
         UpperCAmelCase							=     lm_model(input_ids=UpperCAmelCase__       )
         UpperCAmelCase							=     (batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs["""logits"""].shape					,       UpperCAmelCase__       )





      def        _lowercase				(		self						:					str       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     BlenderbotConfig(
             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					,       )
         UpperCAmelCase							=     FlaxBlenderbotForConditionalGeneration(UpperCAmelCase__       )
         UpperCAmelCase							=     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       )
         UpperCAmelCase							=     np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 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        _lowercase				(		self						:					Union[str, Any]       )    ->   List[Any]:






         """simple docstring"""







         UpperCAmelCase							=     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       )
         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   __lowercase				(  __snake_case ,					unittest.TestCase ,					__snake_case  ):
      UpperCamelCase								=       True
      UpperCamelCase								=       (
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      UpperCamelCase								=       (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()





      def        _lowercase				(		self						:					Optional[int]       )    ->   List[Any]:






         """simple docstring"""







         UpperCAmelCase							=     FlaxBlenderbotModelTester(self       )





      def        _lowercase				(		self						:					str       )    ->   Tuple:






         """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        _lowercase				(		self						:					Tuple       )    ->   str:






         """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        _lowercase				(		self						:					Dict       )    ->   Tuple:






         """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(__lowerCamelCase						:					List[str]					,       __lowerCamelCase						:					Optional[Any]=None					,       **__lowerCamelCase						:					Union[str, Any]       ):
                  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        _lowercase				(		self						:					str       )    ->   str:






         """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(__lowerCamelCase						:					Optional[int]					,       __lowerCamelCase						:					List[Any]					,       __lowerCamelCase						:					int       ):
                  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        _lowercase				(		self						:					int       )    ->   int:






         """simple docstring"""







         for model_class_name in self.all_model_classes:
            UpperCAmelCase							=     model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""       )
            # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
            UpperCAmelCase							=     np.ones((1, 1)       ) * model.config.eos_token_id
            UpperCAmelCase							=     model(UpperCAmelCase__       )
            self.assertIsNotNone(UpperCAmelCase__       )





      @unittest.skipUnless(jax_device != """cpu"""					,       """3B test too slow on CPU."""       )
      @slow
      def        _lowercase				(		self						:					Dict       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     {"""num_beams""": 1, """early_stopping""": True, """min_length""": 1_5, """max_length""": 2_5}
         UpperCAmelCase							=     {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

         UpperCAmelCase							=     FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""					,       from_pt=UpperCAmelCase__       )
         UpperCAmelCase							=     BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""       )

         UpperCAmelCase							=     ["""Sam"""]
         UpperCAmelCase							=     tokenizer(UpperCAmelCase__					,       return_tensors="""jax"""       )

         UpperCAmelCase							=     model.generate(**UpperCAmelCase__					,       **UpperCAmelCase__       )
         UpperCAmelCase							=     """Sam is a great name. It means \"sun\" in Gaelic."""

         UpperCAmelCase							=     tokenizer.batch_decode(UpperCAmelCase__					,       **UpperCAmelCase__       )
         assert generated_txt[0].strip() == tgt_text







def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->int:
   return int((input_a, input_a).count(0   ) == 0   )
def   _UpperCamelCase				(						)						->None:
   assert and_gate(0  ,						0   ) == 0
   assert and_gate(0  ,						1   ) == 0
   assert and_gate(1  ,						0   ) == 0
   assert and_gate(1  ,						1   ) == 1


if __name__ == "__main__":
 test_and_gate()
 print(and_gate(1, 0))
 print(and_gate(0, 0))
 print(and_gate(0, 1))
 print(and_gate(1, 1))



