



'''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
 lowerCAmelCase	:		List[Any]				=   '''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   __lowerCAmelCase					(							lowerCamelCase   :						str				,		lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						int=None				,		lowerCamelCase   :						List[str]=None				,		lowerCamelCase   :						List[Any]=None				,		lowerCamelCase   :						int=None				,		lowerCamelCase   :						Optional[Any]=None				,		lowerCamelCase   :						Any=None				,		):






 '''simple docstring'''




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



     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			,				)     ->			Optional[Any]:
      __lowerCAmelCase												=  parent
      __lowerCAmelCase												=  batch_size
      __lowerCAmelCase												=  seq_length
      __lowerCAmelCase												=  is_training
      __lowerCAmelCase												=  use_labels
      __lowerCAmelCase												=  vocab_size
      __lowerCAmelCase												=  hidden_size
      __lowerCAmelCase												=  num_hidden_layers
      __lowerCAmelCase												=  num_attention_heads
      __lowerCAmelCase												=  intermediate_size
      __lowerCAmelCase												=  hidden_act
      __lowerCAmelCase												=  hidden_dropout_prob
      __lowerCAmelCase												=  attention_probs_dropout_prob
      __lowerCAmelCase												=  max_position_embeddings
      __lowerCAmelCase												=  eos_token_id
      __lowerCAmelCase												=  pad_token_id
      __lowerCAmelCase												=  bos_token_id
      __lowerCAmelCase												=  initializer_range



     def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
      __lowerCAmelCase												=  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,				self.vocab_size     )			,				3			,				self.vocab_size     )
      __lowerCAmelCase												=  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,				dtype=np.intaa     ))			,				-1     )

      __lowerCAmelCase												=  shift_tokens_right(UpperCamelCase			,				1			,				2     )

      __lowerCAmelCase												=  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			,				)
      __lowerCAmelCase												=  prepare_blenderbot_inputs_dict(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
      return config, inputs_dict



     def        UpperCAmelCase_ (    self     )     ->			Dict:
      __lowerCAmelCase							,       __lowerCAmelCase												=  self.prepare_config_and_inputs()
      return config, inputs_dict



     def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			List[Any]:
      __lowerCAmelCase												=  20
      __lowerCAmelCase												=  model_class_name(UpperCamelCase     )

      __lowerCAmelCase												=  model.encode(inputs_dict["input_ids"]     )

      __lowerCAmelCase							,       __lowerCAmelCase												=  (
          inputs_dict["decoder_input_ids"],
          inputs_dict["decoder_attention_mask"],
      )

      __lowerCAmelCase												=  model.init_cache(decoder_input_ids.shape[0]			,				UpperCamelCase			,				UpperCamelCase     )
      __lowerCAmelCase												=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,				dtype="i4"     )

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

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

      __lowerCAmelCase												=  model.decode(UpperCamelCase			,				UpperCamelCase     )

      __lowerCAmelCase												=  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     )     ->			List[str]:
      __lowerCAmelCase												=  20
      __lowerCAmelCase												=  model_class_name(UpperCamelCase     )

      __lowerCAmelCase												=  model.encode(inputs_dict["input_ids"]     )

      __lowerCAmelCase							,       __lowerCAmelCase												=  (
          inputs_dict["decoder_input_ids"],
          inputs_dict["decoder_attention_mask"],
      )

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

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

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

      __lowerCAmelCase												=  model.decode(UpperCamelCase			,				UpperCamelCase			,				decoder_attention_mask=UpperCamelCase     )

      __lowerCAmelCase												=  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 ):
     a      :     Optional[Any]        =							9_9



     def        UpperCAmelCase_ (    self     )     ->			List[str]:
      __lowerCAmelCase												=  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			,				)

      __lowerCAmelCase												=  input_ids.shape[0]
      __lowerCAmelCase												=  BlenderbotConfig(
          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     )     ->			Any:
      __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  self._get_config_and_data()
      __lowerCAmelCase												=  FlaxBlenderbotForConditionalGeneration(UpperCamelCase     )
      __lowerCAmelCase												=  lm_model(input_ids=UpperCamelCase     )
      __lowerCAmelCase												=  (batch_size, input_ids.shape[1], config.vocab_size)
      self.assertEqual(outputs["logits"].shape			,				UpperCamelCase     )



     def        UpperCAmelCase_ (    self     )     ->			int:
      __lowerCAmelCase												=  BlenderbotConfig(
          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			,				)
      __lowerCAmelCase												=  FlaxBlenderbotForConditionalGeneration(UpperCamelCase     )
      __lowerCAmelCase												=  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,				dtype=np.intaa     )
      __lowerCAmelCase												=  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,				dtype=np.intaa     )
      __lowerCAmelCase												=  lm_model(input_ids=UpperCamelCase			,				decoder_input_ids=UpperCamelCase     )
      __lowerCAmelCase												=  (*summary.shape, config.vocab_size)
      self.assertEqual(outputs["logits"].shape			,				UpperCamelCase     )



     def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
      __lowerCAmelCase												=  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,				dtype=np.intaa     )
      __lowerCAmelCase												=  shift_tokens_right(UpperCamelCase			,				1			,				2     )
      __lowerCAmelCase												=  np.equal(UpperCamelCase			,				1     ).astype(np.floataa     ).sum()
      __lowerCAmelCase												=  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 					UpperCAmelCase__	(		UpperCamelCase__							,       unittest.TestCase							,       UpperCamelCase__ ):
     a      :     Tuple        =							True
     a      :     Tuple        =							(
         (
             FlaxBlenderbotModel,
             FlaxBlenderbotForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     a      :     Optional[Any]        =							(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



     def        UpperCAmelCase_ (    self     )     ->			Tuple:
      __lowerCAmelCase												=  FlaxBlenderbotModelTester(self     )



     def        UpperCAmelCase_ (    self     )     ->			str:
      __lowerCAmelCase							,       __lowerCAmelCase												=  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        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
      __lowerCAmelCase							,       __lowerCAmelCase												=  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        UpperCAmelCase_ (    self     )     ->			Tuple:
      __lowerCAmelCase							,       __lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__     ):
        __lowerCAmelCase												=  self._prepare_for_class(UpperCamelCase			,				UpperCamelCase     )
        __lowerCAmelCase												=  model_class(UpperCamelCase     )

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

        with self.subTest("JIT Enabled"     ):
         __lowerCAmelCase												=  encode_jitted(**UpperCamelCase     ).to_tuple()

        with self.subTest("JIT Disabled"     ):
         with jax.disable_jit():
          __lowerCAmelCase												=  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        UpperCAmelCase_ (    self     )     ->			Any:
      __lowerCAmelCase							,       __lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__     ):
        __lowerCAmelCase												=  model_class(UpperCamelCase     )
        __lowerCAmelCase												=  model.encode(inputs_dict["input_ids"]			,				inputs_dict["attention_mask"]     )

        __lowerCAmelCase												=  {
            "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=UpperCamelCase			,				decoder_attention_mask=UpperCamelCase			,				encoder_outputs=UpperCamelCase			,				)

        with self.subTest("JIT Enabled"     ):
         __lowerCAmelCase												=  decode_jitted(**UpperCamelCase     ).to_tuple()

        with self.subTest("JIT Disabled"     ):
         with jax.disable_jit():
          __lowerCAmelCase												=  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        UpperCAmelCase_ (    self     )     ->			Dict:
      for model_class_name in self.all_model_classes:
       __lowerCAmelCase												=  model_class_name.from_pretrained("facebook/blenderbot-400M-distill"     )
       # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
       __lowerCAmelCase												=  np.ones((1, 1)     ) * model.config.eos_token_id
       __lowerCAmelCase												=  model(UpperCamelCase     )
       self.assertIsNotNone(UpperCamelCase     )



     @unittest.skipUnless(jax_device != "cpu"			,				"3B test too slow on CPU."     )
     @slow
     def        UpperCAmelCase_ (    self     )     ->			Tuple:
      __lowerCAmelCase												=  {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
      __lowerCAmelCase												=  {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

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

      __lowerCAmelCase												=  ["Sam"]
      __lowerCAmelCase												=  tokenizer(UpperCamelCase			,				return_tensors="jax"     )

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

      __lowerCAmelCase												=  tokenizer.batch_decode(UpperCamelCase			,				**UpperCamelCase     )
      assert generated_txt[0].strip() == tgt_text




'''simple docstring'''



import os
import sys
import unittest


lowerCAmelCase	:		str				=   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowerCAmelCase	:		Tuple				=   os.path.join('''tests''', '''models''', '''bert''', '''test_modeling_bert.py''')
lowerCAmelCase	:		Tuple				=   os.path.join('''tests''', '''models''', '''blip''', '''test_modeling_blip.py''')






class 					UpperCAmelCase__	(		unittest.TestCase ):



					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  get_test_to_tester_mapping(UpperCamelCase     )
						__lowerCAmelCase												=  get_test_to_tester_mapping(UpperCamelCase     )

						__lowerCAmelCase												=  {"BertModelTest": "BertModelTester"}

						__lowerCAmelCase												=  {
						    "BlipModelTest": "BlipModelTester",
						    "BlipTextImageModelTest": "BlipTextImageModelsModelTester",
						    "BlipTextModelTest": "BlipTextModelTester",
						    "BlipTextRetrievalModelTest": "BlipTextRetrievalModelTester",
						    "BlipVQAModelTest": "BlipVQAModelTester",
						    "BlipVisionModelTest": "BlipVisionModelTester",
						}

						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )
						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  get_model_to_test_mapping(UpperCamelCase     )
						__lowerCAmelCase												=  get_model_to_test_mapping(UpperCamelCase     )

						__lowerCAmelCase												=  {
						    "BertForMaskedLM": ["BertModelTest"],
						    "BertForMultipleChoice": ["BertModelTest"],
						    "BertForNextSentencePrediction": ["BertModelTest"],
						    "BertForPreTraining": ["BertModelTest"],
						    "BertForQuestionAnswering": ["BertModelTest"],
						    "BertForSequenceClassification": ["BertModelTest"],
						    "BertForTokenClassification": ["BertModelTest"],
						    "BertLMHeadModel": ["BertModelTest"],
						    "BertModel": ["BertModelTest"],
						}

						__lowerCAmelCase												=  {
						    "BlipForConditionalGeneration": ["BlipTextImageModelTest"],
						    "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTest"],
						    "BlipForQuestionAnswering": ["BlipVQAModelTest"],
						    "BlipModel": ["BlipModelTest"],
						    "BlipTextModel": ["BlipTextModelTest"],
						    "BlipVisionModel": ["BlipVisionModelTest"],
						}

						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )
						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  get_model_to_tester_mapping(UpperCamelCase     )
						__lowerCAmelCase												=  get_model_to_tester_mapping(UpperCamelCase     )

						__lowerCAmelCase												=  {
						    "BertForMaskedLM": ["BertModelTester"],
						    "BertForMultipleChoice": ["BertModelTester"],
						    "BertForNextSentencePrediction": ["BertModelTester"],
						    "BertForPreTraining": ["BertModelTester"],
						    "BertForQuestionAnswering": ["BertModelTester"],
						    "BertForSequenceClassification": ["BertModelTester"],
						    "BertForTokenClassification": ["BertModelTester"],
						    "BertLMHeadModel": ["BertModelTester"],
						    "BertModel": ["BertModelTester"],
						}

						__lowerCAmelCase												=  {
						    "BlipForConditionalGeneration": ["BlipTextImageModelsModelTester"],
						    "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTester"],
						    "BlipForQuestionAnswering": ["BlipVQAModelTester"],
						    "BlipModel": ["BlipModelTester"],
						    "BlipTextModel": ["BlipTextModelTester"],
						    "BlipVisionModel": ["BlipVisionModelTester"],
						}

						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )
						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )