import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY

@is_pipeline_test
@require_torch_or_tf
class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				List[str]          =    MODEL_FOR_CAUSAL_LM_MAPPING
    SCREAMING_SNAKE_CASE:				List[str]          =    TF_MODEL_FOR_CAUSAL_LM_MAPPING




    @require_torch
    def      _a    (    self ):
       lowerCAmelCase_:							List[Any]       =	pipeline(task="text-generation"       ,						model="sshleifer/tiny-ctrl"       ,						framework="pt" )
       # Using `do_sample=False` to force deterministic output
       lowerCAmelCase_:							Tuple       =	text_generator("This is a test"       ,						do_sample=lowerCamelCase__ )
       self.assertEqual(
           lowerCamelCase__       ,						[
               {
                   "generated_text": (
                       "This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
                       " oscope. FiliFili@@"
                   )
               }
           ]       ,						)

       lowerCAmelCase_:							Union[str, Any]       =	text_generator(["This is a test", "This is a second test"] )
       self.assertEqual(
           lowerCamelCase__       ,						[
               [
                   {
                       "generated_text": (
                           "This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
                           " oscope. FiliFili@@"
                       )
                   }
               ],
               [
                   {
                       "generated_text": (
                           "This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"
                           " oscope. oscope. FiliFili@@"
                       )
                   }
               ],
           ]       ,						)

       lowerCAmelCase_:							Optional[int]       =	text_generator("This is a test"       ,						do_sample=lowerCamelCase__       ,						num_return_sequences=2       ,						return_tensors=lowerCamelCase__ )
       self.assertEqual(
           lowerCamelCase__       ,						[
               {"generated_token_ids": ANY(lowerCamelCase__ )},
               {"generated_token_ids": ANY(lowerCamelCase__ )},
           ]       ,						)
       lowerCAmelCase_:							Any       =	text_generator.model.config.eos_token_id
       lowerCAmelCase_:							Dict       =	"<pad>"
       lowerCAmelCase_:							Any       =	text_generator(
           ["This is a test", "This is a second test"]       ,						do_sample=lowerCamelCase__       ,						num_return_sequences=2       ,						batch_size=2       ,						return_tensors=lowerCamelCase__       ,						)
       self.assertEqual(
           lowerCamelCase__       ,						[
               [
                   {"generated_token_ids": ANY(lowerCamelCase__ )},
                   {"generated_token_ids": ANY(lowerCamelCase__ )},
               ],
               [
                   {"generated_token_ids": ANY(lowerCamelCase__ )},
                   {"generated_token_ids": ANY(lowerCamelCase__ )},
               ],
           ]       ,						)




    @require_tf
    def      _a    (    self ):
       lowerCAmelCase_:							int       =	pipeline(task="text-generation"       ,						model="sshleifer/tiny-ctrl"       ,						framework="tf" )

       # Using `do_sample=False` to force deterministic output
       lowerCAmelCase_:							Tuple       =	text_generator("This is a test"       ,						do_sample=lowerCamelCase__ )
       self.assertEqual(
           lowerCamelCase__       ,						[
               {
                   "generated_text": (
                       "This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
                       " please,"
                   )
               }
           ]       ,						)

       lowerCAmelCase_:							Union[str, Any]       =	text_generator(["This is a test", "This is a second test"]       ,						do_sample=lowerCamelCase__ )
       self.assertEqual(
           lowerCamelCase__       ,						[
               [
                   {
                       "generated_text": (
                           "This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
                           " please,"
                       )
                   }
               ],
               [
                   {
                       "generated_text": (
                           "This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"
                           " Cannes 閲閲Cannes Cannes Cannes 攵 please,"
                       )
                   }
               ],
           ]       ,						)




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[Any]       =	TextGenerationPipeline(model=lowerCamelCase__       ,						tokenizer=lowerCamelCase__ )
       return text_generator, ["This is a test", "Another test"]




    def      _a    (    self ):
       lowerCAmelCase_:							List[Any]       =	"Hello I believe in"
       lowerCAmelCase_:							List[Any]       =	pipeline("text-generation"       ,						model="hf-internal-testing/tiny-random-gpt2" )
       lowerCAmelCase_:							Tuple       =	text_generator(lowerCamelCase__ )
       self.assertEqual(
           lowerCamelCase__       ,						[{"generated_text": "Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"}]       ,						)

       lowerCAmelCase_:							List[str]       =	text_generator(lowerCamelCase__       ,						stop_sequence=" fe" )
       self.assertEqual(lowerCamelCase__       ,						[{"generated_text": "Hello I believe in fe"}] )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Any       =	text_generator.model
       lowerCAmelCase_:							Any       =	text_generator.tokenizer

       lowerCAmelCase_:							Tuple       =	text_generator("This is a test" )
       self.assertEqual(lowerCamelCase__       ,						[{"generated_text": ANY(lowerCamelCase__ )}] )
       self.assertTrue(outputs[0]["generated_text"].startswith("This is a test" ) )

       lowerCAmelCase_:							int       =	text_generator("This is a test"       ,						return_full_text=lowerCamelCase__ )
       self.assertEqual(lowerCamelCase__       ,						[{"generated_text": ANY(lowerCamelCase__ )}] )
       self.assertNotIn("This is a test"       ,						outputs[0]["generated_text"] )

       lowerCAmelCase_:							Dict       =	pipeline(task="text-generation"       ,						model=lowerCamelCase__       ,						tokenizer=lowerCamelCase__       ,						return_full_text=lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	text_generator("This is a test" )
       self.assertEqual(lowerCamelCase__       ,						[{"generated_text": ANY(lowerCamelCase__ )}] )
       self.assertNotIn("This is a test"       ,						outputs[0]["generated_text"] )

       lowerCAmelCase_:							int       =	text_generator("This is a test"       ,						return_full_text=lowerCamelCase__ )
       self.assertEqual(lowerCamelCase__       ,						[{"generated_text": ANY(lowerCamelCase__ )}] )
       self.assertTrue(outputs[0]["generated_text"].startswith("This is a test" ) )

       lowerCAmelCase_:							Union[str, Any]       =	text_generator(["This is great !", "Something else"]       ,						num_return_sequences=2       ,						do_sample=lowerCamelCase__ )
       self.assertEqual(
           lowerCamelCase__       ,						[
               [{"generated_text": ANY(lowerCamelCase__ )}, {"generated_text": ANY(lowerCamelCase__ )}],
               [{"generated_text": ANY(lowerCamelCase__ )}, {"generated_text": ANY(lowerCamelCase__ )}],
           ]       ,						)

       if text_generator.tokenizer.pad_token is not None:
          lowerCAmelCase_:							int       =	text_generator(
              ["This is great !", "Something else"]       ,						num_return_sequences=2       ,						batch_size=2       ,						do_sample=lowerCamelCase__ )
          self.assertEqual(
              lowerCamelCase__       ,						[
                  [{"generated_text": ANY(lowerCamelCase__ )}, {"generated_text": ANY(lowerCamelCase__ )}],
                  [{"generated_text": ANY(lowerCamelCase__ )}, {"generated_text": ANY(lowerCamelCase__ )}],
              ]       ,						)

       with self.assertRaises(lowerCamelCase__ ):
          lowerCAmelCase_:							Union[str, Any]       =	text_generator("test"       ,						return_full_text=lowerCamelCase__       ,						return_text=lowerCamelCase__ )
       with self.assertRaises(lowerCamelCase__ ):
          lowerCAmelCase_:							Any       =	text_generator("test"       ,						return_full_text=lowerCamelCase__       ,						return_tensors=lowerCamelCase__ )
       with self.assertRaises(lowerCamelCase__ ):
          lowerCAmelCase_:							Optional[Any]       =	text_generator("test"       ,						return_text=lowerCamelCase__       ,						return_tensors=lowerCamelCase__ )

       # Empty prompt is slighly special
       # it requires BOS token to exist.
       # Special case for Pegasus which will always append EOS so will
       # work even without BOS.
       if (
           text_generator.tokenizer.bos_token_id is not None
           or "Pegasus" in tokenizer.__class__.__name__
           or "Git" in model.__class__.__name__
       ):
          lowerCAmelCase_:							Optional[int]       =	text_generator("" )
          self.assertEqual(lowerCamelCase__       ,						[{"generated_text": ANY(lowerCamelCase__ )}] )
       else:
          with self.assertRaises((ValueError, AssertionError) ):
             lowerCAmelCase_:							Union[str, Any]       =	text_generator("" )

       if text_generator.framework == "tf":
          # TF generation does not support max_new_tokens, and it's impossible
          # to control long generation with only max_length without
          # fancy calculation, dismissing tests for now.
          return
       # We don't care about infinite range models.
       # They already work.
       # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
       lowerCAmelCase_:							Optional[int]       =	["RwkvForCausalLM", "XGLMForCausalLM", "GPTNeoXForCausalLM"]
       if (
           tokenizer.model_max_length < 10_000
           and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
       ):
          # Handling of large generations
          with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
             text_generator("This is a test" * 500       ,						max_new_tokens=20 )

          lowerCAmelCase_:							Union[str, Any]       =	text_generator("This is a test" * 500       ,						handle_long_generation="hole"       ,						max_new_tokens=20 )
          # Hole strategy cannot work
          with self.assertRaises(lowerCamelCase__ ):
             text_generator(
                 "This is a test" * 500       ,						handle_long_generation="hole"       ,						max_new_tokens=tokenizer.model_max_length + 10       ,						)




    @require_torch
    @require_accelerate
    @require_torch_gpu
    def      _a    (    self ):
       import torch

       # Classic `model_kwargs`
       lowerCAmelCase_:							List[str]       =	pipeline(
           model="hf-internal-testing/tiny-random-bloom"       ,						model_kwargs={"device_map": "auto", "torch_dtype": torch.bfloataa}       ,						)
       self.assertEqual(pipe.model.device       ,						torch.device(0 ) )
       self.assertEqual(pipe.model.lm_head.weight.dtype       ,						torch.bfloataa )
       lowerCAmelCase_:							List[Any]       =	pipe("This is a test" )
       self.assertEqual(
           lowerCamelCase__       ,						[
               {
                   "generated_text": (
                       "This is a test test test test test test test test test test test test test test test test"
                       " test"
                   )
               }
           ]       ,						)

       # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
       lowerCAmelCase_:							Optional[Any]       =	pipeline(model="hf-internal-testing/tiny-random-bloom"       ,						device_map="auto"       ,						torch_dtype=torch.bfloataa )
       self.assertEqual(pipe.model.device       ,						torch.device(0 ) )
       self.assertEqual(pipe.model.lm_head.weight.dtype       ,						torch.bfloataa )
       lowerCAmelCase_:							Optional[Any]       =	pipe("This is a test" )
       self.assertEqual(
           lowerCamelCase__       ,						[
               {
                   "generated_text": (
                       "This is a test test test test test test test test test test test test test test test test"
                       " test"
                   )
               }
           ]       ,						)

       # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
       lowerCAmelCase_:							Any       =	pipeline(model="hf-internal-testing/tiny-random-bloom"       ,						device_map="auto" )
       self.assertEqual(pipe.model.device       ,						torch.device(0 ) )
       self.assertEqual(pipe.model.lm_head.weight.dtype       ,						torch.floataa )
       lowerCAmelCase_:							List[Any]       =	pipe("This is a test" )
       self.assertEqual(
           lowerCamelCase__       ,						[
               {
                   "generated_text": (
                       "This is a test test test test test test test test test test test test test test test test"
                       " test"
                   )
               }
           ]       ,						)




    @require_torch
    @require_torch_gpu
    def      _a    (    self ):
       import torch

       lowerCAmelCase_:							Optional[int]       =	pipeline(model="hf-internal-testing/tiny-random-bloom"       ,						device=0       ,						torch_dtype=torch.floataa )
       pipe("This is a test" )




    @require_torch
    @require_accelerate
    @require_torch_gpu
    def      _a    (    self ):
       import torch

       lowerCAmelCase_:							str       =	pipeline(model="hf-internal-testing/tiny-random-bloom"       ,						device_map="auto"       ,						torch_dtype=torch.floataa )
       pipe("This is a test"       ,						do_sample=lowerCamelCase__       ,						top_p=0.5 )




    def      _a    (    self ):
       lowerCAmelCase_:							Dict       =	"Hello world"
       lowerCAmelCase_:							List[Any]       =	pipeline("text-generation"       ,						model="hf-internal-testing/tiny-random-gpt2" )
       if text_generator.model.framework == "tf":
          lowerCAmelCase_:							List[str]       =	logging.get_logger("transformers.generation.tf_utils" )
       else:
          lowerCAmelCase_:							str       =	logging.get_logger("transformers.generation.utils" )
       lowerCAmelCase_:							List[str]       =	"Both `max_new_tokens`"  # The beggining of the message to be checked in this test

       # Both are set by the user -> log warning
       with CaptureLogger(lowerCamelCase__ ) as cl:
          lowerCAmelCase_:							List[str]       =	text_generator(lowerCamelCase__       ,						max_length=10       ,						max_new_tokens=1 )
       self.assertIn(lowerCamelCase__       ,						cl.out )

       # The user only sets one -> no warning
       with CaptureLogger(lowerCamelCase__ ) as cl:
          lowerCAmelCase_:							Optional[int]       =	text_generator(lowerCamelCase__       ,						max_new_tokens=1 )
       self.assertNotIn(lowerCamelCase__       ,						cl.out )

       with CaptureLogger(lowerCamelCase__ ) as cl:
          lowerCAmelCase_:							List[str]       =	text_generator(lowerCamelCase__       ,						max_length=10 )
       self.assertNotIn(lowerCamelCase__       ,						cl.out )
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
 a  :      Tuple            =    """platform"""

 import jax
 import jax.numpy as jnp

 from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
     FlaxBlenderbotSmallForConditionalGeneration,
     FlaxBlenderbotSmallModel,
     shift_tokens_right,
 )
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				):
   if attention_mask is None:
      lowerCAmelCase_:							Optional[int]       =	np.where(input_ids != config.pad_token_id							,				1							,				0					)
   if decoder_attention_mask is None:
      lowerCAmelCase_:							Union[str, Any]       =	np.where(decoder_input_ids != config.pad_token_id							,				1							,				0					)
   if head_mask is None:
      lowerCAmelCase_:							Optional[int]       =	np.ones((config.encoder_layers, config.encoder_attention_heads)					)
   if decoder_head_mask is None:
      lowerCAmelCase_:							str       =	np.ones((config.decoder_layers, config.decoder_attention_heads)					)
   if cross_attn_head_mask is None:
      lowerCAmelCase_:							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 					_lowercase    :






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__=13       ,						lowerCamelCase__=7       ,						lowerCamelCase__=True       ,						lowerCamelCase__=False       ,						lowerCamelCase__=99       ,						lowerCamelCase__=16       ,						lowerCamelCase__=2       ,						lowerCamelCase__=4       ,						lowerCamelCase__=4       ,						lowerCamelCase__="gelu"       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=32       ,						lowerCamelCase__=2       ,						lowerCamelCase__=1       ,						lowerCamelCase__=0       ,						lowerCamelCase__=0.0_2       ,						):
       lowerCAmelCase_:							Union[str, Any]       =	parent
       lowerCAmelCase_:							Tuple       =	batch_size
       lowerCAmelCase_:							Any       =	seq_length
       lowerCAmelCase_:							Tuple       =	is_training
       lowerCAmelCase_:							Optional[int]       =	use_labels
       lowerCAmelCase_:							List[Any]       =	vocab_size
       lowerCAmelCase_:							str       =	hidden_size
       lowerCAmelCase_:							Union[str, Any]       =	num_hidden_layers
       lowerCAmelCase_:							List[str]       =	num_attention_heads
       lowerCAmelCase_:							Dict       =	intermediate_size
       lowerCAmelCase_:							int       =	hidden_act
       lowerCAmelCase_:							Any       =	hidden_dropout_prob
       lowerCAmelCase_:							str       =	attention_probs_dropout_prob
       lowerCAmelCase_:							Union[str, Any]       =	max_position_embeddings
       lowerCAmelCase_:							Any       =	eos_token_id
       lowerCAmelCase_:							Union[str, Any]       =	pad_token_id
       lowerCAmelCase_:							Union[str, Any]       =	bos_token_id
       lowerCAmelCase_:							Optional[int]       =	initializer_range




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

       lowerCAmelCase_:							Optional[Any]       =	shift_tokens_right(lowerCamelCase__       ,						1       ,						2 )

       lowerCAmelCase_:							Optional[int]       =	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__       ,						)
       lowerCAmelCase_:							Optional[Any]       =	prepare_blenderbot_inputs_dict(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       return config, inputs_dict




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Dict       =	self.prepare_config_and_inputs()
       return config, inputs_dict




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[Any]       =	20
       lowerCAmelCase_:							int       =	model_class_name(lowerCamelCase__ )

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

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

       lowerCAmelCase_:							List[str]       =	model.init_cache(decoder_input_ids.shape[0]       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,						dtype="i4" )

       lowerCAmelCase_:							Optional[Any]       =	jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]       ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,						)
       lowerCAmelCase_:							Optional[Any]       =	model.decode(
           decoder_input_ids[:, :-1]       ,						lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__       ,						past_key_values=lowerCamelCase__       ,						decoder_position_ids=lowerCamelCase__       ,						)

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

       lowerCAmelCase_:							Any       =	model.decode(lowerCamelCase__       ,						lowerCamelCase__ )

       lowerCAmelCase_:							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}''' )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							str       =	20
       lowerCAmelCase_:							Union[str, Any]       =	model_class_name(lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	model.encode(inputs_dict["input_ids"] )

       lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	(
           inputs_dict["decoder_input_ids"],
           inputs_dict["decoder_attention_mask"],
       )

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

       lowerCAmelCase_:							Any       =	model.init_cache(decoder_input_ids.shape[0]       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							List[Any]       =	jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]       ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,						)

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

       lowerCAmelCase_:							Union[str, Any]       =	model.decode(lowerCamelCase__       ,						lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__ )

       lowerCAmelCase_:							Optional[int]       =	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			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    99




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	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_:							Optional[int]       =	input_ids.shape[0]
       lowerCAmelCase_:							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      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							List[Any]       =	self._get_config_and_data()
       lowerCAmelCase_:							Union[str, Any]       =	FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	lm_model(input_ids=lowerCamelCase__ )
       lowerCAmelCase_:							List[str]       =	(batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs["logits"].shape       ,						lowerCamelCase__ )




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




    def      _a    (    self ):
       lowerCAmelCase_:							Tuple       =	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]       ,						dtype=np.intaa )
       lowerCAmelCase_:							Optional[Any]       =	shift_tokens_right(lowerCamelCase__       ,						1       ,						2 )
       lowerCAmelCase_:							List[Any]       =	np.equal(lowerCamelCase__       ,						1 ).astype(np.floataa ).sum()
       lowerCAmelCase_:							Any       =	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 					_lowercase    (     UpperCAmelCase__ ,  unittest.TestCase ,  UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				List[Any]          =    True
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    (
        (
            FlaxBlenderbotSmallModel,
            FlaxBlenderbotSmallForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	FlaxBlenderbotSmallModelTester(self )




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Union[str, Any]       =	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      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[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      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
             lowerCAmelCase_:							Dict       =	self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__ )
             lowerCAmelCase_:							Union[str, Any]       =	model_class(lowerCamelCase__ )

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

             with self.subTest("JIT Enabled" ):
                lowerCAmelCase_:							Optional[int]       =	encode_jitted(**lowerCamelCase__ ).to_tuple()

             with self.subTest("JIT Disabled" ):
                with jax.disable_jit():
                   lowerCAmelCase_:							int       =	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      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
             lowerCAmelCase_:							Dict       =	model_class(lowerCamelCase__ )
             lowerCAmelCase_:							Optional[int]       =	model.encode(inputs_dict["input_ids"]       ,						inputs_dict["attention_mask"] )

             lowerCAmelCase_:							Any       =	{
                 "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__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
                return model.decode(
                    decoder_input_ids=lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__       ,						encoder_outputs=lowerCamelCase__       ,						)

             with self.subTest("JIT Enabled" ):
                lowerCAmelCase_:							Dict       =	decode_jitted(**lowerCamelCase__ ).to_tuple()

             with self.subTest("JIT Disabled" ):
                with jax.disable_jit():
                   lowerCAmelCase_:							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      _a    (    self ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_:							Any       =	model_class_name.from_pretrained("facebook/blenderbot_small-90M" )
          # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
          lowerCAmelCase_:							str       =	np.ones((1, 1) ) * model.config.eos_token_id
          lowerCAmelCase_:							Optional[Any]       =	model(lowerCamelCase__ )
          self.assertIsNotNone(lowerCamelCase__ )