






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__	:			str			   = '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   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_=None		,   lowerCamelCase_=None		,   lowerCamelCase_=None		,   lowerCamelCase_=None		,   lowerCamelCase_=None		,   lowerCamelCase_=None		,   ):






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

				"""simple docstring"""




				def __init__(     self						:    List[str],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Tuple=13,  lowerCamelCase						:    Optional[Any]=7,  lowerCamelCase						:    Optional[int]=True,  lowerCamelCase						:    str=False,  lowerCamelCase						:    int=99,  lowerCamelCase						:    Optional[Any]=16,  lowerCamelCase						:    int=2,  lowerCamelCase						:    Union[str, Any]=4,  lowerCamelCase						:    List[Any]=4,  lowerCamelCase						:    str="gelu",  lowerCamelCase						:    str=0.1,  lowerCamelCase						:    Any=0.1,  lowerCamelCase						:    Union[str, Any]=32,  lowerCamelCase						:    Optional[int]=2,  lowerCamelCase						:    List[Any]=1,  lowerCamelCase						:    Tuple=0,  lowerCamelCase						:    List[str]=0.02,  ):

								'''simple docstring'''
								lowercase__    =			parent
								lowercase__    =			batch_size
								lowercase__    =			seq_length
								lowercase__    =			is_training
								lowercase__    =			use_labels
								lowercase__    =			vocab_size
								lowercase__    =			hidden_size
								lowercase__    =			num_hidden_layers
								lowercase__    =			num_attention_heads
								lowercase__    =			intermediate_size
								lowercase__    =			hidden_act
								lowercase__    =			hidden_dropout_prob
								lowercase__    =			attention_probs_dropout_prob
								lowercase__    =			max_position_embeddings
								lowercase__    =			eos_token_id
								lowercase__    =			pad_token_id
								lowercase__    =			bos_token_id
								lowercase__    =			initializer_range




				def 				lowercase__   (     self						:    Optional[Any]  ):

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

								lowercase__    =			shift_tokens_right(lowerCamelCase,  1,  2  )

								lowercase__    =			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=lowerCamelCase,  )
								lowercase__    =			prepare_blenderbot_inputs_dict(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )
								return config, inputs_dict




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			self.prepare_config_and_inputs()
								return config, inputs_dict




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			20
								lowercase__    =			model_class_name(lowerCamelCase  )

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

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

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

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

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

								lowercase__    =			model.decode(lowerCamelCase,  lowerCamelCase  )

								lowercase__    =			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						:    Union[str, Any],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Any  ):

								'''simple docstring'''
								lowercase__    =			20
								lowercase__    =			model_class_name(lowerCamelCase  )

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

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

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

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

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

								lowercase__    =			model.decode(lowerCamelCase,  lowerCamelCase,  decoder_attention_mask=lowerCamelCase  )

								lowercase__    =			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	):

				"""simple docstring"""




				lowercase__											=       99




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			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,  )

								lowercase__    =			input_ids.shape[0]
								lowercase__    =			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 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__ ,						lowercase__    =			self._get_config_and_data()
								lowercase__    =			FlaxBlenderbotForConditionalGeneration(lowerCamelCase  )
								lowercase__    =			lm_model(input_ids=lowerCamelCase  )
								lowercase__    =			(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape,  lowerCamelCase  )




				def 				lowercase__   (     self						:    str  ):

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




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],  dtype=np.intaa  )
								lowercase__    =			shift_tokens_right(lowerCamelCase,  1,  2  )
								lowercase__    =			np.equal(lowerCamelCase,  1  ).astype(np.floataa  ).sum()
								lowercase__    =			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       _UpperCAmelCase					(       A__		,unittest.TestCase		,A__	):

				"""simple docstring"""




				lowercase__											=       True
				lowercase__											=       (
				    (
				        FlaxBlenderbotModel,
				        FlaxBlenderbotForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				lowercase__											=       (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			FlaxBlenderbotModelTester(self  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			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 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			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 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__  ):
																lowercase__    =			self._prepare_for_class(lowerCamelCase,  lowerCamelCase  )
																lowercase__    =			model_class(lowerCamelCase  )

																@jax.jit
																def encode_jitted(lowerCamelCase						:    Optional[int],  lowerCamelCase						:    Optional[Any]=None,  **lowerCamelCase						:    Any  ):
																				return model.encode(input_ids=lowerCamelCase,  attention_mask=lowerCamelCase  )

																with self.subTest('''JIT Enabled'''  ):
																				lowercase__    =			encode_jitted(**lowerCamelCase  ).to_tuple()

																with self.subTest('''JIT Disabled'''  ):
																				with jax.disable_jit():
																								lowercase__    =			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 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()

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

																lowercase__    =			{
																    '''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						:    Any,  lowerCamelCase						:    Optional[int]  ):
																				return model.decode(
																				    decoder_input_ids=lowerCamelCase,  decoder_attention_mask=lowerCamelCase,  encoder_outputs=lowerCamelCase,  )

																with self.subTest('''JIT Enabled'''  ):
																				lowercase__    =			decode_jitted(**lowerCamelCase  ).to_tuple()

																with self.subTest('''JIT Disabled'''  ):
																				with jax.disable_jit():
																								lowercase__    =			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 				lowercase__   (     self						:    Optional[Any]  ):

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




				@unittest.skipUnless(jax_device != '''cpu''',  '''3B test too slow on CPU.'''  )
				@slow
				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								lowercase__    =			{'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
								lowercase__    =			{'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

								lowercase__    =			FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B''',  from_pt=lowerCamelCase  )
								lowercase__    =			BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''  )

								lowercase__    =			['''Sam''']
								lowercase__    =			tokenizer(lowerCamelCase,  return_tensors='''jax'''  )

								lowercase__    =			model.generate(**lowerCamelCase,  **lowerCamelCase  )
								lowercase__    =			'''Sam is a great name. It means "sun" in Gaelic.'''

								lowercase__    =			tokenizer.batch_decode(lowerCamelCase,  **lowerCamelCase  )
								assert generated_txt[0].strip() == tgt_text












from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       ["""image_processor""", """tokenizer"""]
				lowercase__											=       """BlipImageProcessor"""
				lowercase__											=       ("""BertTokenizer""", """BertTokenizerFast""")




				def __init__(     self						:    Dict,  lowerCamelCase						:    Dict,  lowerCamelCase						:    str  ):

								'''simple docstring'''
								lowercase__    =			False
								super().__init__(lowerCamelCase,  lowerCamelCase  )
								lowercase__    =			self.image_processor




				def __call__(     self						:    int,  lowerCamelCase						:    ImageInput = None,  lowerCamelCase						:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Union[bool, str, PaddingStrategy] = False,  lowerCamelCase						:    Union[bool, str, TruncationStrategy] = None,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    int = 0,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    Optional[bool] = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Optional[Union[str, TensorType]] = None,  **lowerCamelCase						:    Any,  ):

								'''simple docstring'''
								if images is None and text is None:
												raise ValueError('''You have to specify either images or text.'''  )

								# Get only text
								if images is None:
												lowercase__    =			self.tokenizer
												lowercase__    =			self.tokenizer(
												    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
												return text_encoding

								# add pixel_values
								lowercase__    =			self.image_processor(lowerCamelCase,  return_tensors=lowerCamelCase  )

								if text is not None:
												lowercase__    =			self.tokenizer(
												    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
								else:
												lowercase__    =			None

								if text_encoding is not None:
												encoding_image_processor.update(lowerCamelCase  )

								return encoding_image_processor




				def 				lowercase__   (     self						:    Tuple,  *lowerCamelCase						:    Union[str, Any],  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[str],  *lowerCamelCase						:    int,  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase  )




				@property
				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			self.tokenizer.model_input_names
								lowercase__    =			self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )




