






"""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       :	Tuple             = '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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=None ,     lowerCamelCase_=None ,     lowerCamelCase_=None ,     lowerCamelCase_=None ,     lowerCamelCase_=None ,     lowerCamelCase_=None ,     ):
					if attention_mask is None:
										a__          =  np.where(input_ids != config.pad_token_id ,     1 ,     0)
					if decoder_attention_mask is None:
										a__          =  np.where(decoder_input_ids != config.pad_token_id ,     1 ,     0)
					if head_mask is None:
										a__          =  np.ones((config.encoder_layers, config.encoder_attention_heads))
					if decoder_head_mask is None:
										a__          =  np.ones((config.decoder_layers, config.decoder_attention_heads))
					if cross_attn_head_mask is None:
										a__          =  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        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     List[Any]				,		__A:     Union[str, Any]				,		__A:     List[Any]=13				,		__A:     List[str]=7				,		__A:     str=True				,		__A:     str=False				,		__A:     Union[str, Any]=99				,		__A:     int=16				,		__A:     List[Any]=2				,		__A:     Tuple=4				,		__A:     Union[str, Any]=4				,		__A:     Dict="gelu"				,		__A:     Optional[Any]=0.1				,		__A:     Optional[Any]=0.1				,		__A:     Union[str, Any]=32				,		__A:     int=2				,		__A:     Optional[Any]=1				,		__A:     Optional[Any]=0				,		__A:     Optional[int]=0.0_2				,		):

												'''simple docstring'''

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




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

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

												a__          =  shift_tokens_right(lowerCAmelCase_				,		1				,		2    )

												a__          =  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_				,		)
												a__          =  prepare_blenderbot_inputs_dict(lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_    )
												return config, inputs_dict




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     List[str]				,		__A:     Union[str, Any]				,		__A:     List[Any]				,		__A:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  20
												a__          =  model_class_name(lowerCAmelCase_    )

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

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

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

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

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

												a__          =  model.decode(lowerCAmelCase_				,		lowerCAmelCase_    )

												a__          =  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[Any]				,		__A:     Any				,		__A:     str				,		__A:     int    ):

												'''simple docstring'''

												a__          =  20
												a__          =  model_class_name(lowerCAmelCase_    )

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

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

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

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

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

												a__          =  model.decode(lowerCAmelCase_				,		lowerCAmelCase_				,		decoder_attention_mask=lowerCAmelCase_    )

												a__          =  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        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =99




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  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				,		)

												a__          =  input_ids.shape[0]
												a__          =  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:     Union[str, Any]    ):

												'''simple docstring'''

												a__       ,a__       ,a__          =  self._get_config_and_data()
												a__          =  FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_    )
												a__          =  lm_model(input_ids=lowerCAmelCase_    )
												a__          =  (batch_size, input_ids.shape[1], config.vocab_size)
												self.assertEqual(outputs['''logits'''].shape				,		lowerCAmelCase_    )




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

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





							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				,		dtype=np.intaa    )
												a__          =  shift_tokens_right(lowerCAmelCase_				,		1				,		2    )
												a__          =  np.equal(lowerCAmelCase_				,		1    ).astype(np.floataa    ).sum()
												a__          =  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        _SCREAMING_SNAKE_CASE						(  __lowerCAmelCase       , unittest.TestCase       , __lowerCAmelCase							):





							"""simple docstring"""





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




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  FlaxBlenderbotModelTester(self    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__       ,a__          =  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:     int    ):

												'''simple docstring'''

												a__       ,a__          =  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[Any]    ):

												'''simple docstring'''

												a__       ,a__          =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__    ):
																						a__          =  self._prepare_for_class(lowerCAmelCase_				,		lowerCAmelCase_    )
																						a__          =  model_class(lowerCAmelCase_    )

																						@jax.jit
																						def encode_jitted(__A:     str				,		__A:     Dict=None				,		**__A:     int    ):
																											return model.encode(input_ids=lowerCAmelCase_				,		attention_mask=lowerCAmelCase_    )

																						with self.subTest('''JIT Enabled'''    ):
																											a__          =  encode_jitted(**lowerCAmelCase_    ).to_tuple()

																						with self.subTest('''JIT Disabled'''    ):
																											with jax.disable_jit():
																																a__          =  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:     Any    ):

												'''simple docstring'''

												a__       ,a__          =  self.model_tester.prepare_config_and_inputs_for_common()

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

																						a__          =  {
																						    '''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(__A:     List[str]				,		__A:     str				,		__A:     Tuple    ):
																											return model.decode(
																											    decoder_input_ids=lowerCAmelCase_				,		decoder_attention_mask=lowerCAmelCase_				,		encoder_outputs=lowerCAmelCase_				,		)

																						with self.subTest('''JIT Enabled'''    ):
																											a__          =  decode_jitted(**lowerCAmelCase_    ).to_tuple()

																						with self.subTest('''JIT Disabled'''    ):
																											with jax.disable_jit():
																																a__          =  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[int]    ):

												'''simple docstring'''

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





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

												'''simple docstring'''

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

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

												a__          =  ['''Sam''']
												a__          =  tokenizer(lowerCAmelCase_				,		return_tensors='''jax'''    )

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

												a__          =  tokenizer.batch_decode(lowerCAmelCase_				,		**lowerCAmelCase_    )
												assert generated_txt[0].strip() == tgt_text










"""simple docstring"""







import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
__a       :	Optional[int]             = logging.getLogger()

__a       :	Any             = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							def  lowercase		(   self:     List[Any]				,		__A:     List[Any]    ):

												'''simple docstring'''

												os.makedirs(__A				,		exist_ok=__A    )
												a__          =  {'''source''': '''What is love ?''', '''target''': '''life'''}
												a__          =  {'''train''': 12, '''val''': 2, '''test''': 2}
												for split in ["train", "test", "val"]:
																	for field in ["source", "target"]:
																						a__          =  '''\n'''.join([contents[field]] * n_lines[split]    )
																						with open(os.path.join(__A				,		F'{split}.{field}'    )				,		'''w'''    ) as f:
																											f.write(__A    )




							def  lowercase		(   self:     Optional[int]				,		__A:     int				,		__A:     str = "pytorch"    ):

												'''simple docstring'''

												a__          =  self.get_auto_remove_tmp_dir()
												a__          =  os.path.join(__A				,		'''output'''    )
												a__          =  os.path.join(__A				,		'''data'''    )
												self._create_dummy_data(data_dir=__A    )

												a__          =  F'\n                --data_dir {data_dir} \\n                --output_dir {output_dir} \\n                --model_name_or_path facebook/rag-sequence-base \\n                --model_type rag_sequence \\n                --do_train \\n                --do_predict \\n                --n_val -1 \\n                --val_check_interval 1.0 \\n                --train_batch_size 2 \\n                --eval_batch_size 1 \\n                --max_source_length 25 \\n                --max_target_length 25 \\n                --val_max_target_length 25 \\n                --test_max_target_length 25 \\n                --label_smoothing 0.1 \\n                --dropout 0.1 \\n                --attention_dropout 0.1 \\n                --weight_decay 0.001 \\n                --adam_epsilon 1e-08 \\n                --max_grad_norm 0.1 \\n                --lr_scheduler polynomial \\n                --learning_rate 3e-04 \\n                --num_train_epochs 1 \\n                --warmup_steps 4 \\n                --gradient_accumulation_steps 1 \\n                --distributed-port 8787 \\n                --use_dummy_dataset 1 \\n                --distributed_retriever {distributed_retriever} \\n            '.split()

												if gpus > 0:
																	testargs.append(F'--gpus={gpus}'    )
																	if is_apex_available():
																						testargs.append('''--fp16'''    )
												else:
																	testargs.append('''--gpus=0'''    )
																	testargs.append('''--distributed_backend=ddp_cpu'''    )
																	testargs.append('''--num_processes=2'''    )

												a__          =  [sys.executable, str(Path(finetune_rag.__file__    ).resolve()    )] + testargs
												execute_subprocess_async(__A				,		env=self.get_env()    )

												a__          =  os.path.join(__A				,		'''metrics.json'''    )
												with open(__A    ) as f:
																	a__          =  json.load(__A    )
												return result




							@require_torch_gpu
							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )




							@require_torch_multi_gpu
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=2    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )




							@require_torch_gpu
							@require_ray
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1				,		distributed_retriever='''ray'''    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )





							@require_torch_multi_gpu
							@require_ray
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1				,		distributed_retriever='''ray'''    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )


