





"""simple docstring"""
from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
		import numpy as np
		import tensorflow as tf

		from transformers import (
		    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
		    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_MASKED_LM_MAPPING,
		    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
		    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
		    TF_MODEL_FOR_PRETRAINING_MAPPING,
		    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
		    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    TFSharedEmbeddings,
		)

		if _tf_gpu_memory_limit is not None:
				_snake_case					:		Optional[int]							=						tf.config.list_physical_devices('GPU')
				for gpu in gpus:
						# Restrict TensorFlow to only allocate x GB of memory on the GPUs
						try:
								tf.config.set_logical_device_configuration(
								    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
								)
								_snake_case					:		Any							=						tf.config.list_logical_devices('GPU')
								print('Logical GPUs', logical_gpus)
						except RuntimeError as e:
								# Virtual devices must be set before GPUs have been initialized
								print(e)






@require_tf
class  _UpperCAmelCase       :
					UpperCamelCase								=							None
					UpperCamelCase								=							()
					UpperCamelCase								=							()
					UpperCamelCase								=							True
					UpperCamelCase								=							True
					UpperCamelCase								=							True
					UpperCamelCase								=							False


					def      lowerCamelCase	( self      :Any					,	__UpperCamelCase      :Optional[int]					,	__UpperCamelCase      :Dict					,	__UpperCamelCase      :Any=False		):
												A		=  copy.deepcopy(UpperCamelCase__		)

												if model_class in get_values(UpperCamelCase__		):
																			A		=  {
																			    k: tf.tile(tf.expand_dims(UpperCamelCase__					,	1		)					,	(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)		)
																			    if isinstance(UpperCamelCase__					,	tf.Tensor		) and v.ndim > 0
																			    else v
																			    for k, v in inputs_dict.items()
																			}

												if return_labels:
																			if model_class in get_values(UpperCamelCase__		):
																										A		=  tf.ones(self.model_tester.batch_size					,	dtype=tf.intaa		)
																			elif model_class in get_values(UpperCamelCase__		):
																										A		=  tf.zeros(self.model_tester.batch_size					,	dtype=tf.intaa		)
																										A		=  tf.zeros(self.model_tester.batch_size					,	dtype=tf.intaa		)
																			elif model_class in [
																			    *get_values(UpperCamelCase__		),
																			    *get_values(UpperCamelCase__		),
																			]:
																										A		=  tf.zeros(self.model_tester.batch_size					,	dtype=tf.intaa		)
																			elif model_class in get_values(UpperCamelCase__		):
																										A		=  tf.zeros(self.model_tester.batch_size					,	dtype=tf.intaa		)
																			elif model_class in [
																			    *get_values(UpperCamelCase__		),
																			    *get_values(UpperCamelCase__		),
																			    *get_values(UpperCamelCase__		),
																			    *get_values(UpperCamelCase__		),
																			    *get_values(UpperCamelCase__		),
																			]:
																										A		=  tf.zeros(
																										    (self.model_tester.batch_size, self.model_tester.seq_length)					,	dtype=tf.intaa		)
												return inputs_dict


					@slow
					def      lowerCamelCase	( self      :Dict		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes[:2]:
																			A		=  self._prepare_for_class(UpperCamelCase__					,	UpperCamelCase__		)
																			A		=  model_class(UpperCamelCase__		)

																			@tf.function
																			def run_in_graph_mode():
																										return model(UpperCamelCase__		)

																			A		=  run_in_graph_mode()
																			self.assertIsNotNone(UpperCamelCase__		)


					@slow
					def      lowerCamelCase	( self      :Union[str, Any]		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes[:2]:
																			A		=  self._prepare_for_class(UpperCamelCase__					,	UpperCamelCase__		)
																			A		=  model_class(UpperCamelCase__		)

																			@tf.function(experimental_compile=UpperCamelCase__		)
																			def run_in_graph_mode():
																										return model(UpperCamelCase__		)

																			A		=  run_in_graph_mode()
																			self.assertIsNotNone(UpperCamelCase__		)


					@slow
					def      lowerCamelCase	( self      :List[Any]		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes[:2]:
																			A		=  model_class(UpperCamelCase__		)
																			if getattr(UpperCamelCase__					,	"hf_compute_loss"					,	UpperCamelCase__		):
																										# Test that model correctly compute the loss with kwargs
																										A		=  self._prepare_for_class(inputs_dict.copy()					,	UpperCamelCase__					,	return_labels=UpperCamelCase__		)
																										# Is there a better way to remove these decoder inputs?
																										A		=  {
																										    key: val
																										    for key, val in prepared_for_class.items()
																										    if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
																										}

																										A		=  {
																										    "labels",
																										    "label",
																										    "label_ids",
																										    "start_positions",
																										    "start_position",
																										    "end_positions",
																										    "end_position",
																										    "next_sentence_label",
																										}
																										A		=  possible_label_cols.intersection(set(UpperCamelCase__		)		)
																										self.assertGreater(len(UpperCamelCase__		)					,	0					,	msg="No matching label names found!"		)
																										A		=  {key: val for key, val in prepared_for_class.items() if key in label_names}
																										A		=  {key: val for key, val in prepared_for_class.items() if key not in label_names}
																										self.assertGreater(len(UpperCamelCase__		)					,	0		)

																										# Make sure it works with XLA!
																										model.compile(optimizer=tf.keras.optimizers.SGD(0.0		)					,	jit_compile=UpperCamelCase__		)
																										# Make sure the model fits without crashing regardless of where we pass the labels
																										A		=  model.fit(
																										    UpperCamelCase__					,	validation_data=UpperCamelCase__					,	steps_per_epoch=1					,	validation_steps=1					,	shuffle=UpperCamelCase__					,	verbose=0					,	)
																										A		=  history.history["loss"][0]
																										self.assertTrue(not isnan(UpperCamelCase__		)		)
																										A		=  history.history["val_loss"][0]
																										self.assertTrue(not isnan(UpperCamelCase__		)		)

																										# Now test it with separate labels, to make sure that path works in XLA too.
																										A		=  model_class(UpperCamelCase__		)
																										model.compile(optimizer=tf.keras.optimizers.SGD(0.0		)					,	jit_compile=UpperCamelCase__		)
																										A		=  model.fit(
																										    UpperCamelCase__					,	UpperCamelCase__					,	validation_data=(inputs_minus_labels, labels)					,	steps_per_epoch=1					,	validation_steps=1					,	shuffle=UpperCamelCase__					,	verbose=0					,	)

																										A		=  history.history["loss"][0]
																										self.assertTrue(not isnan(UpperCamelCase__		)		)
																										A		=  history.history["val_loss"][0]
																										self.assertTrue(not isnan(UpperCamelCase__		)		)


					@slow
					def      lowerCamelCase	( self      :Optional[int]		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()
												A		=  True
												A		=  True

												if hasattr(UpperCamelCase__					,	"use_cache"		):
																			A		=  True

												A		=  getattr(self.model_tester					,	"encoder_seq_length"					,	self.model_tester.seq_length		)
												A		=  getattr(self.model_tester					,	"key_length"					,	UpperCamelCase__		)

												for model_class in self.all_model_classes[:2]:
																			A		=  self._prepare_for_class(UpperCamelCase__					,	UpperCamelCase__		)
																			A		=  model_class(UpperCamelCase__		)
																			model.build()
																			A		=  len(model(UpperCamelCase__		)		)

																			for key in list(class_inputs_dict.keys()		):
																										# Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
																										if key not in model.input_signature:
																																	del class_inputs_dict[key]
																										# Check it's a tensor, in case the inputs dict has some bools in it too
																										elif isinstance(class_inputs_dict[key]					,	tf.Tensor		) and class_inputs_dict[key].dtype.is_integer:
																																	A		=  tf.cast(class_inputs_dict[key]					,	tf.intaa		)

																			if set(class_inputs_dict.keys()		) != set(model.input_signature.keys()		):
																										continue  # Some models have inputs that the preparation functions don't create, we skip those

																			with tempfile.TemporaryDirectory() as tmpdirname:
																										model.save_pretrained(UpperCamelCase__					,	saved_model=UpperCamelCase__		)
																										A		=  os.path.join(UpperCamelCase__					,	"saved_model"					,	"1"		)
																										A		=  tf.keras.models.load_model(UpperCamelCase__		)
																										A		=  model(UpperCamelCase__		)

																										if self.is_encoder_decoder:
																																	A		=  outputs["encoder_hidden_states"]
																																	A		=  outputs["encoder_attentions"]
																										else:
																																	A		=  outputs["hidden_states"]
																																	A		=  outputs["attentions"]

																										self.assertEqual(len(UpperCamelCase__		)					,	UpperCamelCase__		)

																										A		=  getattr(
																										    self.model_tester					,	"expected_num_hidden_layers"					,	self.model_tester.num_hidden_layers + 1		)

																										self.assertEqual(len(UpperCamelCase__		)					,	UpperCamelCase__		)
																										self.assertListEqual(
																										    list(output_hidden_states[0].shape[-2:]		)					,	[self.model_tester.seq_length, self.model_tester.hidden_size]					,	)

																										self.assertEqual(len(UpperCamelCase__		)					,	self.model_tester.num_hidden_layers		)
																										self.assertListEqual(
																										    list(output_attentions[0].shape[-3:]		)					,	[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]					,	)


					@slow
					def      lowerCamelCase	( self      :List[Any]		):
												tf.keras.mixed_precision.set_global_policy("mixed_float16"		)

												# try/finally block to ensure subsequent tests run in float32
												try:
																			A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()
																			for model_class in self.all_model_classes[:2]:
																										A		=  self._prepare_for_class(UpperCamelCase__					,	UpperCamelCase__		)
																										A		=  model_class(UpperCamelCase__		)
																										A		=  model(UpperCamelCase__		)

																										self.assertIsNotNone(UpperCamelCase__		)
												finally:
																			tf.keras.mixed_precision.set_global_policy("float32"		)


					@slow
					def      lowerCamelCase	( self      :List[str]		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()
												# head_mask and decoder_head_mask has different shapes than other input args
												if "head_mask" in inputs_dict:
																			del inputs_dict["head_mask"]
												if "decoder_head_mask" in inputs_dict:
																			del inputs_dict["decoder_head_mask"]
												if "cross_attn_head_mask" in inputs_dict:
																			del inputs_dict["cross_attn_head_mask"]
												A		=  {
												    module_member
												    for model_class in self.all_model_classes
												    for module in (import_module(model_class.__module__		),)
												    for module_member_name in dir(UpperCamelCase__		)
												    if module_member_name.endswith("MainLayer"		)
												    for module_member in (getattr(UpperCamelCase__					,	UpperCamelCase__		),)
												    if isinstance(UpperCamelCase__					,	UpperCamelCase__		)
												    and tf.keras.layers.Layer in module_member.__bases__
												    and getattr(UpperCamelCase__					,	"_keras_serializable"					,	UpperCamelCase__		)
												}

												for main_layer_class in tf_main_layer_classes:
																			# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
																			if "T5" in main_layer_class.__name__:
																										# Take the same values than in TFT5ModelTester for this shared layer
																										A		=  TFSharedEmbeddings(self.model_tester.vocab_size					,	self.model_tester.hidden_size					,	name="shared"		)
																										A		=  False
																										A		=  main_layer_class(UpperCamelCase__					,	embed_tokens=UpperCamelCase__		)
																			else:
																										A		=  main_layer_class(UpperCamelCase__		)

																			A		=  {
																			    name: tf.keras.Input(tensor.shape[1:]					,	dtype=tensor.dtype		) for name, tensor in inputs_dict.items()
																			}

																			if hasattr(self.model_tester					,	"num_labels"		):
																										A		=  self.model_tester.num_labels
																			else:
																										A		=  2

																			A		=  tf.data.Dataset.from_tensor_slices(
																			    (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)		))		).batch(1		)

																			A		=  main_layer(UpperCamelCase__		)[0]
																			A		=  tf.keras.layers.Dense(UpperCamelCase__					,	activation="softmax"					,	name="outputs"		)(UpperCamelCase__		)
																			A		=  tf.keras.models.Model(inputs=UpperCamelCase__					,	outputs=[outputs]		)

																			model.compile(loss="binary_crossentropy"					,	optimizer="adam"					,	metrics=["binary_accuracy"]		)
																			model.fit(UpperCamelCase__					,	epochs=1		)

																			with tempfile.TemporaryDirectory() as tmpdirname:
																										A		=  os.path.join(UpperCamelCase__					,	"keras_model.h5"		)
																										model.save(UpperCamelCase__		)
																										if "T5" in main_layer_class.__name__:
																																	A		=  tf.keras.models.load_model(
																																	    UpperCamelCase__					,	custom_objects={
																																	        main_layer_class.__name__: main_layer_class,
																																	        "TFSharedEmbeddings": TFSharedEmbeddings,
																																	    }					,	)
																										else:
																																	A		=  tf.keras.models.load_model(
																																	    UpperCamelCase__					,	custom_objects={main_layer_class.__name__: main_layer_class}		)
																										assert isinstance(UpperCamelCase__					,	tf.keras.Model		)
																										model(UpperCamelCase__		)


					@slow
					def      lowerCamelCase	( self      :Dict		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes[:2]:
																			A		=  model_class(UpperCamelCase__		)

																			A		=  copy.deepcopy(UpperCamelCase__		)

																			if not self.is_encoder_decoder:
																										A		=  inputs["input_ids"]
																										del inputs["input_ids"]
																			else:
																										A		=  inputs["input_ids"]
																										A		=  inputs.get("decoder_input_ids"					,	UpperCamelCase__		)
																										del inputs["input_ids"]
																										inputs.pop("decoder_input_ids"					,	UpperCamelCase__		)

																			if not self.is_encoder_decoder:
																										A		=  model.get_input_embeddings()(UpperCamelCase__		)
																			else:
																										A		=  model.get_input_embeddings()(UpperCamelCase__		)
																										A		=  model.get_input_embeddings()(UpperCamelCase__		)

																			A		=  self._prepare_for_class(UpperCamelCase__					,	UpperCamelCase__		)

																			@tf.function
																			def run_in_graph_mode():
																										return model(UpperCamelCase__		)

																			A		=  run_in_graph_mode()
																			self.assertIsNotNone(UpperCamelCase__		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :int					,	__UpperCamelCase      :List[str]		):
												A		=  []
												if model.config.bos_token_id is not None:
																			special_tokens.append(model.config.bos_token_id		)
												if model.config.pad_token_id is not None:
																			special_tokens.append(model.config.pad_token_id		)
												if model.config.eos_token_id is not None:
																			special_tokens.append(model.config.eos_token_id		)

												# create random bad tokens that are not special tokens
												A		=  []
												while len(UpperCamelCase__		) < num_bad_tokens:
																			A		=  tf.squeeze(ids_tensor((1, 1)					,	self.model_tester.vocab_size		)					,	0		).numpy()[0]
																			if token not in special_tokens:
																										bad_tokens.append(UpperCamelCase__		)
												return bad_tokens


					def      lowerCamelCase	( self      :Dict					,	__UpperCamelCase      :Tuple		):






												for token_id in output_ids[0].numpy().tolist():
																			self.assertGreaterEqual(UpperCamelCase__					,	0		)
																			self.assertLess(UpperCamelCase__					,	self.model_tester.vocab_size		)


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :List[Any]					,	__UpperCamelCase      :Dict		):






												for bad_word_ids in bad_words_ids:
																			# for all slices in batch
																			for generated_ids_slice in generated_ids:
																										# for all word idx
																										for i in range(len(UpperCamelCase__		)					,	len(UpperCamelCase__		)		):
																																	# if tokens match
																																	if generated_ids_slice[i - len(UpperCamelCase__		) : i] == bad_word_ids:
																																								return True
												return False









"""simple docstring"""
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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
		_snake_case					:		Dict							=			'platform'

		import jax
		import jax.numpy as jnp

		from transformers import MarianTokenizer
		from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase=None ,  UpperCamelCase=None ,  UpperCamelCase=None ,  UpperCamelCase=None ,  UpperCamelCase=None ,  UpperCamelCase=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  _UpperCAmelCase       :


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

												A		=  MarianConfig(
												    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					,	)
												A		=  prepare_marian_inputs_dict(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase		)
												return config, inputs_dict


					def      lowerCamelCase	( self      :Optional[Any]		):
												A,     A		=  self.prepare_config_and_inputs()
												return config, inputs_dict


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :str					,	__UpperCamelCase      :List[str]		):
												A		=  20
												A		=  model_class_name(__UpperCamelCase		)

												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]					,	__UpperCamelCase					,	__UpperCamelCase		)
												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]					,	__UpperCamelCase					,	decoder_attention_mask=__UpperCamelCase					,	past_key_values=__UpperCamelCase					,	decoder_position_ids=__UpperCamelCase					,	)

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

												A		=  model.decode(__UpperCamelCase					,	__UpperCamelCase		)

												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      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Dict					,	__UpperCamelCase      :Tuple		):
												A		=  20
												A		=  model_class_name(__UpperCamelCase		)

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

												A		=  model.decode(__UpperCamelCase					,	__UpperCamelCase					,	decoder_attention_mask=__UpperCamelCase		)

												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  _UpperCAmelCase       (     lowercase_					,							unittest.TestCase					,							lowercase_			):
					UpperCamelCase								=							True
					UpperCamelCase								=							(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
					UpperCamelCase								=							(FlaxMarianMTModel,) if is_flax_available() else ()


					def      lowerCamelCase	( self      :Dict		):
												A		=  FlaxMarianModelTester(self		)


					def      lowerCamelCase	( self      :str		):
												A,     A		=  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      lowerCamelCase	( self      :Dict		):
												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(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :int		):
												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(__UpperCamelCase					,	__UpperCamelCase		)
																										A		=  model_class(__UpperCamelCase		)

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

																										with self.subTest("JIT Enabled"		):
																																	A		=  encode_jitted(**__UpperCamelCase		).to_tuple()

																										with self.subTest("JIT Disabled"		):
																																	with jax.disable_jit():
																																								A		=  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      lowerCamelCase	( self      :Optional[int]		):
												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(__UpperCamelCase		)
																										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(__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :List[str]		):
																																	return model.decode(
																																	    decoder_input_ids=__UpperCamelCase					,	decoder_attention_mask=__UpperCamelCase					,	encoder_outputs=__UpperCamelCase					,	)

																										with self.subTest("JIT Enabled"		):
																																	A		=  decode_jitted(**__UpperCamelCase		).to_tuple()

																										with self.subTest("JIT Disabled"		):
																																	with jax.disable_jit():
																																								A		=  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      lowerCamelCase	( self      :str		):
												for model_class_name in self.all_model_classes:
																			A		=  model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de"		)
																			# FlaxMarianForSequenceClassification expects eos token in input_ids
																			A		=  np.ones((1, 1)		) * model.config.eos_token_id
																			A		=  model(__UpperCamelCase		)
																			self.assertIsNotNone(__UpperCamelCase		)







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     unittest.TestCase			):
					UpperCamelCase								=							None
					UpperCamelCase								=							None


					@classmethod
					def      lowerCamelCase	( cls      :Tuple		):
												A		=  f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
												return cls


					@cached_property
					def      lowerCamelCase	( self      :Optional[int]		):
												return MarianTokenizer.from_pretrained(self.model_name		)


					@property
					def      lowerCamelCase	( self      :int		):
												return self.tokenizer.eos_token_id


					@cached_property
					def      lowerCamelCase	( self      :List[Any]		):
												A		=  FlaxMarianMTModel.from_pretrained(self.model_name		)
												self.assertEqual(model.config.decoder_start_token_id					,	model.config.pad_token_id		)
												return model


					def      lowerCamelCase	( self      :int					,	**__UpperCamelCase      :List[str]		):
												A		=  self.translate_src_text(**__UpperCamelCase		)
												self.assertListEqual(self.expected_text					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :List[Any]					,	**__UpperCamelCase      :List[Any]		):
												A		=  self.tokenizer(self.src_text					,	padding=__UpperCamelCase					,	return_tensors="np"					,	**__UpperCamelCase		)
												A		=  self.model.generate(
												    model_inputs.input_ids					,	attention_mask=model_inputs.attention_mask					,	num_beams=2					,	max_length=1_28					,	).sequences
												A		=  self.tokenizer.batch_decode(__UpperCamelCase					,	skip_special_tokens=__UpperCamelCase		)
												return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''en'''
					UpperCamelCase								=							'''fr'''
					UpperCamelCase								=							[
					    '''I am a small frog.''',
					    '''Now I can forget the 100 words of german that I know.''',
					]
					UpperCamelCase								=							[
					    '''Je suis une petite grenouille.''',
					    '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
					]


					@slow
					def      lowerCamelCase	( self      :str		):
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''fr'''
					UpperCamelCase								=							'''en'''
					UpperCamelCase								=							[
					    '''Donnez moi le micro.''',
					    '''Tom et Mary étaient assis à une table.''',  # Accents
					]
					UpperCamelCase								=							[
					    '''Give me the microphone.''',
					    '''Tom and Mary were sitting at a table.''',
					]


					@slow
					def      lowerCamelCase	( self      :Optional[Any]		):
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''mt'''
					UpperCamelCase								=							'''en'''
					UpperCamelCase								=							['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
					UpperCamelCase								=							['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']


					@slow
					def      lowerCamelCase	( self      :Optional[int]		):
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''en'''
					UpperCamelCase								=							'''de'''
					UpperCamelCase								=							[
					    '''I am a small frog.''',
					    '''Now I can forget the 100 words of german that I know.''',
					    '''Tom asked his teacher for advice.''',
					    '''That\'s how I would do it.''',
					    '''Tom really admired Mary\'s courage.''',
					    '''Turn around and close your eyes.''',
					]
					UpperCamelCase								=							[
					    '''Ich bin ein kleiner Frosch.''',
					    '''Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.''',
					    '''Tom bat seinen Lehrer um Rat.''',
					    '''So würde ich das machen.''',
					    '''Tom bewunderte Marias Mut wirklich.''',
					    '''Drehen Sie sich um und schließen Sie die Augen.''',
					]


					@slow
					def      lowerCamelCase	( self      :Any		):
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''en'''
					UpperCamelCase								=							'''zh'''
					UpperCamelCase								=							['''My name is Wolfgang and I live in Berlin''']
					UpperCamelCase								=							['''我叫沃尔夫冈 我住在柏林''']


					@slow
					def      lowerCamelCase	( self      :List[str]		):
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''ru'''
					UpperCamelCase								=							'''fr'''
					UpperCamelCase								=							['''Он показал мне рукопись своей новой пьесы.''']
					UpperCamelCase								=							['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']


					@slow
					def      lowerCamelCase	( self      :Any		):
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''en'''
					UpperCamelCase								=							'''ROMANCE'''
					UpperCamelCase								=							[
					    '''>>fr<< Don\'t spend so much time watching TV.''',
					    '''>>pt<< Your message has been sent.''',
					    '''>>es<< He\'s two years older than me.''',
					]
					UpperCamelCase								=							[
					    '''Ne passez pas autant de temps à regarder la télé.''',
					    '''A sua mensagem foi enviada.''',
					    '''Es dos años más viejo que yo.''',
					]


					@slow
					def      lowerCamelCase	( self      :Optional[int]		):
												self._assert_generated_batch_equal_expected()


