"""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
	_a	:	List[Any]=					"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     __UpperCAmelCase							(       UpperCAmelCase_       :							str     ,				UpperCAmelCase_       :							List[str]     ,				UpperCAmelCase_       :							str=None     ,				UpperCAmelCase_       :							List[Any]=None     ,				UpperCAmelCase_       :							Optional[int]=None     ,				UpperCAmelCase_       :							Tuple=None     ,				UpperCAmelCase_       :							str=None     ,				UpperCAmelCase_       :							Dict=None     ,				)				->     Tuple:

	'''simple docstring'''





	if attention_mask is None:
		__snake_case     :				Optional[Any] 						=					np.where(input_ids != config.pad_token_id     ,				1     ,				0    )
	if decoder_attention_mask is None:
		__snake_case     :				List[str] 						=					np.where(decoder_input_ids != config.pad_token_id     ,				1     ,				0    )
	if head_mask is None:
		__snake_case     :				Union[str, Any] 						=					np.ones((config.encoder_layers, config.encoder_attention_heads)    )
	if decoder_head_mask is None:
		__snake_case     :				List[Any] 						=					np.ones((config.decoder_layers, config.decoder_attention_heads)    )
	if cross_attn_head_mask is None:
		__snake_case     :				Optional[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 						UpperCamelCase     :



				def __init__(self							: Optional[int]      ,    _A							: Any      ,    _A							: Union[str, Any]=13      ,    _A							: List[str]=7      ,    _A							: Optional[Any]=True      ,    _A							: List[Any]=False      ,    _A							: Any=99      ,    _A							: str=16      ,    _A							: Optional[int]=2      ,    _A							: List[str]=4      ,    _A							: str=4      ,    _A							: Any="gelu"      ,    _A							: Union[str, Any]=0.1      ,    _A							: Dict=0.1      ,    _A							: Optional[Any]=32      ,    _A							: Optional[int]=2      ,    _A							: List[str]=1      ,    _A							: Any=0      ,    _A							: Optional[int]=0.02      ,    )       ->      str:
					__snake_case     :				List[Any] 						=					parent
					__snake_case     :				Tuple 						=					batch_size
					__snake_case     :				Any 						=					seq_length
					__snake_case     :				Any 						=					is_training
					__snake_case     :				Optional[int] 						=					use_labels
					__snake_case     :				str 						=					vocab_size
					__snake_case     :				Dict 						=					hidden_size
					__snake_case     :				str 						=					num_hidden_layers
					__snake_case     :				List[Any] 						=					num_attention_heads
					__snake_case     :				Tuple 						=					intermediate_size
					__snake_case     :				Optional[int] 						=					hidden_act
					__snake_case     :				int 						=					hidden_dropout_prob
					__snake_case     :				Optional[Any] 						=					attention_probs_dropout_prob
					__snake_case     :				Optional[int] 						=					max_position_embeddings
					__snake_case     :				List[Any] 						=					eos_token_id
					__snake_case     :				Dict 						=					pad_token_id
					__snake_case     :				str 						=					bos_token_id
					__snake_case     :				Union[str, Any] 						=					initializer_range



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

					__snake_case     :				Optional[Any] 						=					shift_tokens_right(_A      ,    1      ,    2)

					__snake_case     :				str 						=					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=_A      ,    )
					__snake_case     :				List[str] 						=					prepare_marian_inputs_dict(_A      ,    _A      ,    _A)
					return config, inputs_dict



				def    _lowercase	(self							: Optional[Any])       ->      List[Any]:
					__snake_case						,							__snake_case     :				Tuple 						=					self.prepare_config_and_inputs()
					return config, inputs_dict



				def    _lowercase	(self							: Tuple      ,    _A							: Tuple      ,    _A							: Dict      ,    _A							: List[str])       ->      Optional[int]:
					__snake_case     :				Union[str, Any] 						=					20
					__snake_case     :				str 						=					model_class_name(_A)

					__snake_case     :				Union[str, Any] 						=					model.encode(inputs_dict['input_ids'])

					__snake_case						,							__snake_case     :				Tuple 						=					(
					    inputs_dict['decoder_input_ids'],
					    inputs_dict['decoder_attention_mask'],
					)

					__snake_case     :				str 						=					model.init_cache(decoder_input_ids.shape[0]      ,    _A      ,    _A)
					__snake_case     :				Union[str, Any] 						=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,    dtype='i4')

					__snake_case     :				Optional[int] 						=					jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]      ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,    )
					__snake_case     :				Union[str, Any] 						=					model.decode(
					    decoder_input_ids[:, :-1]      ,    _A      ,    decoder_attention_mask=_A      ,    past_key_values=_A      ,    decoder_position_ids=_A      ,    )

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

					__snake_case     :				Tuple 						=					model.decode(_A      ,    _A)

					__snake_case     :				Any 						=					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							: Optional[Any]      ,    _A							: Union[str, Any]      ,    _A							: List[Any]      ,    _A							: List[str])       ->      int:
					__snake_case     :				str 						=					20
					__snake_case     :				Tuple 						=					model_class_name(_A)

					__snake_case     :				Tuple 						=					model.encode(inputs_dict['input_ids'])

					__snake_case						,							__snake_case     :				str 						=					(
					    inputs_dict['decoder_input_ids'],
					    inputs_dict['decoder_attention_mask'],
					)

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

					__snake_case     :				Dict 						=					model.init_cache(decoder_input_ids.shape[0]      ,    _A      ,    _A)
					__snake_case     :				Tuple 						=					jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]      ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,    )

					__snake_case     :				Dict 						=					model.decode(
					    decoder_input_ids[:, :-1]      ,    _A      ,    decoder_attention_mask=_A      ,    past_key_values=_A      ,    decoder_position_ids=_A      ,    )
					__snake_case     :				List[str] 						=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,    dtype='i4')
					__snake_case     :				List[Any] 						=					model.decode(
					    decoder_input_ids[:, -1:]      ,    _A      ,    past_key_values=outputs_cache.past_key_values      ,    decoder_attention_mask=_A      ,    decoder_position_ids=_A      ,    )

					__snake_case     :				Dict 						=					model.decode(_A      ,    _A      ,    decoder_attention_mask=_A)

					__snake_case     :				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 						UpperCamelCase     (       lowercase ,      unittest.TestCase ,      lowercase		):
				UpperCAmelCase							: Tuple       =						True
				UpperCAmelCase							: Any       =						(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
				UpperCAmelCase							: Dict       =						(FlaxMarianMTModel,) if is_flax_available() else ()



				def    _lowercase	(self							: List[Any])       ->      Optional[Any]:
					__snake_case     :				Union[str, Any] 						=					FlaxMarianModelTester(self)



				def    _lowercase	(self							: List[Any])       ->      List[Any]:
					__snake_case						,							__snake_case     :				Tuple 						=					self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
						self.model_tester.check_use_cache_forward(_A      ,    _A      ,    _A)



				def    _lowercase	(self							: List[str])       ->      str:
					__snake_case						,							__snake_case     :				str 						=					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(_A      ,    _A      ,    _A)



				def    _lowercase	(self							: List[Any])       ->      List[Any]:
					__snake_case						,							__snake_case     :				Tuple 						=					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__):
							__snake_case     :				int 						=					self._prepare_for_class(_A      ,    _A)
							__snake_case     :				List[str] 						=					model_class(_A)

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

							with self.subTest('JIT Enabled'):
								__snake_case     :				Tuple 						=					encode_jitted(**_A).to_tuple()

							with self.subTest('JIT Disabled'):
								with jax.disable_jit():
									__snake_case     :				str 						=					encode_jitted(**_A).to_tuple()

							self.assertEqual(len(_A)      ,    len(_A))
							for jitted_output, output in zip(_A      ,    _A):
								self.assertEqual(jitted_output.shape      ,    output.shape)



				def    _lowercase	(self							: List[str])       ->      Optional[int]:
					__snake_case						,							__snake_case     :				int 						=					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__):
							__snake_case     :				Optional[Any] 						=					model_class(_A)
							__snake_case     :				List[Any] 						=					model.encode(inputs_dict['input_ids']      ,    inputs_dict['attention_mask'])

							__snake_case     :				Optional[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(_A							: List[str]      ,    _A							: Union[str, Any]      ,    _A							: List[str]):
								return model.decode(
								    decoder_input_ids=_A      ,    decoder_attention_mask=_A      ,    encoder_outputs=_A      ,    )

							with self.subTest('JIT Enabled'):
								__snake_case     :				Optional[int] 						=					decode_jitted(**_A).to_tuple()

							with self.subTest('JIT Disabled'):
								with jax.disable_jit():
									__snake_case     :				Optional[Any] 						=					decode_jitted(**_A).to_tuple()

							self.assertEqual(len(_A)      ,    len(_A))
							for jitted_output, output in zip(_A      ,    _A):
								self.assertEqual(jitted_output.shape      ,    output.shape)






				@slow
				def    _lowercase	(self							: Optional[Any])       ->      Optional[Any]:
					for model_class_name in self.all_model_classes:
						__snake_case     :				int 						=					model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de')
						# FlaxMarianForSequenceClassification expects eos token in input_ids
						__snake_case     :				Optional[Any] 						=					np.ones((1, 1)) * model.config.eos_token_id
						__snake_case     :				Optional[int] 						=					model(_A)
						self.assertIsNotNone(_A)







@require_flax
@require_sentencepiece
@require_tokenizers
class 						UpperCamelCase     (       unittest.TestCase		):
				UpperCAmelCase							: str       =						None
				UpperCAmelCase							: int       =						None



				@classmethod
				def    _lowercase	(cls							: Any)       ->      None:
					__snake_case     :				Optional[Any] 						=					f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
					return cls



				@cached_property
				def    _lowercase	(self							: Union[str, Any])       ->      str:
					return MarianTokenizer.from_pretrained(self.model_name)



				@property
				def    _lowercase	(self							: List[str])       ->      int:
					return self.tokenizer.eos_token_id



				@cached_property
				def    _lowercase	(self							: Dict)       ->      Optional[int]:
					__snake_case     :				FlaxMarianMTModel 						=					FlaxMarianMTModel.from_pretrained(self.model_name)
					self.assertEqual(model.config.decoder_start_token_id      ,    model.config.pad_token_id)
					return model



				def    _lowercase	(self							: Dict      ,    **_A							: Dict)       ->      str:
					__snake_case     :				int 						=					self.translate_src_text(**_A)
					self.assertListEqual(self.expected_text      ,    _A)






				def    _lowercase	(self							: List[str]      ,    **_A							: Optional[int])       ->      Tuple:
					__snake_case     :				Optional[int] 						=					self.tokenizer(self.src_text      ,    padding=_A      ,    return_tensors='np'      ,    **_A)
					__snake_case     :				Tuple 						=					self.model.generate(
					    model_inputs.input_ids      ,    attention_mask=model_inputs.attention_mask      ,    num_beams=2      ,    max_length=1_28      ,    ).sequences
					__snake_case     :				Any 						=					self.tokenizer.batch_decode(_A      ,    skip_special_tokens=_A)
					return generated_words







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



				@slow
				def    _lowercase	(self							: int)       ->      Tuple:
					self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: str       =						"""fr"""
				UpperCAmelCase							: Optional[Any]       =						"""en"""
				UpperCAmelCase							: Union[str, Any]       =						[
				    """Donnez moi le micro.""",
				    """Tom et Mary étaient assis à une table.""",  # Accents
				]
				UpperCAmelCase							: Tuple       =						[
				    """Give me the microphone.""",
				    """Tom and Mary were sitting at a table.""",
				]



				@slow
				def    _lowercase	(self							: Any)       ->      Optional[Any]:
					self._assert_generated_batch_equal_expected()







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



				@slow
				def    _lowercase	(self							: List[Any])       ->      Dict:
					self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: Any       =						"""en"""
				UpperCAmelCase							: Union[str, Any]       =						"""de"""
				UpperCAmelCase							: Optional[Any]       =						[
				    """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							: Any       =						[
				    """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    _lowercase	(self							: Optional[int])       ->      Dict:
					self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: Optional[Any]       =						"""en"""
				UpperCAmelCase							: List[str]       =						"""zh"""
				UpperCAmelCase							: Any       =						["""My name is Wolfgang and I live in Berlin"""]
				UpperCAmelCase							: Any       =						["""我叫沃尔夫冈 我住在柏林"""]



				@slow
				def    _lowercase	(self							: Optional[int])       ->      Tuple:
					self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: Tuple       =						"""ru"""
				UpperCAmelCase							: Tuple       =						"""fr"""
				UpperCAmelCase							: Optional[Any]       =						["""Он показал мне рукопись своей новой пьесы."""]
				UpperCAmelCase							: Any       =						["""Il m'a montré le manuscrit de sa nouvelle pièce."""]



				@slow
				def    _lowercase	(self							: Dict)       ->      List[str]:
					self._assert_generated_batch_equal_expected()







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



				@slow
				def    _lowercase	(self							: Any)       ->      int:
					self._assert_generated_batch_equal_expected()






"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


_a	:	Union[str, Any]=					"Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"

class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: str       =						"""google/flan-t5-base"""
				UpperCAmelCase							: int       =						(
				    """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
				    """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
				)
				UpperCAmelCase							: Dict       =						"""text_qa"""
				UpperCAmelCase							: Dict       =						AutoTokenizer
				UpperCAmelCase							: Optional[Any]       =						AutoModelForSeqaSeqLM

				UpperCAmelCase							: List[str]       =						["""text""", """text"""]
				UpperCAmelCase							: Any       =						["""text"""]



				def    _lowercase	(self							: Tuple      ,    _A							: str      ,    _A							: str)       ->      Tuple:
					__snake_case     :				List[Any] 						=					QA_PROMPT.format(text=_A      ,    question=_A)
					return self.pre_processor(_A      ,    return_tensors='pt')



				def    _lowercase	(self							: Tuple      ,    _A							: Optional[Any])       ->      List[Any]:
					__snake_case     :				List[Any] 						=					self.model.generate(**_A)

					__snake_case						,							__snake_case     :				Any 						=					inputs['input_ids'].shape
					__snake_case     :				Any 						=					output_ids.shape[0]

					return output_ids.reshape(_A      ,    out_b // in_b      ,    *output_ids.shape[1:])[0][0]






				def    _lowercase	(self							: Optional[int]      ,    _A							: int)       ->      str:
					return self.pre_processor.decode(_A      ,    skip_special_tokens=_A      ,    clean_up_tokenization_spaces=_A)





