





"""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
					lowercase__      =				"""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 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase=None   , __UpperCamelCase=None   , __UpperCamelCase=None   , __UpperCamelCase=None   , __UpperCamelCase=None   , __UpperCamelCase=None   , )		->					Union[str, Any]:

		"""simple docstring"""







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





		'''simple docstring'''






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





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

				lowerCAmelCase_     :							List[Any]             =    shift_tokens_right(a_		,    1		,    2			)

				lowerCAmelCase_     :							Optional[int]             =    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_		,    )
				lowerCAmelCase_     :							Tuple             =    prepare_marian_inputs_dict(a_		,    a_		,    a_			)
				return config, inputs_dict





		def      lowerCamelCase (     self							:			List[str]			):
				lowerCAmelCase_     :							Optional[int]             =    self.prepare_config_and_inputs()
				return config, inputs_dict





		def      lowerCamelCase (     self							:			Dict		,    a_							:			Optional[int]		,    a_							:			List[str]		,    a_							:			Union[str, Any]			):
				lowerCAmelCase_     :							Optional[int]             =    20
				lowerCAmelCase_     :							List[Any]             =    model_class_name(a_			)

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

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

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

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

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

				lowerCAmelCase_     :							int             =    model.decode(a_		,    a_			)

				lowerCAmelCase_     :							Optional[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      lowerCamelCase (     self							:			List[Any]		,    a_							:			Union[str, Any]		,    a_							:			Optional[Any]		,    a_							:			List[Any]			):
				lowerCAmelCase_     :							Union[str, Any]             =    20
				lowerCAmelCase_     :							Dict             =    model_class_name(a_			)

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

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

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

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

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

				lowerCAmelCase_     :							Any             =    model.decode(a_		,    a_		,    decoder_attention_mask=a_			)

				lowerCAmelCase_     :							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}'''			)







@require_flax
class 	__lowerCamelCase       (				A__				,							unittest.TestCase				,							A__    ):





		'''simple docstring'''






		a_			:       Any      =							True
		a_			:       Union[str, Any]      =							(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
		a_			:       List[str]      =							(FlaxMarianMTModel,) if is_flax_available() else ()





		def      lowerCamelCase (     self							:			List[Any]			):
				lowerCAmelCase_     :							List[Any]             =    FlaxMarianModelTester(self			)





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

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

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

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

								with self.subTest("JIT Disabled"			):
										with jax.disable_jit():
												lowerCAmelCase_     :							Tuple             =    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      lowerCamelCase (     self							:			Dict			):
				lowerCAmelCase_     :							Dict             =    self.model_tester.prepare_config_and_inputs_for_common()

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

								lowerCAmelCase_     :							Optional[int]             =    {
								    "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_							:			Optional[int]		,    a_							:			str		,    a_							:			Any			):
										return model.decode(
										    decoder_input_ids=a_		,    decoder_attention_mask=a_		,    encoder_outputs=a_		,    )

								with self.subTest("JIT Enabled"			):
										lowerCAmelCase_     :							Union[str, Any]             =    decode_jitted(**a_			).to_tuple()

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







@require_flax
@require_sentencepiece
@require_tokenizers
class 	__lowerCamelCase       (				unittest.TestCase    ):





		'''simple docstring'''






		a_			:       Optional[int]      =							None
		a_			:       Optional[int]      =							None





		@classmethod
		def      lowerCamelCase (     cls							:			Tuple			):
				lowerCAmelCase_     :							Optional[Any]             =    f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
				return cls





		@cached_property
		def      lowerCamelCase (     self							:			Any			):
				return MarianTokenizer.from_pretrained(self.model_name			)





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





		@cached_property
		def      lowerCamelCase (     self							:			Tuple			):
				lowerCAmelCase_     :							FlaxMarianMTModel             =    FlaxMarianMTModel.from_pretrained(self.model_name			)
				self.assertEqual(model.config.decoder_start_token_id		,    model.config.pad_token_id			)
				return model





		def      lowerCamelCase (     self							:			Dict		,    **a_							:			Tuple			):
				lowerCAmelCase_     :							Any             =    self.translate_src_text(**a_			)
				self.assertListEqual(self.expected_text		,    a_			)





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







@require_flax
@require_sentencepiece
@require_tokenizers
class 	__lowerCamelCase       (				A__    ):





		'''simple docstring'''






		a_			:       Optional[int]      =							"""en"""
		a_			:       Union[str, Any]      =							"""fr"""
		a_			:       Tuple      =							[
		    """I am a small frog.""",
		    """Now I can forget the 100 words of german that I know.""",
		]
		a_			:       str      =							[
		    """Je suis une petite grenouille.""",
		    """Maintenant, je peux oublier les 100 mots d'allemand que je connais.""",
		]





		@slow
		def      lowerCamelCase (     self							:			Union[str, Any]			):
				self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 	__lowerCamelCase       (				A__    ):





		'''simple docstring'''






		a_			:       Any      =							"""fr"""
		a_			:       Optional[int]      =							"""en"""
		a_			:       Tuple      =							[
		    """Donnez moi le micro.""",
		    """Tom et Mary étaient assis à une table.""",  # Accents
		]
		a_			:       Union[str, Any]      =							[
		    """Give me the microphone.""",
		    """Tom and Mary were sitting at a table.""",
		]





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







@require_flax
@require_sentencepiece
@require_tokenizers
class 	__lowerCamelCase       (				A__    ):





		'''simple docstring'''






		a_			:       List[Any]      =							"""mt"""
		a_			:       Optional[Any]      =							"""en"""
		a_			:       List[Any]      =							["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
		a_			:       Dict      =							["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]





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







@require_flax
@require_sentencepiece
@require_tokenizers
class 	__lowerCamelCase       (				A__    ):





		'''simple docstring'''






		a_			:       Optional[int]      =							"""en"""
		a_			:       int      =							"""de"""
		a_			:       Dict      =							[
		    """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.""",
		]
		a_			:       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      lowerCamelCase (     self							:			str			):
				self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 	__lowerCamelCase       (				A__    ):





		'''simple docstring'''






		a_			:       str      =							"""en"""
		a_			:       int      =							"""zh"""
		a_			:       List[Any]      =							["""My name is Wolfgang and I live in Berlin"""]
		a_			:       int      =							["""我叫沃尔夫冈 我住在柏林"""]





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







@require_flax
@require_sentencepiece
@require_tokenizers
class 	__lowerCamelCase       (				A__    ):





		'''simple docstring'''






		a_			:       List[Any]      =							"""ru"""
		a_			:       Dict      =							"""fr"""
		a_			:       List[Any]      =							["""Он показал мне рукопись своей новой пьесы."""]
		a_			:       List[str]      =							["""Il m'a montré le manuscrit de sa nouvelle pièce."""]





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







@require_flax
@require_sentencepiece
@require_tokenizers
class 	__lowerCamelCase       (				A__    ):





		'''simple docstring'''






		a_			:       List[str]      =							"""en"""
		a_			:       Optional[Any]      =							"""ROMANCE"""
		a_			:       int      =							[
		    """>>fr<< Don't spend so much time watching TV.""",
		    """>>pt<< Your message has been sent.""",
		    """>>es<< He's two years older than me.""",
		]
		a_			:       int      =							[
		    """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							:			Tuple			):
				self._assert_generated_batch_equal_expected()








"""simple docstring"""

from .pipeline_kandinskya_a import KandinskyVaaPipeline
from .pipeline_kandinsky2_2_controlnet import KandinskyVaaControlnetPipeline
from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyVaaControlnetImgaImgPipeline
from .pipeline_kandinsky2_2_img2img import KandinskyVaaImgaImgPipeline
from .pipeline_kandinsky2_2_inpainting import KandinskyVaaInpaintPipeline
from .pipeline_kandinsky2_2_prior import KandinskyVaaPriorPipeline
from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyVaaPriorEmbaEmbPipeline
