



'''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
					__SCREAMING_SNAKE_CASE :      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 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      List[Any]							,							lowerCamelCase__   :      Dict							,							lowerCamelCase__   :      Any=None							,							lowerCamelCase__   :      int=None							,							lowerCamelCase__   :      Any=None							,							lowerCamelCase__   :      int=None							,							lowerCamelCase__   :      int=None							,							lowerCamelCase__   :      int=None							,							):


			'''simple docstring'''







			if attention_mask is None:
						A:      List[Any]														=   np.where(input_ids != config.pad_token_id							,							1							,							0				)
			if decoder_attention_mask is None:
						A:      List[Any]														=   np.where(decoder_input_ids != config.pad_token_id							,							1							,							0				)
			if head_mask is None:
						A:      Tuple														=   np.ones((config.encoder_layers, config.encoder_attention_heads)				)
			if decoder_head_mask is None:
						A:      str														=   np.ones((config.decoder_layers, config.decoder_attention_heads)				)
			if cross_attn_head_mask is None:
						A:      List[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   SCREAMING_SNAKE_CASE__				:
	"""simple docstring"""


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


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

				A:      List[str]														=   shift_tokens_right(A					,   1					,   2			)

				A:      Optional[Any]														=   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					,   )
				A:      Tuple														=   prepare_marian_inputs_dict(A					,   A					,   A			)
				return config, inputs_dict


	def      a__ (		self			) ->							str:
				A:      List[Any]														=   self.prepare_config_and_inputs()
				return config, inputs_dict


	def      a__ (		self					,   A					,   A					,   A			) ->							Union[str, Any]:
				A:      List[str]														=   20
				A:      str														=   model_class_name(A			)

				A:      int														=   model.encode(inputs_dict["""input_ids"""]			)

				A:      Tuple														=   (
				    inputs_dict["decoder_input_ids"],
				    inputs_dict["decoder_attention_mask"],
				)

				A:      Optional[Any]														=   model.init_cache(decoder_input_ids.shape[0]					,   A					,   A			)
				A:      Optional[int]														=   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,   dtype="""i4"""			)

				A:      List[str]														=   jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]					,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,   )
				A:      int														=   model.decode(
				    decoder_input_ids[:, :-1]					,   A					,   decoder_attention_mask=A					,   past_key_values=A					,   decoder_position_ids=A					,   )

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

				A:      Dict														=   model.decode(A					,   A			)

				A:      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}'			)





	def      a__ (		self					,   A					,   A					,   A			) ->							Optional[int]:
				A:      Tuple														=   20
				A:      List[Any]														=   model_class_name(A			)

				A:      str														=   model.encode(inputs_dict["""input_ids"""]			)

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

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

				A:      Optional[Any]														=   model.init_cache(decoder_input_ids.shape[0]					,   A					,   A			)
				A:      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)					,   )

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

				A:      List[Any]														=   model.decode(A					,   A					,   decoder_attention_mask=A			)

				A:      Tuple														=   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__				(   A__     ,			unittest.TestCase     ,			A__					):
	"""simple docstring"""


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


	def      a__ (		self			) ->							int:
				A:      List[str]														=   FlaxMarianModelTester(self			)


	def      a__ (		self			) ->							List[Any]:
				A:      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      a__ (		self			) ->							Union[str, Any]:
				A:      Dict														=   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      a__ (		self			) ->							List[str]:
				A:      Union[str, Any]														=   self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__			):
										A:      Optional[int]														=   self._prepare_for_class(A					,   A			)
										A:      Tuple														=   model_class(A			)

										@jax.jit
										def encode_jitted(A					,   A=None					,   **A			):
													return model.encode(input_ids=A					,   attention_mask=A			)

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

										with self.subTest("""JIT Disabled"""			):
													with jax.disable_jit():
																A:      List[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      a__ (		self			) ->							int:
				A:      Dict														=   self.model_tester.prepare_config_and_inputs_for_common()

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

										A:      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					,   A					,   A			):
													return model.decode(
													    decoder_input_ids=A					,   decoder_attention_mask=A					,   encoder_outputs=A					,   )

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

										with self.subTest("""JIT Disabled"""			):
													with jax.disable_jit():
																A:      Optional[int]														=   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      a__ (		self			) ->							List[str]:






				for model_class_name in self.all_model_classes:
							A:      Union[str, Any]														=   model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""			)
							# FlaxMarianForSequenceClassification expects eos token in input_ids
							A:      Optional[int]														=   np.ones((1, 1)			) * model.config.eos_token_id
							A:      Optional[int]														=   model(A			)
							self.assertIsNotNone(A			)







@require_flax
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE__				(   unittest.TestCase					):
	"""simple docstring"""


	A__    :    Any						=				None
	A__    :    Tuple						=				None


	@classmethod
	def      a__ (		cls			) ->							List[str]:
				A:      List[Any]														=   f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
				return cls


	@cached_property
	def      a__ (		self			) ->							Any:
				return MarianTokenizer.from_pretrained(self.model_name			)


	@property
	def      a__ (		self			) ->							int:
				return self.tokenizer.eos_token_id


	@cached_property
	def      a__ (		self			) ->							Any:
				A:      FlaxMarianMTModel														=   FlaxMarianMTModel.from_pretrained(self.model_name			)
				self.assertEqual(model.config.decoder_start_token_id					,   model.config.pad_token_id			)
				return model


	def      a__ (		self					,   **A			) ->							Union[str, Any]:
				A:      List[Any]														=   self.translate_src_text(**A			)
				self.assertListEqual(self.expected_text					,   A			)





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







@require_flax
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE__				(   A__					):
	"""simple docstring"""


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


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







@require_flax
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE__				(   A__					):
	"""simple docstring"""


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


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







@require_flax
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE__				(   A__					):
	"""simple docstring"""


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


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







@require_flax
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE__				(   A__					):
	"""simple docstring"""


	A__    :    List[str]						=				'''en'''
	A__    :    Any						=				'''de'''
	A__    :    str						=				[
	    '''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__    :    Dict						=				[
	    '''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      a__ (		self			) ->							Optional[int]:
				self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE__				(   A__					):
	"""simple docstring"""


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


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







@require_flax
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE__				(   A__					):
	"""simple docstring"""


	A__    :    int						=				'''ru'''
	A__    :    List[Any]						=				'''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      a__ (		self			) ->							Optional[Any]:
				self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE__				(   A__					):
	"""simple docstring"""


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










'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__SCREAMING_SNAKE_CASE :      int             =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE :      Optional[int]             ={
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}


class   SCREAMING_SNAKE_CASE__				(   snake_case_     ,			snake_case_					):
	"""simple docstring"""


	A__    :    List[str]						=				'''convnext'''


	def __init__(		self					,   A=3					,   A=4					,   A=4					,   A=None					,   A=None					,   A="gelu"					,   A=0.02					,   A=1e-12					,   A=1e-6					,   A=0.0					,   A=2_24					,   A=None					,   A=None					,   **A					,   ) ->							Dict:
				super().__init__(**A			)

				A:      List[str]														=   num_channels
				A:      Optional[Any]														=   patch_size
				A:      List[str]														=   num_stages
				A:      Tuple														=   [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
				A:      List[Any]														=   [3, 3, 9, 3] if depths is None else depths
				A:      int														=   hidden_act
				A:      int														=   initializer_range
				A:      Optional[int]														=   layer_norm_eps
				A:      Tuple														=   layer_scale_init_value
				A:      int														=   drop_path_rate
				A:      Tuple														=   image_size
				A:      Union[str, Any]														=   ["""stem"""] + [f'stage{idx}' for idx in range(1					,   len(self.depths			) + 1			)]
				A							,		A:      List[str]														=   get_aligned_output_features_output_indices(
				    out_features=A					,   out_indices=A					,   stage_names=self.stage_names			)







class   SCREAMING_SNAKE_CASE__				(   snake_case_					):
	"""simple docstring"""


	A__    :    Any						=				version.parse('''1.11'''					)


	@property
	def      a__ (		self			) ->							Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
				    ]			)





	@property
	def      a__ (		self			) ->							float:
				return 1e-5





