
'''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 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__=None      ,    SCREAMING_SNAKE_CASE__=None      ,    SCREAMING_SNAKE_CASE__=None      ,    SCREAMING_SNAKE_CASE__=None      ,    SCREAMING_SNAKE_CASE__=None      ,    SCREAMING_SNAKE_CASE__=None      ,    )   ->				Optional[int]:


		'''simple docstring'''


		if attention_mask is None:
				snake_case   :    int			       =		np.where(input_ids != config.pad_token_id      ,    1      ,    0       )
		if decoder_attention_mask is None:
				snake_case   :    str			       =		np.where(decoder_input_ids != config.pad_token_id      ,    1      ,    0       )
		if head_mask is None:
				snake_case   :    Any			       =		np.ones((config.encoder_layers, config.encoder_attention_heads)       )
		if decoder_head_mask is None:
				snake_case   :    Dict			       =		np.ones((config.decoder_layers, config.decoder_attention_heads)       )
		if cross_attn_head_mask is None:
				snake_case   :    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      snake_case__    :







		"""simple docstring"""

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






				"""simple docstring"""







				snake_case   :    List[Any]			       =		parent
				snake_case   :    Dict			       =		batch_size
				snake_case   :    str			       =		seq_length
				snake_case   :    Optional[int]			       =		is_training
				snake_case   :    Any			       =		use_labels
				snake_case   :    List[Any]			       =		vocab_size
				snake_case   :    int			       =		hidden_size
				snake_case   :    Optional[Any]			       =		num_hidden_layers
				snake_case   :    int			       =		num_attention_heads
				snake_case   :    int			       =		intermediate_size
				snake_case   :    Union[str, Any]			       =		hidden_act
				snake_case   :    Any			       =		hidden_dropout_prob
				snake_case   :    int			       =		attention_probs_dropout_prob
				snake_case   :    str			       =		max_position_embeddings
				snake_case   :    List[str]			       =		eos_token_id
				snake_case   :    Any			       =		pad_token_id
				snake_case   :    Dict			       =		bos_token_id
				snake_case   :    List[str]			       =		initializer_range



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       str:






				"""simple docstring"""







				snake_case   :    Dict			       =		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,     self.vocab_size	)   ,     3   ,     self.vocab_size	)
				snake_case   :    List[Any]			       =		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   :    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   ,     )
				snake_case   :    Dict			       =		prepare_marian_inputs_dict(__A   ,     __A   ,     __A	)
				return config, inputs_dict



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Union[str, Any]:






				"""simple docstring"""







				snake_case       ,snake_case   :    Any			       =		self.prepare_config_and_inputs()
				return config, inputs_dict



		def 	lowerCAmelCase							(	self							:       Any   ,     UpperCamelCase__							:       Any   ,     UpperCamelCase__							:       List[Any]   ,     UpperCamelCase__							:       Optional[int]	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		20
				snake_case   :    int			       =		model_class_name(__A	)

				snake_case   :    int			       =		model.encode(inputs_dict['''input_ids''']	)

				snake_case       ,snake_case   :    Any			       =		(
				    inputs_dict['''decoder_input_ids'''],
				    inputs_dict['''decoder_attention_mask'''],
				)

				snake_case   :    Optional[int]			       =		model.init_cache(decoder_input_ids.shape[0]   ,     __A   ,     __A	)
				snake_case   :    Tuple			       =		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,     dtype='''i4'''	)

				snake_case   :    List[Any]			       =		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   :    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   :    List[str]			       =		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   :    Union[str, Any]			       =		model.decode(__A   ,     __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}'	)



		def 	lowerCAmelCase							(	self							:       Optional[int]   ,     UpperCamelCase__							:       Optional[int]   ,     UpperCamelCase__							:       List[str]   ,     UpperCamelCase__							:       int	)		->       str:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		20
				snake_case   :    Tuple			       =		model_class_name(__A	)

				snake_case   :    int			       =		model.encode(inputs_dict['''input_ids''']	)

				snake_case       ,snake_case   :    Union[str, Any]			       =		(
				    inputs_dict['''decoder_input_ids'''],
				    inputs_dict['''decoder_attention_mask'''],
				)

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

				snake_case   :    List[Any]			       =		model.init_cache(decoder_input_ids.shape[0]   ,     __A   ,     __A	)
				snake_case   :    Union[str, Any]			       =		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   :    List[str]			       =		model.decode(
				    decoder_input_ids[:, :-1]   ,     __A   ,     decoder_attention_mask=__A   ,     past_key_values=__A   ,     decoder_position_ids=__A   ,     )
				snake_case   :    int			       =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,     dtype='''i4'''	)
				snake_case   :    List[str]			       =		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   :    Optional[int]			       =		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      snake_case__    (	UpperCamelCase__  , unittest.TestCase  , UpperCamelCase__ ):







		"""simple docstring"""

		lowerCamelCase							=		True
		lowerCamelCase							=		(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
		lowerCamelCase							=		(FlaxMarianMTModel,) if is_flax_available() else ()



		def 	lowerCAmelCase							(	self							:       Any	)		->       str:






				"""simple docstring"""







				snake_case   :    str			       =		FlaxMarianModelTester(self	)



		def 	lowerCAmelCase							(	self							:       Any	)		->       Tuple:






				"""simple docstring"""







				snake_case       ,snake_case   :    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							:       Union[str, Any]	)		->       List[Any]:






				"""simple docstring"""







				snake_case       ,snake_case   :    Optional[Any]			       =		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							:       List[Any]	)		->       Any:






				"""simple docstring"""







				snake_case       ,snake_case   :    List[str]			       =		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   :    Dict			       =		self._prepare_for_class(__A   ,     __A	)
								snake_case   :    int			       =		model_class(__A	)

								@jax.jit
								def encode_jitted(UpperCamelCase__							:       List[str]   ,     UpperCamelCase__							:       List[str]=None   ,     **UpperCamelCase__							:       Tuple	):
										return model.encode(input_ids=__A   ,     attention_mask=__A	)

								with self.subTest('''JIT Enabled'''	):
										snake_case   :    List[str]			       =		encode_jitted(**__A	).to_tuple()

								with self.subTest('''JIT Disabled'''	):
										with jax.disable_jit():
												snake_case   :    int			       =		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							:       Tuple	)		->       Optional[int]:






				"""simple docstring"""







				snake_case       ,snake_case   :    Any			       =		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   :    List[str]			       =		model_class(__A	)
								snake_case   :    List[str]			       =		model.encode(inputs_dict['''input_ids''']   ,     inputs_dict['''attention_mask''']	)

								snake_case   :    Dict			       =		{
								    '''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__							:       Union[str, Any]   ,     UpperCamelCase__							:       Optional[Any]   ,     UpperCamelCase__							:       List[Any]	):
										return model.decode(
										    decoder_input_ids=__A   ,     decoder_attention_mask=__A   ,     encoder_outputs=__A   ,     )

								with self.subTest('''JIT Enabled'''	):
										snake_case   :    Any			       =		decode_jitted(**__A	).to_tuple()

								with self.subTest('''JIT Disabled'''	):
										with jax.disable_jit():
												snake_case   :    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 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       str:






				"""simple docstring"""







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







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







		"""simple docstring"""

		lowerCamelCase							=		None
		lowerCamelCase							=		None



		@classmethod
		def 	lowerCAmelCase							(	cls							:       int	)		->       None:






				"""simple docstring"""







				snake_case   :    str			       =		f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
				return cls



		@cached_property
		def 	lowerCAmelCase							(	self							:       Any	)		->       int:






				"""simple docstring"""







				return MarianTokenizer.from_pretrained(self.model_name	)



		@property
		def 	lowerCAmelCase							(	self							:       Optional[int]	)		->       int:






				"""simple docstring"""







				return self.tokenizer.eos_token_id



		@cached_property
		def 	lowerCAmelCase							(	self							:       Optional[int]	)		->       str:






				"""simple docstring"""







				snake_case   :    str			       =		FlaxMarianMTModel.from_pretrained(self.model_name	)
				self.assertEqual(model.config.decoder_start_token_id   ,     model.config.pad_token_id	)
				return model



		def 	lowerCAmelCase							(	self							:       Optional[int]   ,     **UpperCamelCase__							:       int	)		->       Any:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		self.translate_src_text(**__A	)
				self.assertListEqual(self.expected_text   ,     __A	)



		def 	lowerCAmelCase							(	self							:       Union[str, Any]   ,     **UpperCamelCase__							:       List[Any]	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    Any			       =		self.tokenizer(self.src_text   ,     padding=__A   ,     return_tensors='''np'''   ,     **__A	)
				snake_case   :    List[Any]			       =		self.model.generate(
				    model_inputs.input_ids   ,     attention_mask=model_inputs.attention_mask   ,     num_beams=2   ,     max_length=128   ,     ).sequences
				snake_case   :    Any			       =		self.tokenizer.batch_decode(__A   ,     skip_special_tokens=__A	)
				return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class      snake_case__    (	UpperCamelCase__ ):







		"""simple docstring"""

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



		@slow
		def 	lowerCAmelCase							(	self							:       str	)		->       Optional[Any]:






				"""simple docstring"""







				self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class      snake_case__    (	UpperCamelCase__ ):







		"""simple docstring"""

		lowerCamelCase							=		"""fr"""
		lowerCamelCase							=		"""en"""
		lowerCamelCase							=		[
		    """Donnez moi le micro.""",
		    """Tom et Mary étaient assis à une table.""",  # Accents
		]
		lowerCamelCase							=		[
		    """Give me the microphone.""",
		    """Tom and Mary were sitting at a table.""",
		]



		@slow
		def 	lowerCAmelCase							(	self							:       Any	)		->       Tuple:






				"""simple docstring"""







				self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class      snake_case__    (	UpperCamelCase__ ):







		"""simple docstring"""

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



		@slow
		def 	lowerCAmelCase							(	self							:       List[str]	)		->       int:






				"""simple docstring"""







				self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class      snake_case__    (	UpperCamelCase__ ):







		"""simple docstring"""

		lowerCamelCase							=		"""en"""
		lowerCamelCase							=		"""de"""
		lowerCamelCase							=		[
		    """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.""",
		]
		lowerCamelCase							=		[
		    """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							:       List[Any]	)		->       List[str]:






				"""simple docstring"""







				self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class      snake_case__    (	UpperCamelCase__ ):







		"""simple docstring"""

		lowerCamelCase							=		"""en"""
		lowerCamelCase							=		"""zh"""
		lowerCamelCase							=		["""My name is Wolfgang and I live in Berlin"""]
		lowerCamelCase							=		["""我叫沃尔夫冈 我住在柏林"""]



		@slow
		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       int:






				"""simple docstring"""







				self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class      snake_case__    (	UpperCamelCase__ ):







		"""simple docstring"""

		lowerCamelCase							=		"""ru"""
		lowerCamelCase							=		"""fr"""
		lowerCamelCase							=		["""Он показал мне рукопись своей новой пьесы."""]
		lowerCamelCase							=		["""Il m'a montré le manuscrit de sa nouvelle pièce."""]



		@slow
		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       List[str]:






				"""simple docstring"""







				self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class      snake_case__    (	UpperCamelCase__ ):







		"""simple docstring"""

		lowerCamelCase							=		"""en"""
		lowerCamelCase							=		"""ROMANCE"""
		lowerCamelCase							=		[
		    """>>fr<< Don't spend so much time watching TV.""",
		    """>>pt<< Your message has been sent.""",
		    """>>es<< He's two years older than me.""",
		]
		lowerCamelCase							=		[
		    """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							:       str	)		->       int:






				"""simple docstring"""







				self._assert_generated_batch_equal_expected()









from datetime import date

import requests

_lowerCamelCase       =	'https://api.carbonintensity.org.uk/intensity'






def  SCREAMING_SNAKE_CASE__							(				):
		SCREAMING_SNAKE_CASE__								=					requests.get(UpperCamelCase__			).json()["""data"""][0]
		return last_half_hour["intensity"]["actual"]






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Dict    ,      UpperCamelCase__:							Optional[int]			):
		return requests.get(f'''{BASE_URL}/{start}/{end}'''			).json()["data"]


if __name__ == "__main__":
	for entry in fetch_from_to(start=date(2020, 10, 1), end=date(2020, 10, 3)):
		print('from {from} to {to}: {intensity[actual]}'.format(**entry))
	print(F'''{fetch_last_half_hour() = }''')