



"""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_								=			"""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 (  lowercase_						,      lowercase_						,      lowercase_=None						,      lowercase_=None						,      lowercase_=None						,      lowercase_=None						,      lowercase_=None						,      lowercase_=None						,      ):
						if attention_mask is None:
												UpperCAmelCase			 =					np.where(input_ids != config.pad_token_id						,      1						,      0		)
						if decoder_attention_mask is None:
												UpperCAmelCase			 =					np.where(decoder_input_ids != config.pad_token_id						,      1						,      0		)
						if head_mask is None:
												UpperCAmelCase			 =					np.ones((config.encoder_layers, config.encoder_attention_heads)		)
						if decoder_head_mask is None:
												UpperCAmelCase			 =					np.ones((config.decoder_layers, config.decoder_attention_heads)		)
						if cross_attn_head_mask is None:
												UpperCAmelCase			 =					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  A_     :



						"""simple docstring"""






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






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

												UpperCAmelCase			 =					shift_tokens_right(lowercase_  ,    1  ,    2   )

												UpperCAmelCase			 =					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=lowercase_  ,    )
												UpperCAmelCase			 =					prepare_marian_inputs_dict(lowercase_  ,    lowercase_  ,    lowercase_   )
												return config, inputs_dict






						def       UpperCAmelCase__	(   self					:Tuple   )							->     Any:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.prepare_config_and_inputs()
												return config, inputs_dict






						def       UpperCAmelCase__	(   self					:Tuple  ,    lowercase_					:List[Any]  ,    lowercase_					:Any  ,    lowercase_					:str   )							->     Tuple:
												UpperCAmelCase			 =					20
												UpperCAmelCase			 =					model_class_name(lowercase_   )

												UpperCAmelCase			 =					model.encode(inputs_dict['input_ids']   )

												UpperCAmelCase ,       UpperCAmelCase			 =					(
												    inputs_dict['decoder_input_ids'],
												    inputs_dict['decoder_attention_mask'],
												)

												UpperCAmelCase			 =					model.init_cache(decoder_input_ids.shape[0]  ,    lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,    dtype='i4'   )

												UpperCAmelCase			 =					jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]  ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,    )
												UpperCAmelCase			 =					model.decode(
												    decoder_input_ids[:, :-1]  ,    lowercase_  ,    decoder_attention_mask=lowercase_  ,    past_key_values=lowercase_  ,    decoder_position_ids=lowercase_  ,    )

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

												UpperCAmelCase			 =					model.decode(lowercase_  ,    lowercase_   )

												UpperCAmelCase			 =					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       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:List[Any]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Any   )							->     Optional[Any]:
												UpperCAmelCase			 =					20
												UpperCAmelCase			 =					model_class_name(lowercase_   )

												UpperCAmelCase			 =					model.encode(inputs_dict['input_ids']   )

												UpperCAmelCase ,       UpperCAmelCase			 =					(
												    inputs_dict['decoder_input_ids'],
												    inputs_dict['decoder_attention_mask'],
												)

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

												UpperCAmelCase			 =					model.init_cache(decoder_input_ids.shape[0]  ,    lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]  ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,    )

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

												UpperCAmelCase			 =					model.decode(lowercase_  ,    lowercase_  ,    decoder_attention_mask=lowercase_   )

												UpperCAmelCase			 =					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  A_     (	SCREAMING_SNAKE_CASE_      ,  unittest.TestCase      ,  SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				True
						__UpperCamelCase													=				(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
						__UpperCamelCase													=				(FlaxMarianMTModel,) if is_flax_available() else ()






						def       UpperCAmelCase__	(   self					:List[str]   )							->     Tuple:
												UpperCAmelCase			 =					FlaxMarianModelTester(self   )






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     Optional[Any]:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(lowercase_  ,    lowercase_  ,    lowercase_   )






						def       UpperCAmelCase__	(   self					:List[str]   )							->     str:
												UpperCAmelCase ,       UpperCAmelCase			 =					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(lowercase_  ,    lowercase_  ,    lowercase_   )






						def       UpperCAmelCase__	(   self					:Dict   )							->     Tuple:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__   ):
																								UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_   )
																								UpperCAmelCase			 =					model_class(lowercase_   )

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

																								with self.subTest('JIT Enabled'   ):
																														UpperCAmelCase			 =					encode_jitted(**lowercase_   ).to_tuple()

																								with self.subTest('JIT Disabled'   ):
																														with jax.disable_jit():
																																				UpperCAmelCase			 =					encode_jitted(**lowercase_   ).to_tuple()

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






						def       UpperCAmelCase__	(   self					:List[str]   )							->     List[str]:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__   ):
																								UpperCAmelCase			 =					model_class(lowercase_   )
																								UpperCAmelCase			 =					model.encode(inputs_dict['input_ids']  ,    inputs_dict['attention_mask']   )

																								UpperCAmelCase			 =					{
																								    '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(lowercase_					:List[str]  ,    lowercase_					:Any  ,    lowercase_					:int   ):
																														return model.decode(
																														    decoder_input_ids=lowercase_  ,    decoder_attention_mask=lowercase_  ,    encoder_outputs=lowercase_  ,    )

																								with self.subTest('JIT Enabled'   ):
																														UpperCAmelCase			 =					decode_jitted(**lowercase_   ).to_tuple()

																								with self.subTest('JIT Disabled'   ):
																														with jax.disable_jit():
																																				UpperCAmelCase			 =					decode_jitted(**lowercase_   ).to_tuple()

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






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







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



						"""simple docstring"""
						__UpperCamelCase													=				None
						__UpperCamelCase													=				None






						@classmethod
						def       UpperCAmelCase__	(   cls					:Any   )							->     None:
												UpperCAmelCase			 =					f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
												return cls






						@cached_property
						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     Tuple:
												return MarianTokenizer.from_pretrained(self.model_name   )






						@property
						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     int:
												return self.tokenizer.eos_token_id






						@cached_property
						def       UpperCAmelCase__	(   self					:Dict   )							->     Union[str, Any]:
												UpperCAmelCase			 =					FlaxMarianMTModel.from_pretrained(self.model_name   )
												self.assertEqual(model.config.decoder_start_token_id  ,    model.config.pad_token_id   )
												return model






						def       UpperCAmelCase__	(   self					:List[str]  ,    **lowercase_					:int   )							->     Union[str, Any]:
												UpperCAmelCase			 =					self.translate_src_text(**lowercase_   )
												self.assertListEqual(self.expected_text  ,    lowercase_   )






						def       UpperCAmelCase__	(   self					:Dict  ,    **lowercase_					:Tuple   )							->     List[Any]:
												UpperCAmelCase			 =					self.tokenizer(self.src_text  ,    padding=lowercase_  ,    return_tensors='np'  ,    **lowercase_   )
												UpperCAmelCase			 =					self.model.generate(
												    model_inputs.input_ids  ,    attention_mask=model_inputs.attention_mask  ,    num_beams=2  ,    max_length=1_28  ,    ).sequences
												UpperCAmelCase			 =					self.tokenizer.batch_decode(lowercase_  ,    skip_special_tokens=lowercase_   )
												return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__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       UpperCAmelCase__	(   self					:List[Any]   )							->     List[str]:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__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       UpperCAmelCase__	(   self					:Optional[int]   )							->     int:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__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       UpperCAmelCase__	(   self					:List[Any]   )							->     Optional[Any]:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__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       UpperCAmelCase__	(   self					:Optional[Any]   )							->     Union[str, Any]:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__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       UpperCAmelCase__	(   self					:Any   )							->     Union[str, Any]:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__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       UpperCAmelCase__	(   self					:str   )							->     Any:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__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       UpperCAmelCase__	(   self					:List[str]   )							->     Any:
												self._assert_generated_batch_equal_expected()











import requests

try:
							from django.contrib.auth import authenticate, login
							from django.shortcuts import redirect, render
except ImportError:
							UpperCamelCase__						=		UpperCamelCase__						=		UpperCamelCase__						=		UpperCamelCase__						=		print






def    snake_case   (				A     )				->				List[str]:




	'''simple docstring'''


	lowerCamelCase_				:						Optional[int]				  =				'secretKey'
	lowerCamelCase_				:						str				  =				'https://www.google.com/recaptcha/api/siteverify'

	# when method is not POST, direct user to login page
	if request.method != "POST":
		return render(A		,  'login.html'     )

	# from the frontend, get username, password, and client_key
	lowerCamelCase_				:						int				  =				request.POST.get('username'     )
	lowerCamelCase_				:						str				  =				request.POST.get('password'     )
	lowerCamelCase_				:						List[Any]				  =				request.POST.get('g-recaptcha-response'     )

	# post recaptcha response to Google's recaptcha api
	lowerCamelCase_				:						Union[str, Any]				  =				requests.post(A		,  data={'secret': secret_key, 'response': client_key}     )
	# if the recaptcha api verified our keys
	if response.json().get('success'		,  A     ):
		# authenticate the user
		lowerCamelCase_				:						int				  =				authenticate(A		,  username=A		,  password=A     )
		if user_in_database:
			login(A		,  A     )
			return redirect('/your-webpage'     )
	return render(A		,  'login.html'     )
