
'''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
						UpperCAmelCase            = "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   A			(							A_				:       str     ,				A_				:       str     ,				A_				:       int=None     ,				A_				:       Optional[Any]=None     ,				A_				:       Tuple=None     ,				A_				:       List[str]=None     ,				A_				:       Optional[int]=None     ,				A_				:       Tuple=None     ,				):
						if attention_mask is None:
												snake_case							:  str			=     np.where(input_ids != config.pad_token_id     ,				1     ,				0							)
						if decoder_attention_mask is None:
												snake_case							:  Any			=     np.where(decoder_input_ids != config.pad_token_id     ,				1     ,				0							)
						if head_mask is None:
												snake_case							:  Optional[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							:  Tuple			=     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 :




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




				def 						__snake_case				(				self						:					Optional[Any]   ):
										snake_case							:  Any			=     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							:  Tuple			=     shift_tokens_right(SCREAMING_SNAKE_CASE_,     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=SCREAMING_SNAKE_CASE_,     )
										snake_case							:  Optional[Any]			=     prepare_marian_inputs_dict(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										return config, inputs_dict




				def 						__snake_case				(				self						:					List[Any]   ):
										snake_case,       snake_case							:  Optional[int]			=     self.prepare_config_and_inputs()
										return config, inputs_dict




				def 						__snake_case				(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										snake_case							:  List[Any]			=     20
										snake_case							:  List[str]			=     model_class_name(SCREAMING_SNAKE_CASE_   )

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

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

										snake_case							:  str			=     model.init_cache(decoder_input_ids.shape[0],     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										snake_case							:  Dict			=     jnp.ones((decoder_input_ids.shape[0], max_decoder_length),     dtype='''i4'''   )

										snake_case							:  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),     )
										snake_case							:  Optional[Any]			=     model.decode(
										    decoder_input_ids[:, :-1],     SCREAMING_SNAKE_CASE_,     decoder_attention_mask=SCREAMING_SNAKE_CASE_,     past_key_values=SCREAMING_SNAKE_CASE_,     decoder_position_ids=SCREAMING_SNAKE_CASE_,     )

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

										snake_case							:  Union[str, Any]			=     model.decode(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )

										snake_case							:  Union[str, 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 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Dict   ):
										snake_case							:  Optional[Any]			=     20
										snake_case							:  str			=     model_class_name(SCREAMING_SNAKE_CASE_   )

										snake_case							:  str			=     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							:  List[str]			=     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],     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										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							:  Tuple			=     model.decode(
										    decoder_input_ids[:, :-1],     SCREAMING_SNAKE_CASE_,     decoder_attention_mask=SCREAMING_SNAKE_CASE_,     past_key_values=SCREAMING_SNAKE_CASE_,     decoder_position_ids=SCREAMING_SNAKE_CASE_,     )
										snake_case							:  Any			=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],     dtype='''i4'''   )
										snake_case							:  Optional[int]			=     model.decode(
										    decoder_input_ids[:, -1:],     SCREAMING_SNAKE_CASE_,     past_key_values=outputs_cache.past_key_values,     decoder_attention_mask=SCREAMING_SNAKE_CASE_,     decoder_position_ids=SCREAMING_SNAKE_CASE_,     )

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

										snake_case							:  Dict			=     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 (					__magic_name__					,unittest.TestCase					,__magic_name__     ):
				_snake_case		     =			True
				_snake_case		     =			(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
				_snake_case		     =			(FlaxMarianMTModel,) if is_flax_available() else ()




				def 						__snake_case				(				self						:					List[str]   ):
										snake_case							:  str			=     FlaxMarianModelTester(self   )




				def 						__snake_case				(				self						:					List[Any]   ):
										snake_case,       snake_case							:  int			=     self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Dict   ):
										snake_case,       snake_case							:  List[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(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Dict   ):
										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							:  str			=     self._prepare_for_class(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
																						snake_case							:  int			=     model_class(SCREAMING_SNAKE_CASE_   )

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

																						with self.subTest('''JIT Enabled'''   ):
																												snake_case							:  int			=     encode_jitted(**SCREAMING_SNAKE_CASE_   ).to_tuple()

																						with self.subTest('''JIT Disabled'''   ):
																												with jax.disable_jit():
																																		snake_case							:  Tuple			=     encode_jitted(**SCREAMING_SNAKE_CASE_   ).to_tuple()

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




				def 						__snake_case				(				self						:					Union[str, 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							:  str			=     model_class(SCREAMING_SNAKE_CASE_   )
																						snake_case							:  Tuple			=     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(SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					int   ):
																												return model.decode(
																												    decoder_input_ids=SCREAMING_SNAKE_CASE_,     decoder_attention_mask=SCREAMING_SNAKE_CASE_,     encoder_outputs=SCREAMING_SNAKE_CASE_,     )

																						with self.subTest('''JIT Enabled'''   ):
																												snake_case							:  List[Any]			=     decode_jitted(**SCREAMING_SNAKE_CASE_   ).to_tuple()

																						with self.subTest('''JIT Disabled'''   ):
																												with jax.disable_jit():
																																		snake_case							:  List[Any]			=     decode_jitted(**SCREAMING_SNAKE_CASE_   ).to_tuple()

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




				@slow
				def 						__snake_case				(				self						:					List[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							:  List[str]			=     np.ones((1, 1)   ) * model.config.eos_token_id
																snake_case							:  Union[str, Any]			=     model(SCREAMING_SNAKE_CASE_   )
																self.assertIsNotNone(SCREAMING_SNAKE_CASE_   )




@require_flax
@require_sentencepiece
@require_tokenizers
class     a (					unittest.TestCase     ):
				_snake_case		     =			None
				_snake_case		     =			None




				@classmethod
				def 						__snake_case				(				cls						:					List[Any]   ):
										snake_case							:  Tuple			=     F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
										return cls




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




				@property
				def 						__snake_case				(				self						:					List[str]   ):
										return self.tokenizer.eos_token_id




				@cached_property
				def 						__snake_case				(				self						:					Tuple   ):
										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 						__snake_case				(				self						:					str,     **SCREAMING_SNAKE_CASE_						:					int   ):
										snake_case							:  Optional[int]			=     self.translate_src_text(**SCREAMING_SNAKE_CASE_   )
										self.assertListEqual(self.expected_text,     SCREAMING_SNAKE_CASE_   )




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




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




				@slow
				def 						__snake_case				(				self						:					Any   ):
										self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class     a (					__magic_name__     ):
				_snake_case		     =			'''fr'''
				_snake_case		     =			'''en'''
				_snake_case		     =			[
				    '''Donnez moi le micro.''',
				    '''Tom et Mary étaient assis à une table.''',  # Accents
				]
				_snake_case		     =			[
				    '''Give me the microphone.''',
				    '''Tom and Mary were sitting at a table.''',
				]




				@slow
				def 						__snake_case				(				self						:					Any   ):
										self._assert_generated_batch_equal_expected()




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




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




@require_flax
@require_sentencepiece
@require_tokenizers
class     a (					__magic_name__     ):
				_snake_case		     =			'''en'''
				_snake_case		     =			'''de'''
				_snake_case		     =			[
				    '''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.''',
				]
				_snake_case		     =			[
				    '''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 						__snake_case				(				self						:					Tuple   ):
										self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class     a (					__magic_name__     ):
				_snake_case		     =			'''en'''
				_snake_case		     =			'''zh'''
				_snake_case		     =			['''My name is Wolfgang and I live in Berlin''']
				_snake_case		     =			['''我叫沃尔夫冈 我住在柏林''']




				@slow
				def 						__snake_case				(				self						:					int   ):
										self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class     a (					__magic_name__     ):
				_snake_case		     =			'''ru'''
				_snake_case		     =			'''fr'''
				_snake_case		     =			['''Он показал мне рукопись своей новой пьесы.''']
				_snake_case		     =			['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']




				@slow
				def 						__snake_case				(				self						:					int   ):
										self._assert_generated_batch_equal_expected()




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




				@slow
				def 						__snake_case				(				self						:					Optional[Any]   ):
										self._assert_generated_batch_equal_expected()








'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_  :							Any            =	{
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							str            =	[
					    "DPRContextEncoderTokenizerFast",
					    "DPRQuestionEncoderTokenizerFast",
					    "DPRReaderTokenizerFast",
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Optional[int]            =	[
					    "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPRContextEncoder",
					    "DPRPretrainedContextEncoder",
					    "DPRPreTrainedModel",
					    "DPRPretrainedQuestionEncoder",
					    "DPRPretrainedReader",
					    "DPRQuestionEncoder",
					    "DPRReader",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Dict            =	[
					    "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFDPRContextEncoder",
					    "TFDPRPretrainedContextEncoder",
					    "TFDPRPretrainedQuestionEncoder",
					    "TFDPRPretrainedReader",
					    "TFDPRQuestionEncoder",
					    "TFDPRReader",
					]


if TYPE_CHECKING:
					from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
					from .tokenization_dpr import (
					    DPRContextEncoderTokenizer,
					    DPRQuestionEncoderTokenizer,
					    DPRReaderOutput,
					    DPRReaderTokenizer,
					)

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_dpr_fast import (
										    DPRContextEncoderTokenizerFast,
										    DPRQuestionEncoderTokenizerFast,
										    DPRReaderTokenizerFast,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpr import (
										    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPRContextEncoder,
										    DPRPretrainedContextEncoder,
										    DPRPreTrainedModel,
										    DPRPretrainedQuestionEncoder,
										    DPRPretrainedReader,
										    DPRQuestionEncoder,
										    DPRReader,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_dpr import (
										    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDPRContextEncoder,
										    TFDPRPretrainedContextEncoder,
										    TFDPRPretrainedQuestionEncoder,
										    TFDPRPretrainedReader,
										    TFDPRQuestionEncoder,
										    TFDPRReader,
										)

else:
					import sys

					snake_case_  :							List[str]            =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





