



import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
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
  a__    =	"""platform"""
  import jax
  import jax.numpy as jnp
  import numpy as np

  from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel







@require_flax
class 		snake_case						:



   '''simple docstring'''






   snake_case_					: Optional[Any]          =		PegasusConfig
   snake_case_					: Optional[Any]          =		{}
   snake_case_					: str          =		"""gelu"""

   def __init__(					self							:			List[Any]				,		lowerCAmelCase							:			List[Any]				,		lowerCAmelCase							:			str=13				,		lowerCAmelCase							:			Union[str, Any]=7				,		lowerCAmelCase							:			Optional[int]=True				,		lowerCAmelCase							:			Any=False				,		lowerCAmelCase							:			int=99				,		lowerCAmelCase							:			List[str]=32				,		lowerCAmelCase							:			Tuple=5				,		lowerCAmelCase							:			Optional[Any]=4				,		lowerCAmelCase							:			Union[str, Any]=37				,		lowerCAmelCase							:			List[Any]=0.1				,		lowerCAmelCase							:			Tuple=0.1				,		lowerCAmelCase							:			int=20				,		lowerCAmelCase							:			str=2				,		lowerCAmelCase							:			Any=1				,		lowerCAmelCase							:			Dict=0				,		)				->				List[Any]:

    """simple docstring"""







    _snake_case    :  str					=					parent
    _snake_case    :  str					=					batch_size
    _snake_case    :  List[str]					=					seq_length
    _snake_case    :  Union[str, Any]					=					is_training
    _snake_case    :  int					=					use_labels
    _snake_case    :  Any					=					vocab_size
    _snake_case    :  str					=					hidden_size
    _snake_case    :  int					=					num_hidden_layers
    _snake_case    :  Union[str, Any]					=					num_attention_heads
    _snake_case    :  List[Any]					=					intermediate_size

    _snake_case    :  str					=					hidden_dropout_prob
    _snake_case    :  Optional[Any]					=					attention_probs_dropout_prob
    _snake_case    :  int					=					max_position_embeddings
    _snake_case    :  List[Any]					=					eos_token_id
    _snake_case    :  Optional[Any]					=					pad_token_id
    _snake_case    :  Optional[Any]					=					bos_token_id

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

    """simple docstring"""







    _snake_case    :  List[str]					=					ids_tensor([self.batch_size, self.seq_length - 1]				,		self.vocab_size).clip(3				,		self.vocab_size)
    _snake_case    :  List[Any]					=					np.expand_dims(np.array([self.eos_token_id] * self.batch_size)				,		1)
    _snake_case    :  Optional[Any]					=					np.concatenate([input_ids, eos_tensor]				,		axis=1)

    _snake_case    :  Tuple					=					ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size)

    _snake_case    :  Optional[int]					=					self.config_cls(
        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_ids=[2]				,		bos_token_id=self.bos_token_id				,		pad_token_id=self.pad_token_id				,		decoder_start_token_id=self.pad_token_id				,		**self.config_updates				,		)
    _snake_case    :  Any					=					prepare_pegasus_inputs_dict(lowerCAmelCase				,		lowerCAmelCase				,		lowerCAmelCase)
    return config, inputs_dict

   def      UpperCamelCase_  (					self							:			List[str]				,		lowerCAmelCase							:			int				,		lowerCAmelCase							:			Dict				,		lowerCAmelCase							:			List[Any])				->				List[str]:

    """simple docstring"""







    _snake_case    :  Any					=					20
    _snake_case    :  Dict					=					model_class_name(lowerCAmelCase)

    _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    :  int					=					model.init_cache(decoder_input_ids.shape[0]				,		lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  int					=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,		dtype="""i4""")

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

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

    _snake_case    :  Dict					=					model.decode(lowerCAmelCase				,		lowerCAmelCase)

    _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}''')


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

    """simple docstring"""







    _snake_case    :  str					=					20
    _snake_case    :  Any					=					model_class_name(lowerCAmelCase)

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

    _snake_case    :  Tuple					=					model.init_cache(decoder_input_ids.shape[0]				,		lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  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)				,		)

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

    _snake_case    :  int					=					model.decode(lowerCAmelCase				,		lowerCAmelCase				,		decoder_attention_mask=lowerCAmelCase)

    _snake_case    :  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       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	Dict=None				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]=None				,       )  ->     Dict:
 if attention_mask is None:
  _snake_case    :  List[str]					=					np.not_equal(SCREAMING_SNAKE_CASE__				,       config.pad_token_id				).astype(np.inta				)
 if decoder_attention_mask is None:
  _snake_case    :  List[str]					=					np.concatenate(
      [
          np.ones(decoder_input_ids[:, :1].shape				,       dtype=np.inta				),
          np.not_equal(decoder_input_ids[:, 1:]				,       config.pad_token_id				).astype(np.inta				),
      ]				,       axis=-1				,       )
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": decoder_attention_mask,
 }







@require_flax
class 		snake_case						(	SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		(
       (
           FlaxPegasusForConditionalGeneration,
           FlaxPegasusModel,
       )
       if is_flax_available()
       else ()
   )
   snake_case_					: List[Any]          =		(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
   snake_case_					: int          =		True
   snake_case_					: str          =		False
   snake_case_					: int          =		False
   snake_case_					: Optional[int]          =		False

   def      UpperCamelCase_  (					self							:			Optional[int])				->				int:

    """simple docstring"""







    _snake_case    :  Any					=					FlaxPegasusModelTester(self)
    _snake_case    :  int					=					ConfigTester(self				,		config_class=lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Tuple)				->				Union[str, Any]:

    """simple docstring"""







    self.config_tester.run_common_tests()

   def      UpperCamelCase_  (					self							:			int)				->				Tuple:

    """simple docstring"""







    _snake_case	,    _snake_case    :  int					=					self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(lowerCAmelCase				,		lowerCAmelCase				,		lowerCAmelCase)

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

    """simple docstring"""







    _snake_case	,    _snake_case    :  Dict					=					self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase				,		lowerCAmelCase				,		lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				Optional[int]:

    """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    :  int					=					self._prepare_for_class(lowerCAmelCase				,		lowerCAmelCase)
      _snake_case    :  Optional[int]					=					model_class(lowerCAmelCase)

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

      with self.subTest("""JIT Enabled"""):
       _snake_case    :  Union[str, Any]					=					encode_jitted(**lowerCAmelCase).to_tuple()

      with self.subTest("""JIT Disabled"""):
       with jax.disable_jit():
        _snake_case    :  List[str]					=					encode_jitted(**lowerCAmelCase).to_tuple()

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

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

    """simple docstring"""







    _snake_case	,    _snake_case    :  int					=					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(lowerCAmelCase)
      _snake_case    :  Union[str, Any]					=					model.encode(inputs_dict["""input_ids"""]				,		inputs_dict["""attention_mask"""])

      _snake_case    :  List[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(lowerCAmelCase							:			int				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Optional[Any]):
       return model.decode(
           decoder_input_ids=lowerCAmelCase				,		decoder_attention_mask=lowerCAmelCase				,		encoder_outputs=lowerCAmelCase				,		)

      with self.subTest("""JIT Enabled"""):
       _snake_case    :  Optional[int]					=					decode_jitted(**lowerCAmelCase).to_tuple()

      with self.subTest("""JIT Disabled"""):
       with jax.disable_jit():
        _snake_case    :  int					=					decode_jitted(**lowerCAmelCase).to_tuple()

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

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

    """simple docstring"""







    for model_class_name in self.all_model_classes:
     _snake_case    :  int					=					model_class_name.from_pretrained("""google/pegasus-large"""				,		from_pt=lowerCAmelCase)
     _snake_case    :  List[str]					=					np.ones((1, 1))
     _snake_case    :  str					=					model(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)


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

    """simple docstring"""







    _snake_case    :  List[str]					=					FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""")
    _snake_case    :  List[Any]					=					PegasusTokenizer.from_pretrained("""google/pegasus-xsum""")

    _snake_case    :  str					=					[
        """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
        """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
    ]

    _snake_case    :  Optional[Any]					=					[
        """California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
        """Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
    ]

    _snake_case    :  Tuple					=					tokenizer(lowerCAmelCase				,		return_tensors="""np"""				,		truncation=lowerCAmelCase				,		max_length=512				,		padding=lowerCAmelCase)
    _snake_case    :  Tuple					=					model.generate(**lowerCAmelCase				,		num_beams=2).sequences
    _snake_case    :  Any					=					tokenizer.batch_decode(lowerCAmelCase				,		skip_special_tokens=lowerCAmelCase)
    assert tgt_text == decoded





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import numpy
  import tensorflow as tf

  from transformers import (
      TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BertConfig,
      DPRConfig,
      TFDPRContextEncoder,
      TFDPRQuestionEncoder,
      TFDPRReader,
  )







class 		snake_case						:



   '''simple docstring'''






   def __init__(					self							:			Optional[Any]				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Optional[Any]=13				,		lowerCAmelCase							:			int=7				,		lowerCAmelCase							:			Optional[int]=True				,		lowerCAmelCase							:			Optional[Any]=True				,		lowerCAmelCase							:			List[str]=True				,		lowerCAmelCase							:			str=True				,		lowerCAmelCase							:			Any=99				,		lowerCAmelCase							:			Dict=32				,		lowerCAmelCase							:			List[str]=2				,		lowerCAmelCase							:			str=4				,		lowerCAmelCase							:			List[str]=37				,		lowerCAmelCase							:			Dict="gelu"				,		lowerCAmelCase							:			Optional[int]=0.1				,		lowerCAmelCase							:			Any=0.1				,		lowerCAmelCase							:			List[Any]=512				,		lowerCAmelCase							:			Optional[int]=16				,		lowerCAmelCase							:			Dict=2				,		lowerCAmelCase							:			str=0.02				,		lowerCAmelCase							:			Optional[Any]=3				,		lowerCAmelCase							:			Optional[Any]=4				,		lowerCAmelCase							:			Tuple=None				,		lowerCAmelCase							:			List[Any]=0				,		)				->				int:

    """simple docstring"""







    _snake_case    :  Dict					=					parent
    _snake_case    :  int					=					batch_size
    _snake_case    :  str					=					seq_length
    _snake_case    :  List[str]					=					is_training
    _snake_case    :  Tuple					=					use_input_mask
    _snake_case    :  Optional[Any]					=					use_token_type_ids
    _snake_case    :  Any					=					use_labels
    _snake_case    :  str					=					vocab_size
    _snake_case    :  List[str]					=					hidden_size
    _snake_case    :  List[str]					=					num_hidden_layers
    _snake_case    :  Dict					=					num_attention_heads
    _snake_case    :  int					=					intermediate_size
    _snake_case    :  Any					=					hidden_act
    _snake_case    :  List[Any]					=					hidden_dropout_prob
    _snake_case    :  Tuple					=					attention_probs_dropout_prob
    _snake_case    :  List[Any]					=					max_position_embeddings
    _snake_case    :  int					=					type_vocab_size
    _snake_case    :  Dict					=					type_sequence_label_size
    _snake_case    :  List[str]					=					initializer_range
    _snake_case    :  Dict					=					num_labels
    _snake_case    :  Optional[Any]					=					num_choices
    _snake_case    :  Dict					=					scope
    _snake_case    :  List[Any]					=					projection_dim

   def      UpperCamelCase_  (					self							:			Dict)				->				List[str]:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size)

    _snake_case    :  List[str]					=					None
    if self.use_input_mask:
     # follow test_modeling_tf_ctrl.py
     _snake_case    :  List[Any]					=					random_attention_mask([self.batch_size, self.seq_length])

    _snake_case    :  str					=					None
    if self.use_token_type_ids:
     _snake_case    :  Optional[Any]					=					ids_tensor([self.batch_size, self.seq_length]				,		self.type_vocab_size)

    _snake_case    :  Tuple					=					None
    _snake_case    :  Any					=					None
    _snake_case    :  str					=					None
    if self.use_labels:
     _snake_case    :  Optional[Any]					=					ids_tensor([self.batch_size]				,		self.type_sequence_label_size)
     _snake_case    :  Union[str, Any]					=					ids_tensor([self.batch_size, self.seq_length]				,		self.num_labels)
     _snake_case    :  Optional[Any]					=					ids_tensor([self.batch_size]				,		self.num_choices)

    _snake_case    :  Union[str, Any]					=					BertConfig(
        vocab_size=self.vocab_size				,		hidden_size=self.hidden_size				,		num_hidden_layers=self.num_hidden_layers				,		num_attention_heads=self.num_attention_heads				,		intermediate_size=self.intermediate_size				,		hidden_act=self.hidden_act				,		hidden_dropout_prob=self.hidden_dropout_prob				,		attention_probs_dropout_prob=self.attention_probs_dropout_prob				,		max_position_embeddings=self.max_position_embeddings				,		type_vocab_size=self.type_vocab_size				,		is_decoder=lowerCAmelCase				,		initializer_range=self.initializer_range				,		)
    _snake_case    :  Optional[int]					=					DPRConfig(projection_dim=self.projection_dim				,		**config.to_dict())

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

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

    """simple docstring"""







    _snake_case    :  Dict					=					TFDPRContextEncoder(config=lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		token_type_ids=lowerCAmelCase)
    _snake_case    :  Optional[int]					=					model(lowerCAmelCase				,		token_type_ids=lowerCAmelCase)
    _snake_case    :  List[str]					=					model(lowerCAmelCase)
    self.parent.assertEqual(result.pooler_output.shape				,		(self.batch_size, self.projection_dim or self.hidden_size))

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

    """simple docstring"""







    _snake_case    :  Optional[int]					=					TFDPRQuestionEncoder(config=lowerCAmelCase)
    _snake_case    :  List[Any]					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		token_type_ids=lowerCAmelCase)
    _snake_case    :  List[Any]					=					model(lowerCAmelCase				,		token_type_ids=lowerCAmelCase)
    _snake_case    :  Tuple					=					model(lowerCAmelCase)
    self.parent.assertEqual(result.pooler_output.shape				,		(self.batch_size, self.projection_dim or self.hidden_size))

   def      UpperCamelCase_  (					self							:			List[str]				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Union[str, Any]				,		lowerCAmelCase							:			Dict				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			str)				->				Tuple:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					TFDPRReader(config=lowerCAmelCase)
    _snake_case    :  Any					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase)

    self.parent.assertEqual(result.start_logits.shape				,		(self.batch_size, self.seq_length))
    self.parent.assertEqual(result.end_logits.shape				,		(self.batch_size, self.seq_length))
    self.parent.assertEqual(result.relevance_logits.shape				,		(self.batch_size,))


   def      UpperCamelCase_  (					self							:			str)				->				Any:

    """simple docstring"""







    _snake_case    :  List[Any]					=					self.prepare_config_and_inputs()
    (
        (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    
    )    :  Union[str, Any]					=					config_and_inputs
    _snake_case    :  Union[str, Any]					=					{"""input_ids""": input_ids}
    return config, inputs_dict







@require_tf
class 		snake_case						(	SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   snake_case_					: Any          =		(
       (
           TFDPRContextEncoder,
           TFDPRQuestionEncoder,
           TFDPRReader,
       )
       if is_tf_available()
       else ()
   )
   snake_case_					: List[str]          =		{"""feature-extraction""": TFDPRQuestionEncoder} if is_tf_available() else {}

   snake_case_					: Tuple          =		False
   snake_case_					: str          =		False
   snake_case_					: Tuple          =		False
   snake_case_					: int          =		False
   snake_case_					: List[Any]          =		False

   def      UpperCamelCase_  (					self							:			Tuple)				->				str:

    """simple docstring"""







    _snake_case    :  List[str]					=					TFDPRModelTester(self)
    _snake_case    :  Dict					=					ConfigTester(self				,		config_class=lowerCAmelCase				,		hidden_size=37)

   def      UpperCamelCase_  (					self							:			List[Any])				->				Optional[Any]:

    """simple docstring"""







    self.config_tester.run_common_tests()

   def      UpperCamelCase_  (					self							:			List[str])				->				Optional[int]:

    """simple docstring"""







    _snake_case    :  Any					=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_dpr_context_encoder(*lowerCAmelCase)

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

    """simple docstring"""







    _snake_case    :  str					=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_dpr_question_encoder(*lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			List[str])				->				Tuple:

    """simple docstring"""







    _snake_case    :  str					=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_dpr_reader(*lowerCAmelCase)


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

    """simple docstring"""







    for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case    :  List[Any]					=					TFDPRContextEncoder.from_pretrained(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)

    for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case    :  List[str]					=					TFDPRContextEncoder.from_pretrained(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)

    for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case    :  Any					=					TFDPRQuestionEncoder.from_pretrained(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)

    for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case    :  Union[str, Any]					=					TFDPRReader.from_pretrained(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)







@require_tf
class 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






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

    """simple docstring"""







    _snake_case    :  List[str]					=					TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base""")

    _snake_case    :  str					=					tf.constant(
        [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]])  # [CLS] hello, is my dog cute? [SEP]
    _snake_case    :  Optional[int]					=					model(lowerCAmelCase)[0]  # embedding shape = (1, 768)
    # compare the actual values for a slice.
    _snake_case    :  Union[str, Any]					=					tf.constant(
        [
            [
                0.03_236_253,
                0.12_753_335,
                0.16_818_509,
                0.00_279_786,
                0.3_896_933,
                0.24_264_945,
                0.2_178_971,
                -0.02_335_227,
                -0.08_481_959,
                -0.14_324_117,
            ]
        ])
    self.assertTrue(numpy.allclose(output[:, :10].numpy()				,		expected_slice.numpy()				,		atol=1E-4))
