



'''simple docstring'''





import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )







class     UpperCAmelCase_       (       unittest.TestCase	):
    """simple docstring"""







    def __init__(    self					:     int ,     snake_case_					:     Tuple ,     snake_case_					:     Dict=13 ,     snake_case_					:     Optional[Any]=7 ,     snake_case_					:     List[Any]=True ,     snake_case_					:     int=True ,     snake_case_					:     Optional[Any]=True ,     snake_case_					:     Optional[Any]=True ,     snake_case_					:     Optional[Any]=99 ,     snake_case_					:     Any=32 ,     snake_case_					:     List[Any]=5 ,     snake_case_					:     Union[str, Any]=4 ,     snake_case_					:     Any=37 ,     snake_case_					:     int="gelu" ,     snake_case_					:     List[Any]=0.1 ,     snake_case_					:     str=0.1 ,     snake_case_					:     str=512 ,     snake_case_					:     int=16 ,     snake_case_					:     Dict=2 ,     snake_case_					:     Dict=0.02 ,     snake_case_					:     List[Any]=4 ,     ):
          snake_case__  :    List[Any]    						=      parent
          snake_case__  :    List[str]    						=      batch_size
          snake_case__  :    Tuple    						=      seq_length
          snake_case__  :    Dict    						=      is_training
          snake_case__  :    List[Any]    						=      use_attention_mask
          snake_case__  :    Optional[int]    						=      use_token_type_ids
          snake_case__  :    Union[str, Any]    						=      use_labels
          snake_case__  :    Optional[Any]    						=      vocab_size
          snake_case__  :    List[Any]    						=      hidden_size
          snake_case__  :    List[Any]    						=      num_hidden_layers
          snake_case__  :    Tuple    						=      num_attention_heads
          snake_case__  :    Dict    						=      intermediate_size
          snake_case__  :    List[Any]    						=      hidden_act
          snake_case__  :    Tuple    						=      hidden_dropout_prob
          snake_case__  :    Tuple    						=      attention_probs_dropout_prob
          snake_case__  :    str    						=      max_position_embeddings
          snake_case__  :    List[str]    						=      type_vocab_size
          snake_case__  :    Any    						=      type_sequence_label_size
          snake_case__  :    Optional[int]    						=      initializer_range
          snake_case__  :    int    						=      num_choices




    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    Optional[Any]    						=      ids_tensor([self.batch_size, self.seq_length] ,     self.vocab_size						)

          snake_case__  :    int    						=      None
          if self.use_attention_mask:
                snake_case__  :    List[str]    						=      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__  :    Optional[int]    						=      RoFormerConfig(
              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=snake_case_ ,     initializer_range=self.initializer_range ,     )

          return config, input_ids, token_type_ids, attention_mask




    def lowerCamelCase       (    self					:     str						):
          snake_case__  :    Tuple    						=      self.prepare_config_and_inputs()
          snake_case__						,				snake_case__						,				snake_case__						,				snake_case__  :    Tuple    						=      config_and_inputs
          snake_case__  :    Tuple    						=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
          return config, inputs_dict







@require_flax
class     UpperCAmelCase_       (       _a		,		unittest.TestCase	):
    """simple docstring"""







    lowercase					=		True

    lowercase					=		(
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )




    def lowerCamelCase       (    self					:     Dict						):
          snake_case__  :    Optional[Any]    						=      FlaxRoFormerModelTester(self						)




    @slow
    def lowerCamelCase       (    self					:     int						):
          for model_class_name in self.all_model_classes:
                snake_case__  :    Tuple    						=      model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" ,     from_pt=snake_case_						)
                snake_case__  :    Tuple    						=      model(np.ones((1, 1)						)						)
                self.assertIsNotNone(snake_case_						)







@require_flax
class     UpperCAmelCase_       (       unittest.TestCase	):
    """simple docstring"""







    @slow
    def lowerCamelCase       (    self					:     Optional[int]						):
          snake_case__  :    Dict    						=      FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""						)
          snake_case__  :    Dict    						=      jnp.array([[0, 1, 2, 3, 4, 5]]						)
          snake_case__  :    List[str]    						=      model(snake_case_						)[0]

          snake_case__  :    Union[str, Any]    						=      50_000

          snake_case__  :    Union[str, Any]    						=      (1, 6, vocab_size)
          self.assertEqual(output.shape ,     snake_case_						)

          snake_case__  :    Tuple    						=      jnp.array(
              [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]						)

          self.assertTrue(jnp.allclose(output[:, :3, :3] ,     snake_case_ ,     atol=1E-4						)						)





'''simple docstring'''





__a          =		frozenset(
    [
        "prompt",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

__a          =		frozenset(["prompt", "negative_prompt"])

__a          =		frozenset([])

__a          =		frozenset(["image"])

__a          =		frozenset(
    [
        "image",
        "height",
        "width",
        "guidance_scale",
    ]
)

__a          =		frozenset(["image"])

__a          =		frozenset(
    [
        "prompt",
        "image",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]
)

__a          =		frozenset(["prompt", "image", "negative_prompt"])

__a          =		frozenset(
    [
        # Text guided image variation with an image mask
        "prompt",
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]
)

__a          =		frozenset(["prompt", "image", "mask_image", "negative_prompt"])

__a          =		frozenset(
    [
        # image variation with an image mask
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

__a          =		frozenset(["image", "mask_image"])

__a          =		frozenset(
    [
        "example_image",
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

__a          =		frozenset(["example_image", "image", "mask_image"])

__a          =		frozenset(["class_labels"])

__a          =		frozenset(["class_labels"])

__a          =		frozenset(["batch_size"])

__a          =		frozenset([])

__a          =		frozenset(["batch_size"])

__a          =		frozenset([])

__a          =		frozenset(
    [
        "prompt",
        "audio_length_in_s",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

__a          =		frozenset(["prompt", "negative_prompt"])
__a          =		frozenset(["input_tokens"])

__a          =		frozenset(["input_tokens"])
