




"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import RoFormerConfig, 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 tensorflow as tf

       from transformers import (
           TFRoFormerForCausalLM,
           TFRoFormerForMaskedLM,
           TFRoFormerForMultipleChoice,
           TFRoFormerForQuestionAnswering,
           TFRoFormerForSequenceClassification,
           TFRoFormerForTokenClassification,
           TFRoFormerModel,
       )
       from transformers.models.roformer.modeling_tf_roformer import (
           TFRoFormerSelfAttention,
           TFRoFormerSinusoidalPositionalEmbedding,
       )





class 				lowercase_       :



 '''simple docstring'''

 def __init__( self       :       Union[str, Any]    ,							_UpperCAmelCase       :       List[str]    ,							_UpperCAmelCase       :       Tuple=13    ,							_UpperCAmelCase       :       List[str]=7    ,							_UpperCAmelCase       :       Dict=True    ,							_UpperCAmelCase       :       Optional[Any]=True    ,							_UpperCAmelCase       :       str=True    ,							_UpperCAmelCase       :       List[Any]=True    ,							_UpperCAmelCase       :       List[str]=99    ,							_UpperCAmelCase       :       Optional[int]=32    ,							_UpperCAmelCase       :       Dict=2    ,							_UpperCAmelCase       :       List[Any]=4    ,							_UpperCAmelCase       :       Tuple=37    ,							_UpperCAmelCase       :       List[Any]="gelu"    ,							_UpperCAmelCase       :       Union[str, Any]=0.1    ,							_UpperCAmelCase       :       List[Any]=0.1    ,							_UpperCAmelCase       :       Tuple=512    ,							_UpperCAmelCase       :       Any=16    ,							_UpperCAmelCase       :       List[Any]=2    ,							_UpperCAmelCase       :       List[str]=0.02    ,							_UpperCAmelCase       :       Optional[int]=3    ,							_UpperCAmelCase       :       List[str]=4    ,							_UpperCAmelCase       :       Optional[Any]=None    ,							):
       _A					   =   parent
       _A					   =   13
       _A					   =   7
       _A					   =   True
       _A					   =   True
       _A					   =   True
       _A					   =   True
       _A					   =   99
       _A					   =   32
       _A					   =   2
       _A					   =   4
       _A					   =   37
       _A					   =   '''gelu'''
       _A					   =   0.1
       _A					   =   0.1
       _A					   =   512
       _A					   =   16
       _A					   =   2
       _A					   =   0.02
       _A					   =   3
       _A					   =   4
       _A					   =   None

 def 			lowerCAmelCase_							( self       :       Dict		):
       _A					   =   ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size		)

       _A					   =   None
       if self.use_input_mask:
             _A					   =   random_attention_mask([self.batch_size, self.seq_length]		)

       _A					   =   None
       if self.use_token_type_ids:
             _A					   =   ids_tensor([self.batch_size, self.seq_length]    ,							self.type_vocab_size		)

       _A					   =   None
       _A					   =   None
       _A					   =   None
       if self.use_labels:
             _A					   =   ids_tensor([self.batch_size]    ,							self.type_sequence_label_size		)
             _A					   =   ids_tensor([self.batch_size, self.seq_length]    ,							self.num_labels		)
             _A					   =   ids_tensor([self.batch_size]    ,							self.num_choices		)

       _A					   =   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    ,							initializer_range=self.initializer_range    ,							return_dict=_a    ,							)

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

 def 			lowerCAmelCase_							( self       :       int    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       Tuple    ,							_UpperCAmelCase       :       int    ,							_UpperCAmelCase       :       List[str]    ,							_UpperCAmelCase       :       Tuple    ,							_UpperCAmelCase       :       int    ,							_UpperCAmelCase       :       Optional[Any]		):
       _A					   =   TFRoFormerModel(config=_a		)
       _A					   =   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

       _A					   =   [input_ids, input_mask]
       _A					   =   model(_a		)

       _A					   =   model(_a		)

       self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size)		)

 def 			lowerCAmelCase_							( self       :       List[str]    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       int    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       Tuple    ,							_UpperCAmelCase       :       Optional[Any]    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       int		):
       _A					   =   True
       _A					   =   TFRoFormerForCausalLM(config=_a		)
       _A					   =   {
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       _A					   =   model(_a		)['''logits''']
       self.parent.assertListEqual(
           list(prediction_scores.numpy().shape		)    ,							[self.batch_size, self.seq_length, self.vocab_size]		)

 def 			lowerCAmelCase_							( self       :       Optional[Any]    ,							_UpperCAmelCase       :       Union[str, Any]    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       str    ,							_UpperCAmelCase       :       Union[str, Any]    ,							_UpperCAmelCase       :       int    ,							_UpperCAmelCase       :       int		):
       _A					   =   TFRoFormerForMaskedLM(config=_a		)
       _A					   =   {
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       _A					   =   model(_a		)
       self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.vocab_size)		)

 def 			lowerCAmelCase_							( self       :       List[Any]    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       Optional[Any]    ,							_UpperCAmelCase       :       Union[str, Any]    ,							_UpperCAmelCase       :       str    ,							_UpperCAmelCase       :       int    ,							_UpperCAmelCase       :       List[str]		):
       _A					   =   self.num_labels
       _A					   =   TFRoFormerForSequenceClassification(config=_a		)
       _A					   =   {
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }

       _A					   =   model(_a		)
       self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels)		)

 def 			lowerCAmelCase_							( self       :       str    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       int    ,							_UpperCAmelCase       :       Tuple    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       int		):
       _A					   =   self.num_choices
       _A					   =   TFRoFormerForMultipleChoice(config=_a		)
       _A					   =   tf.tile(tf.expand_dims(_a    ,							1		)    ,							(1, self.num_choices, 1)		)
       _A					   =   tf.tile(tf.expand_dims(_a    ,							1		)    ,							(1, self.num_choices, 1)		)
       _A					   =   tf.tile(tf.expand_dims(_a    ,							1		)    ,							(1, self.num_choices, 1)		)
       _A					   =   {
           '''input_ids''': multiple_choice_inputs_ids,
           '''attention_mask''': multiple_choice_input_mask,
           '''token_type_ids''': multiple_choice_token_type_ids,
       }
       _A					   =   model(_a		)
       self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_choices)		)

 def 			lowerCAmelCase_							( self       :       str    ,							_UpperCAmelCase       :       Union[str, Any]    ,							_UpperCAmelCase       :       Union[str, Any]    ,							_UpperCAmelCase       :       Tuple    ,							_UpperCAmelCase       :       int    ,							_UpperCAmelCase       :       Tuple    ,							_UpperCAmelCase       :       Union[str, Any]    ,							_UpperCAmelCase       :       Any		):
       _A					   =   self.num_labels
       _A					   =   TFRoFormerForTokenClassification(config=_a		)
       _A					   =   {
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       _A					   =   model(_a		)
       self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.num_labels)		)

 def 			lowerCAmelCase_							( self       :       Union[str, Any]    ,							_UpperCAmelCase       :       Dict    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       Dict    ,							_UpperCAmelCase       :       Dict    ,							_UpperCAmelCase       :       Optional[Any]    ,							_UpperCAmelCase       :       Optional[int]		):
       _A					   =   TFRoFormerForQuestionAnswering(config=_a		)
       _A					   =   {
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }

       _A					   =   model(_a		)
       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)		)



 def 			lowerCAmelCase_							( self       :       Optional[Any]		):
       _A					   =   self.prepare_config_and_inputs()
       (
           _A					
       )					   =   config_and_inputs
       _A					   =   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict





@require_tf
class 				lowercase_       (     __lowercase			,	__lowercase			,	unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase    :     str        =	(
     (
         TFRoFormerModel,
         TFRoFormerForCausalLM,
         TFRoFormerForMaskedLM,
         TFRoFormerForQuestionAnswering,
         TFRoFormerForSequenceClassification,
         TFRoFormerForTokenClassification,
         TFRoFormerForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 UpperCAmelCase    :     Any        =	(
     {
         "feature-extraction": TFRoFormerModel,
         "fill-mask": TFRoFormerForMaskedLM,
         "question-answering": TFRoFormerForQuestionAnswering,
         "text-classification": TFRoFormerForSequenceClassification,
         "text-generation": TFRoFormerForCausalLM,
         "token-classification": TFRoFormerForTokenClassification,
         "zero-shot": TFRoFormerForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )

 UpperCAmelCase    :     str        =	False
 UpperCAmelCase    :     Optional[Any]        =	False

 def 			lowerCAmelCase_							( self       :       List[str]    ,							_UpperCAmelCase       :       List[str]    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       Dict		):
       if pipeline_test_casse_name == "TextGenerationPipelineTests":
             return True

       return False

 def 			lowerCAmelCase_							( self       :       Dict		):
       _A					   =   TFRoFormerModelTester(self		)
       _A					   =   ConfigTester(self    ,							config_class=_a    ,							hidden_size=37		)

 def 			lowerCAmelCase_							( self       :       str		):
       self.config_tester.run_common_tests()

 def 			lowerCAmelCase_							( self       :       Optional[int]		):
       _A					   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_a		)

 def 			lowerCAmelCase_							( self       :       Union[str, Any]		):
       _A					   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_a		)

 def 			lowerCAmelCase_							( self       :       Any		):
       _A					   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head(*_a		)

 def 			lowerCAmelCase_							( self       :       Dict		):
       _A					   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*_a		)

 def 			lowerCAmelCase_							( self       :       Optional[int]		):
       _A					   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_a		)

 def 			lowerCAmelCase_							( self       :       Optional[Any]		):
       _A					   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_a		)

 def 			lowerCAmelCase_							( self       :       List[Any]		):
       _A					   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_a		)



 @slow
 def 			lowerCAmelCase_							( self       :       List[str]		):
       _A					   =   TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'		)
       self.assertIsNotNone(_a		)





@require_tf
class 				lowercase_       (     unittest.TestCase							):



 '''simple docstring'''

 @slow
 def 			lowerCAmelCase_							( self       :       Optional[int]		):
       _A					   =   TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'		)
       _A					   =   tf.constant([[0, 1, 2, 3, 4, 5]]		)
       _A					   =   model(_a		)[0]

       # TODO Replace vocab size
       _A					   =   50_000

       _A					   =   [1, 6, vocab_size]
       self.assertEqual(output.shape    ,							_a		)

       print(output[:, :3, :3]		)

       # TODO Replace values below with what was printed above.
       _A					   =   tf.constant(
           [
               [
                   [-0.1205_3341, -1.026_4901, 0.2922_1946],
                   [-1.513_3783, 0.19_7433, 0.1519_0607],
                   [-5.013_5403, -3.90_0256, -0.8403_8764],
               ]
           ]		)
       tf.debugging.assert_near(output[:, :3, :3]    ,							_a    ,							atol=1E-4		)





@require_tf
class 				lowercase_       (     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase    :     Optional[int]        =	1E-4

 def 			lowerCAmelCase_							( self       :       Tuple		):
       _A					   =   tf.constant([[4, 10]]		)
       _A					   =   TFRoFormerSinusoidalPositionalEmbedding(num_positions=6    ,							embedding_dim=6		)

       _A					   =   emba(input_ids.shape		)
       _A					   =   tf.constant(
           [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]		)

       tf.debugging.assert_near(_a    ,							_a    ,							atol=self.tolerance		)



 def 			lowerCAmelCase_							( self       :       int		):
       _A					   =   tf.constant(
           [
               [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
               [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
               [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
           ]		)
       _A					   =   TFRoFormerSinusoidalPositionalEmbedding(num_positions=512    ,							embedding_dim=512		)
       emba([2, 16, 512]		)
       _A					   =   emba.weight[:3, :5]

       tf.debugging.assert_near(_a    ,							_a    ,							atol=self.tolerance		)





@require_tf
class 				lowercase_       (     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase    :     Optional[int]        =	1E-4

 def 			lowerCAmelCase_							( self       :       Tuple		):
       # 2,12,16,64
       _A					   =   tf.reshape(tf.range(2 * 12 * 16 * 64    ,							dtype=tf.floataa		)    ,							shape=(2, 12, 16, 64)		) / 100

       _A					   =   -tf.reshape(tf.range(2 * 12 * 16 * 64    ,							dtype=tf.floataa		)    ,							shape=(2, 12, 16, 64)		) / 100

       _A					   =   TFRoFormerSinusoidalPositionalEmbedding(num_positions=32    ,							embedding_dim=64		)
       _A					   =   embed_positions([2, 16, 768]		)[None, None, :, :]

       _A					   =   TFRoFormerSelfAttention.apply_rotary_position_embeddings(
           _a    ,							_a    ,							_a		)

       _A					   =   tf.constant(
           [
               [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
               [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
               [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
               [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
               [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
               [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
           ]		)
       _A					   =   tf.constant(
           [
               [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
               [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
               [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
               [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
               [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
               [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
           ]		)

       tf.debugging.assert_near(query_layer[0, 0, :6, :8]    ,							_a    ,							atol=self.tolerance		)
       tf.debugging.assert_near(key_layer[0, 0, :6, :8]    ,							_a    ,							atol=self.tolerance		)




import requests

a__				=		'''YOUR API KEY'''



def    __UpperCAmelCase (	__a      :			str		,__a      :			str = giphy_api_key	)			->     list:

					"""simple docstring"""

					_a	:    Optional[Any]         =		'''+'''.join(query.split()	)
					_a	:    Union[str, Any]         =		F"""https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"""
					_a	:    List[Any]         =		requests.get(__a	).json()['''data''']
					return [gif["url"] for gif in gifs]


if __name__ == "__main__":
			print('''\n'''.join(get_gifs('''space ship''')))



