


'''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      a    :


     """simple docstring"""






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

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Tuple:
          __UpperCAmelCase     :			List[Any]  							=      ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size      )

          __UpperCAmelCase     :			Union[str, Any]  							=      None
          if self.use_input_mask:
               __UpperCAmelCase     :			Tuple  							=      random_attention_mask([self.batch_size, self.seq_length]      )

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

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

          __UpperCAmelCase     :			Union[str, Any]  							=      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=snake_case							,			)

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

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		List[str]							,			snake_case					:		int							,			snake_case					:		Optional[int]							,			snake_case					:		List[Any]							,			snake_case					:		Optional[int]							,			snake_case					:		Tuple							,			snake_case					:		int      )						->							Optional[Any]:
          __UpperCAmelCase     :			Any  							=      TFRoFormerModel(config=snake_case      )
          __UpperCAmelCase     :			Tuple  							=      {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

          __UpperCAmelCase     :			Dict  							=      [input_ids, input_mask]
          __UpperCAmelCase     :			Tuple  							=      model(snake_case      )

          __UpperCAmelCase     :			Tuple  							=      model(snake_case      )

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

     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		int							,			snake_case					:		List[Any]							,			snake_case					:		Optional[Any]							,			snake_case					:		Optional[Any]							,			snake_case					:		int							,			snake_case					:		Any							,			snake_case					:		List[Any]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			int  							=      True
          __UpperCAmelCase     :			Any  							=      TFRoFormerForCausalLM(config=snake_case      )
          __UpperCAmelCase     :			Any  							=      {
              '''input_ids''': input_ids,
              '''attention_mask''': input_mask,
              '''token_type_ids''': token_type_ids,
          }
          __UpperCAmelCase     :			Union[str, Any]  							=      model(snake_case      )['''logits''']
          self.parent.assertListEqual(
              list(prediction_scores.numpy().shape      )							,			[self.batch_size, self.seq_length, self.vocab_size]      )

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		List[str]							,			snake_case					:		int							,			snake_case					:		str							,			snake_case					:		Tuple							,			snake_case					:		Dict							,			snake_case					:		int							,			snake_case					:		List[str]      )						->							List[Any]:
          __UpperCAmelCase     :			List[str]  							=      TFRoFormerForMaskedLM(config=snake_case      )
          __UpperCAmelCase     :			List[Any]  							=      {
              '''input_ids''': input_ids,
              '''attention_mask''': input_mask,
              '''token_type_ids''': token_type_ids,
          }
          __UpperCAmelCase     :			List[str]  							=      model(snake_case      )
          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.vocab_size)      )

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		List[str]							,			snake_case					:		int							,			snake_case					:		Tuple							,			snake_case					:		List[Any]							,			snake_case					:		int							,			snake_case					:		List[str]							,			snake_case					:		Any      )						->							Optional[int]:
          __UpperCAmelCase     :			Optional[Any]  							=      self.num_labels
          __UpperCAmelCase     :			List[Any]  							=      TFRoFormerForSequenceClassification(config=snake_case      )
          __UpperCAmelCase     :			Optional[Any]  							=      {
              '''input_ids''': input_ids,
              '''attention_mask''': input_mask,
              '''token_type_ids''': token_type_ids,
          }

          __UpperCAmelCase     :			Union[str, Any]  							=      model(snake_case      )
          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_labels)      )

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		Tuple							,			snake_case					:		List[str]							,			snake_case					:		List[str]							,			snake_case					:		Optional[int]							,			snake_case					:		Dict							,			snake_case					:		List[str]							,			snake_case					:		List[str]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			List[str]  							=      self.num_choices
          __UpperCAmelCase     :			Optional[int]  							=      TFRoFormerForMultipleChoice(config=snake_case      )
          __UpperCAmelCase     :			Tuple  							=      tf.tile(tf.expand_dims(snake_case							,			1      )							,			(1, self.num_choices, 1)      )
          __UpperCAmelCase     :			Optional[Any]  							=      tf.tile(tf.expand_dims(snake_case							,			1      )							,			(1, self.num_choices, 1)      )
          __UpperCAmelCase     :			str  							=      tf.tile(tf.expand_dims(snake_case							,			1      )							,			(1, self.num_choices, 1)      )
          __UpperCAmelCase     :			Any  							=      {
              '''input_ids''': multiple_choice_inputs_ids,
              '''attention_mask''': multiple_choice_input_mask,
              '''token_type_ids''': multiple_choice_token_type_ids,
          }
          __UpperCAmelCase     :			int  							=      model(snake_case      )
          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_choices)      )

     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		List[str]							,			snake_case					:		Dict							,			snake_case					:		Optional[Any]							,			snake_case					:		Tuple							,			snake_case					:		List[Any]							,			snake_case					:		Any							,			snake_case					:		Tuple      )						->							str:
          __UpperCAmelCase     :			Dict  							=      self.num_labels
          __UpperCAmelCase     :			List[str]  							=      TFRoFormerForTokenClassification(config=snake_case      )
          __UpperCAmelCase     :			str  							=      {
              '''input_ids''': input_ids,
              '''attention_mask''': input_mask,
              '''token_type_ids''': token_type_ids,
          }
          __UpperCAmelCase     :			List[str]  							=      model(snake_case      )
          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.num_labels)      )

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		str							,			snake_case					:		List[Any]							,			snake_case					:		Optional[int]							,			snake_case					:		Tuple							,			snake_case					:		List[str]							,			snake_case					:		Union[str, Any]							,			snake_case					:		List[Any]      )						->							Dict:
          __UpperCAmelCase     :			Optional[int]  							=      TFRoFormerForQuestionAnswering(config=snake_case      )
          __UpperCAmelCase     :			str  							=      {
              '''input_ids''': input_ids,
              '''attention_mask''': input_mask,
              '''token_type_ids''': token_type_ids,
          }

          __UpperCAmelCase     :			str  							=      model(snake_case      )
          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					:		Dict      )						->							int:
          __UpperCAmelCase     :			Optional[int]  							=      self.prepare_config_and_inputs()
          (
              (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       
          )     :			List[Any]  							=      config_and_inputs
          __UpperCAmelCase     :			List[Any]  							=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict



@require_tf
class      a    (		_a     ,  _a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Any							       =       (
         (
             TFRoFormerModel,
             TFRoFormerForCausalLM,
             TFRoFormerForMaskedLM,
             TFRoFormerForQuestionAnswering,
             TFRoFormerForSequenceClassification,
             TFRoFormerForTokenClassification,
             TFRoFormerForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE	:			Tuple							       =       (
         {
             "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 {}
     )

     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       False
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       False

     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		Union[str, Any]							,			snake_case					:		Any							,			snake_case					:		Optional[Any]							,			snake_case					:		Tuple							,			snake_case					:		List[str]      )						->							Dict:
          if pipeline_test_casse_name == "TextGenerationPipelineTests":
               return True

          return False

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							List[Any]:
          __UpperCAmelCase     :			Tuple  							=      TFRoFormerModelTester(self      )
          __UpperCAmelCase     :			str  							=      ConfigTester(self							,			config_class=snake_case							,			hidden_size=37      )

     def 			lowerCamelCase__			(    self					:		int      )						->							Tuple:
          self.config_tester.run_common_tests()

     def 			lowerCamelCase__			(    self					:		int      )						->							Any:
          __UpperCAmelCase     :			int  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Dict:
          __UpperCAmelCase     :			Dict  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							Any:
          __UpperCAmelCase     :			str  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_lm_head(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Tuple  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Dict:
          __UpperCAmelCase     :			Union[str, Any]  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Optional[int]:
          __UpperCAmelCase     :			str  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Optional[int]:
          __UpperCAmelCase     :			Dict  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*snake_case      )






     @slow
     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			List[Any]  							=      TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''      )
          self.assertIsNotNone(snake_case      )



@require_tf
class      a    (		unittest.TestCase	):


     """simple docstring"""






     @slow
     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Dict:
          __UpperCAmelCase     :			Optional[int]  							=      TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''      )
          __UpperCAmelCase     :			List[Any]  							=      tf.constant([[0, 1, 2, 3, 4, 5]]      )
          __UpperCAmelCase     :			str  							=      model(snake_case      )[0]

          # TODO Replace vocab size
          __UpperCAmelCase     :			Union[str, Any]  							=      5_0000

          __UpperCAmelCase     :			List[Any]  							=      [1, 6, vocab_size]
          self.assertEqual(output.shape							,			snake_case      )

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

          # TODO Replace values below with what was printed above.
          __UpperCAmelCase     :			Tuple  							=      tf.constant(
              [
                  [
                      [-0.12_053_341, -1.0_264_901, 0.29_221_946],
                      [-1.5_133_783, 0.197_433, 0.15_190_607],
                      [-5.0_135_403, -3.900_256, -0.84_038_764],
                  ]
              ]      )
          tf.debugging.assert_near(output[:, :3, :3]							,			snake_case							,			atol=1E-4      )



@require_tf
class      a    (		unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Dict							       =       1E-4

     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							int:
          __UpperCAmelCase     :			Dict  							=      tf.constant([[4, 10]]      )
          __UpperCAmelCase     :			str  							=      TFRoFormerSinusoidalPositionalEmbedding(num_positions=6							,			embedding_dim=6      )

          __UpperCAmelCase     :			Dict  							=      emba(input_ids.shape      )
          __UpperCAmelCase     :			Tuple  							=      tf.constant(
              [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]      )

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






     def 			lowerCamelCase__			(    self					:		Any      )						->							Optional[int]:
          __UpperCAmelCase     :			Tuple  							=      tf.constant(
              [
                  [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
                  [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
                  [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
              ]      )
          __UpperCAmelCase     :			Union[str, Any]  							=      TFRoFormerSinusoidalPositionalEmbedding(num_positions=512							,			embedding_dim=512      )
          emba([2, 16, 512]      )
          __UpperCAmelCase     :			Any  							=      emba.weight[:3, :5]

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



@require_tf
class      a    (		unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Dict							       =       1E-4

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							List[str]:
          # 2,12,16,64
          __UpperCAmelCase     :			Optional[int]  							=      tf.reshape(tf.range(2 * 12 * 16 * 64							,			dtype=tf.floataa      )							,			shape=(2, 12, 16, 64)      ) / 100

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

          __UpperCAmelCase     :			List[Any]  							=      TFRoFormerSinusoidalPositionalEmbedding(num_positions=32							,			embedding_dim=64      )
          __UpperCAmelCase     :			List[str]  							=      embed_positions([2, 16, 768]      )[None, None, :, :]

          __UpperCAmelCase							,       __UpperCAmelCase     :			Union[str, Any]  							=      TFRoFormerSelfAttention.apply_rotary_position_embeddings(
              snake_case							,			snake_case							,			snake_case      )

          __UpperCAmelCase     :			List[str]  							=      tf.constant(
              [
                  [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
                  [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
                  [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
                  [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
                  [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
                  [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
              ]      )
          __UpperCAmelCase     :			Optional[int]  							=      tf.constant(
              [
                  [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
                  [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
                  [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
                  [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
                  [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
                  [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
              ]      )

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



'''simple docstring'''



from argparse import ArgumentParser

from .env import EnvironmentCommand




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[Any]  							=      ArgumentParser('''Diffusers CLI tool'''      , usage='''diffusers-cli <command> [<args>]'''  )
     __UpperCAmelCase     :			Union[str, Any]  							=      parser.add_subparsers(help='''diffusers-cli command helpers'''  )

     # Register commands
     EnvironmentCommand.register_subcommand(_lowercase  )

     # Let's go
     __UpperCAmelCase     :			Union[str, Any]  							=      parser.parse_args()

     if not hasattr(_lowercase      , '''func'''  ):
          parser.print_help()
          exit(1  )

     # Run
     __UpperCAmelCase     :			Any  							=      args.func(_lowercase  )
     service.run()


if __name__ == "__main__":
      main()