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      lowerCAmelCase						:



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__=13				,      lowerCAmelCase__=7				,      lowerCAmelCase__=True				,      lowerCAmelCase__=True				,      lowerCAmelCase__=True				,      lowerCAmelCase__=True				,      lowerCAmelCase__=99				,      lowerCAmelCase__=32				,      lowerCAmelCase__=2				,      lowerCAmelCase__=4				,      lowerCAmelCase__=37				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=512				,      lowerCAmelCase__=16				,      lowerCAmelCase__=2				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=3				,      lowerCAmelCase__=4				,      lowerCAmelCase__=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 			a__  (					self       ):
            _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=lowerCAmelCase__				,      )

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



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	TFRoFormerModel(config=lowerCAmelCase__       )
            _A=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

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

            _A=	model(lowerCAmelCase__       )

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



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	True
            _A=	TFRoFormerForCausalLM(config=lowerCAmelCase__       )
            _A=	{
                'input_ids': input_ids,
                'attention_mask': input_mask,
                'token_type_ids': token_type_ids,
            }
            _A=	model(lowerCAmelCase__       )['logits']
            self.parent.assertListEqual(
                list(prediction_scores.numpy().shape       )				,      [self.batch_size, self.seq_length, self.vocab_size]       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	TFRoFormerForMaskedLM(config=lowerCAmelCase__       )
            _A=	{
                'input_ids': input_ids,
                'attention_mask': input_mask,
                'token_type_ids': token_type_ids,
            }
            _A=	model(lowerCAmelCase__       )
            self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.seq_length, self.vocab_size)       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	self.num_labels
            _A=	TFRoFormerForSequenceClassification(config=lowerCAmelCase__       )
            _A=	{
                'input_ids': input_ids,
                'attention_mask': input_mask,
                'token_type_ids': token_type_ids,
            }

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



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	self.num_choices
            _A=	TFRoFormerForMultipleChoice(config=lowerCAmelCase__       )
            _A=	tf.tile(tf.expand_dims(lowerCAmelCase__				,      1       )				,      (1, self.num_choices, 1)       )
            _A=	tf.tile(tf.expand_dims(lowerCAmelCase__				,      1       )				,      (1, self.num_choices, 1)       )
            _A=	tf.tile(tf.expand_dims(lowerCAmelCase__				,      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(lowerCAmelCase__       )
            self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.num_choices)       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	self.num_labels
            _A=	TFRoFormerForTokenClassification(config=lowerCAmelCase__       )
            _A=	{
                'input_ids': input_ids,
                'attention_mask': input_mask,
                'token_type_ids': token_type_ids,
            }
            _A=	model(lowerCAmelCase__       )
            self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.seq_length, self.num_labels)       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	TFRoFormerForQuestionAnswering(config=lowerCAmelCase__       )
            _A=	{
                'input_ids': input_ids,
                'attention_mask': input_mask,
                'token_type_ids': token_type_ids,
            }

            _A=	model(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)       )





     def 			a__  (					self       ):
            _A=	self.prepare_config_and_inputs()
            (
                (
                _A
            ),			(
                _A
            ),			(
                _A
            ),			(
                _A
            ),			(
                _A
            ),			(
                _A
            ),			(
                _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      lowerCAmelCase						(					_a			,		_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     Dict		=(
         (
             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     :     List[Any]		=False
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=False



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            if pipeline_test_casse_name == "TextGenerationPipelineTests":
                   return True

            return False



     def 			a__  (					self       ):
            _A=	TFRoFormerModelTester(self       )
            _A=	ConfigTester(self				,      config_class=lowerCAmelCase__				,      hidden_size=37       )



     def 			a__  (					self       ):
            self.config_tester.run_common_tests()



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_lm_head(*lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__       )





     @slow
     def 			a__  (					self       ):
            _A=	TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'       )
            self.assertIsNotNone(lowerCAmelCase__       )





@require_tf
class      lowerCAmelCase						(					unittest.TestCase			):



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

            # TODO Replace vocab size
            _A=	50000

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

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

            # TODO Replace values below with what was printed above.
            _A=	tf.constant(
                [
                    [
                        [-0.12053341, -1.0264901, 0.29221946],
                        [-1.5133783, 0.197433, 0.15190607],
                        [-5.0135403, -3.900256, -0.84038764],
                    ]
                ]       )
            tf.debugging.assert_near(output[:, :3, :3]				,      lowerCAmelCase__				,      atol=1E-4       )





@require_tf
class      lowerCAmelCase						(					unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     str		=1e-4



     def 			a__  (					self       ):
            _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(lowerCAmelCase__				,      lowerCAmelCase__				,      atol=self.tolerance       )





     def 			a__  (					self       ):
            _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(lowerCAmelCase__				,      lowerCAmelCase__				,      atol=self.tolerance       )





@require_tf
class      lowerCAmelCase						(					unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     List[Any]		=1e-4



     def 			a__  (					self       ):
            # 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,			_A=	TFRoFormerSelfAttention.apply_rotary_position_embeddings(
                lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )

            _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]				,      lowerCAmelCase__				,      atol=self.tolerance       )
            tf.debugging.assert_near(key_layer[0, 0, :6, :8]				,      lowerCAmelCase__				,      atol=self.tolerance       )
import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
      import torch

if is_soundfile_availble():
      import soundfile as sf

if is_vision_available():
      from PIL import Image

def 							UpperCamelCase				(    lowerCAmelCase_=""   )		->						str:




       '''simple docstring'''






       _A=	tempfile.mkdtemp()
       return os.path.join(lowerCAmelCase_       ,					str(uuid.uuida()   ) + suffix   )





@require_soundfile
@require_torch
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.rand(12				,      dtype=torch.floataa       ) - 0.5
            _A=	AgentAudio(lowerCAmelCase__       )
            _A=	str(agent_type.to_string()       )

            # Ensure that the tensor and the agent_type's tensor are the same
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      agent_type.to_raw()				,      atol=1E-4       )       )

            del agent_type

            # Ensure the path remains even after the object deletion
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )

            # Ensure that the file contains the same value as the original tensor
            _A,			_A=	sf.read(lowerCAmelCase__       )
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      torch.tensor(lowerCAmelCase__       )				,      atol=1E-4       )       )





     def 			a__  (					self       ):
            _A=	torch.rand(12				,      dtype=torch.floataa       ) - 0.5
            _A=	get_new_path(suffix='.wav'       )
            sf.write(lowerCAmelCase__				,      lowerCAmelCase__				,      16000       )

            _A=	AgentAudio(lowerCAmelCase__       )

            self.assertTrue(torch.allclose(lowerCAmelCase__				,      agent_type.to_raw()				,      atol=1E-4       )       )
            self.assertEqual(agent_type.to_string()				,      lowerCAmelCase__       )





@require_vision
@require_torch
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.randint(0				,      256				,      (64, 64, 3)       )
            _A=	AgentImage(lowerCAmelCase__       )
            _A=	str(agent_type.to_string()       )

            # Ensure that the tensor and the agent_type's tensor are the same
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      agent_type._tensor				,      atol=1E-4       )       )

            self.assertIsInstance(agent_type.to_raw()				,      Image.Image       )

            # Ensure the path remains even after the object deletion
            del agent_type
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )



     def 			a__  (					self       ):
            _A=	Path(get_tests_dir('fixtures/tests_samples/COCO'       )       ) / '000000039769.png'
            _A=	Image.open(lowerCAmelCase__       )
            _A=	AgentImage(lowerCAmelCase__       )

            self.assertTrue(path.samefile(agent_type.to_string()       )       )
            self.assertTrue(image == agent_type.to_raw()       )

            # Ensure the path remains even after the object deletion
            del agent_type
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )





     def 			a__  (					self       ):
            _A=	Path(get_tests_dir('fixtures/tests_samples/COCO'       )       ) / '000000039769.png'
            _A=	Image.open(lowerCAmelCase__       )
            _A=	AgentImage(lowerCAmelCase__       )

            self.assertFalse(path.samefile(agent_type.to_string()       )       )
            self.assertTrue(image == agent_type.to_raw()       )

            # Ensure the path remains even after the object deletion
            del agent_type
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )





class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	'Hey!'
            _A=	AgentText(lowerCAmelCase__       )

            self.assertEqual(lowerCAmelCase__				,      agent_type.to_string()       )
            self.assertEqual(lowerCAmelCase__				,      agent_type.to_raw()       )
            self.assertEqual(lowerCAmelCase__				,      lowerCAmelCase__       )