

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 				SCREAMING_SNAKE_CASE__ :





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





    def 				snake_case_    (				self):
         lowercase__   :       Any				=	ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size)

         lowercase__   :       Optional[int]				=	None
         if self.use_input_mask:
              lowercase__   :       Union[str, Any]				=	random_attention_mask([self.batch_size, self.seq_length])

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

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

         lowercase__   :       str				=	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 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
         lowercase__   :       Any				=	TFRoFormerModel(config=a)
         lowercase__   :       Tuple				=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

         lowercase__   :       int				=	[input_ids, input_mask]
         lowercase__   :       List[str]				=	model(a)

         lowercase__   :       List[str]				=	model(a)

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





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
         lowercase__   :       str				=	True
         lowercase__   :       Any				=	TFRoFormerForCausalLM(config=a)
         lowercase__   :       Optional[Any]				=	{
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'token_type_ids': token_type_ids,
         }
         lowercase__   :       Any				=	model(a)['logits']
         self.parent.assertListEqual(
             list(prediction_scores.numpy().shape)						,       [self.batch_size, self.seq_length, self.vocab_size])





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
         lowercase__   :       Any				=	TFRoFormerForMaskedLM(config=a)
         lowercase__   :       Optional[Any]				=	{
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'token_type_ids': token_type_ids,
         }
         lowercase__   :       List[str]				=	model(a)
         self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.seq_length, self.vocab_size))





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
         lowercase__   :       Union[str, Any]				=	self.num_labels
         lowercase__   :       Tuple				=	TFRoFormerForSequenceClassification(config=a)
         lowercase__   :       str				=	{
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'token_type_ids': token_type_ids,
         }

         lowercase__   :       List[Any]				=	model(a)
         self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.num_labels))





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





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
         lowercase__   :       Dict				=	self.num_labels
         lowercase__   :       Any				=	TFRoFormerForTokenClassification(config=a)
         lowercase__   :       Any				=	{
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'token_type_ids': token_type_ids,
         }
         lowercase__   :       Optional[int]				=	model(a)
         self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.seq_length, self.num_labels))





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
         lowercase__   :       Tuple				=	TFRoFormerForQuestionAnswering(config=a)
         lowercase__   :       Any				=	{
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'token_type_ids': token_type_ids,
         }

         lowercase__   :       Union[str, Any]				=	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 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	self.prepare_config_and_inputs()
         (
             (
             lowercase__
         )				,			(
             lowercase__
         )				,			(
             lowercase__
         )				,			(
             lowercase__
         )				,			(
             lowercase__
         )				,			(
             lowercase__
         )				,			(
             lowercase__
         )				,			
         )   :       Union[str, Any]				=	config_and_inputs
         lowercase__   :       Any				=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
         return config, inputs_dict



@require_tf
class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	str														=					(
        (
            TFRoFormerModel,
            TFRoFormerForCausalLM,
            TFRoFormerForMaskedLM,
            TFRoFormerForQuestionAnswering,
            TFRoFormerForSequenceClassification,
            TFRoFormerForTokenClassification,
            TFRoFormerForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    __lowerCamelCase						:	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 {}
    )

    __lowerCamelCase						:	List[str]														=					False
    __lowerCamelCase						:	Tuple														=					False





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a):
         if pipeline_test_casse_name == "TextGenerationPipelineTests":
              return True

         return False





    def 				snake_case_    (				self):
         lowercase__   :       int				=	TFRoFormerModelTester(self)
         lowercase__   :       str				=	ConfigTester(self						,       config_class=a						,       hidden_size=37)





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





    def 				snake_case_    (				self):
         lowercase__   :       Any				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a)





    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*a)





    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head(*a)





    def 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*a)





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*a)





    def 				snake_case_    (				self):
         lowercase__   :       int				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*a)





    def 				snake_case_    (				self):
         lowercase__   :       Dict				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*a)





    @slow
    def 				snake_case_    (				self):
         lowercase__   :       str				=	TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base')
         self.assertIsNotNone(a)



@require_tf
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    @slow
    def 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
         lowercase__   :       List[Any]				=	tf.constant([[0, 1, 2, 3, 4, 5]])
         lowercase__   :       Optional[Any]				=	model(a)[0]

         # TODO Replace vocab size
         lowercase__   :       Dict				=	5_0000

         lowercase__   :       Tuple				=	[1, 6, vocab_size]
         self.assertEqual(output.shape						,       a)

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

         # TODO Replace values below with what was printed above.
         lowercase__   :       Dict				=	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]						,       a						,       atol=1e-4)



@require_tf
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):
    __lowerCamelCase						:	Dict														=					1e-4





    def 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	tf.constant([[4, 10]])
         lowercase__   :       Optional[Any]				=	TFRoFormerSinusoidalPositionalEmbedding(num_positions=6						,       embedding_dim=6)

         lowercase__   :       int				=	emba(input_ids.shape)
         lowercase__   :       List[Any]				=	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(a						,       a						,       atol=self.tolerance)





    def 				snake_case_    (				self):
         lowercase__   :       Any				=	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],
             ])
         lowercase__   :       Optional[int]				=	TFRoFormerSinusoidalPositionalEmbedding(num_positions=512						,       embedding_dim=512)
         emba([2, 16, 512])
         lowercase__   :       str				=	emba.weight[:3, :5]

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



@require_tf
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):
    __lowerCamelCase						:	Union[str, Any]														=					1e-4





    def 				snake_case_    (				self):
         # 2,12,16,64
         lowercase__   :       Union[str, Any]				=	tf.reshape(tf.range(2 * 12 * 16 * 64						,       dtype=tf.floataa)						,       shape=(2, 12, 16, 64)) / 100

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

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

         lowercase__				,			lowercase__   :       Optional[Any]				=	TFRoFormerSelfAttention.apply_rotary_position_embeddings(
             a						,       a						,       a)

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




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


snake_case_        =			False
snake_case_        =			True
snake_case_        =			False


if __name__ == "__main__":
       snake_case_        =			argparse.ArgumentParser()

       parser.add_argument(
           '''--repo_path''',
           default=None,
           type=str,
           required=True,
           help='''The config json file corresponding to the architecture.''',
       )

       parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

       snake_case_        =			parser.parse_args()

       snake_case_        =			{
           '''image_size''': '''sample_size''',
           '''num_res_blocks''': '''layers_per_block''',
           '''block_channels''': '''block_out_channels''',
           '''down_blocks''': '''down_block_types''',
           '''up_blocks''': '''up_block_types''',
           '''downscale_freq_shift''': '''freq_shift''',
           '''resnet_num_groups''': '''norm_num_groups''',
           '''resnet_act_fn''': '''act_fn''',
           '''resnet_eps''': '''norm_eps''',
           '''num_head_channels''': '''attention_head_dim''',
       }

       snake_case_        =			{
           '''time_steps''': '''time_proj''',
           '''mid''': '''mid_block''',
           '''downsample_blocks''': '''down_blocks''',
           '''upsample_blocks''': '''up_blocks''',
       }

       snake_case_        =			'''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

       with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
              snake_case_        =			reader.read()
              snake_case_        =			json.loads(text)

       if do_only_config:
              for key in config_parameters_to_change.keys():
                     config.pop(key, None)

       if has_file(args.repo_path, '''config.json'''):
              snake_case_        =			UNetaDModel(**config)
       else:
              snake_case_        =			UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
              snake_case_        =			class_name(**config)

       if do_only_config:
              model.save_config(os.path.join(args.repo_path, subfolder))

       snake_case_        =			dict(model.config)

       if do_only_renaming:
              for key, value in config_parameters_to_change.items():
                     if key in config:
                            snake_case_        =			config[key]
                            del config[key]

              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

       if do_only_weights:
              snake_case_        =			torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

              snake_case_        =			{}
              for param_key, param_value in state_dict.items():
                     if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
                            continue
                     snake_case_        =			False
                     for key, new_key in key_parameters_to_change.items():
                            if not has_changed and param_key.split('''.''')[0] == key:
                                   snake_case_        =			param_value
                                   snake_case_        =			True
                     if not has_changed:
                            snake_case_        =			param_value

              model.load_state_dict(new_state_dict)
              model.save_pretrained(os.path.join(args.repo_path, subfolder))

