




from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_=None ,				lowercase_=None						)   ->       List[str]:



       """simple docstring"""

       if attention_mask is None:
              A__       =    tf.cast(tf.math.not_equal(lowercase_ ,				config.pad_token_id						) ,				tf.inta						)
       return {"input_ids": input_ids, "attention_mask": attention_mask}


@require_tf
class 				UpperCamelCase_    :

    '''simple docstring'''






    UpperCAmelCase__							  =      OPTConfig
    UpperCAmelCase__							  =      {}
    UpperCAmelCase__							  =      '''gelu'''


    def __init__(			self      :	Tuple					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	Dict=13					, UpperCAmelCase__      :	List[str]=7					, UpperCAmelCase__      :	Dict=True					, UpperCAmelCase__      :	Any=False					, UpperCAmelCase__      :	Optional[int]=99					, UpperCAmelCase__      :	Dict=16					, UpperCAmelCase__      :	Dict=2					, UpperCAmelCase__      :	Tuple=4					, UpperCAmelCase__      :	Tuple=4					, UpperCAmelCase__      :	int="gelu"					, UpperCAmelCase__      :	List[str]=0.1					, UpperCAmelCase__      :	Optional[Any]=0.1					, UpperCAmelCase__      :	Dict=20					, UpperCAmelCase__      :	Any=2					, UpperCAmelCase__      :	Tuple=1					, UpperCAmelCase__      :	str=0					, UpperCAmelCase__      :	int=16					, UpperCAmelCase__      :	List[Any]=16					, )						->Optional[int]:





           '''simple docstring'''

           A__       =    parent
           A__       =    batch_size
           A__       =    seq_length
           A__       =    is_training
           A__       =    use_labels
           A__       =    vocab_size
           A__       =    hidden_size
           A__       =    num_hidden_layers
           A__       =    num_attention_heads
           A__       =    intermediate_size
           A__       =    hidden_act
           A__       =    hidden_dropout_prob
           A__       =    attention_probs_dropout_prob
           A__       =    max_position_embeddings
           A__       =    eos_token_id
           A__       =    pad_token_id
           A__       =    bos_token_id
           A__       =    embed_dim
           A__       =    word_embed_proj_dim
           A__       =    False


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->List[Any]:





           '''simple docstring'''

           A__       =    ids_tensor([self.batch_size, self.seq_length - 1]					, self.vocab_size)
           A__       =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)					, 1)
           A__       =    tf.concat([input_ids, eos_tensor]					, axis=1)

           A__       =    self.config_cls(
               vocab_size=self.vocab_size					, hidden_size=self.hidden_size					, num_hidden_layers=self.num_hidden_layers					, num_attention_heads=self.num_attention_heads					, ffn_dim=self.intermediate_size					, dropout=self.hidden_dropout_prob					, attention_dropout=self.attention_probs_dropout_prob					, max_position_embeddings=self.max_position_embeddings					, eos_token_id=self.eos_token_id					, bos_token_id=self.bos_token_id					, pad_token_id=self.pad_token_id					, embed_dim=self.embed_dim					, word_embed_proj_dim=self.word_embed_proj_dim					, is_encoder_decoder=UpperCAmelCase__					, **self.config_updates					, )
           A__       =    prepare_opt_inputs_dict(UpperCAmelCase__					, UpperCAmelCase__)
           return config, inputs_dict





    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	int)						->Optional[Any]:





           '''simple docstring'''

           A__       =    TFOPTModel(config=UpperCAmelCase__)
           A__       =    inputs_dict['''input_ids''']

           A__       =    input_ids[:1, :]
           A__       =    inputs_dict['''attention_mask'''][:1, :]
           A__       =    1

           # first forward pass
           A__       =    model(UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, use_cache=UpperCAmelCase__)

           A__ ,							A__       =    outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           A__       =    ids_tensor((self.batch_size, 3)					, config.vocab_size)
           A__       =    tf.cast(ids_tensor((self.batch_size, 3)					, 2)					, tf.inta)

           # append to next input_ids and
           A__       =    tf.concat([input_ids, next_tokens]					, axis=-1)
           A__       =    tf.concat([attention_mask, next_attn_mask]					, axis=-1)

           A__       =    model(UpperCAmelCase__					, attention_mask=UpperCAmelCase__)[0]
           A__       =    model(UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, past_key_values=UpperCAmelCase__)[0]

           self.parent.assertEqual(next_tokens.shape[1]					, output_from_past.shape[1])

           # select random slice
           A__       =    int(ids_tensor((1,)					, output_from_past.shape[-1]))
           A__       =    output_from_no_past[:, -3:, random_slice_idx]
           A__       =    output_from_past[:, :, random_slice_idx]

           # test that outputs are equal for slice
           tf.debugging.assert_near(UpperCAmelCase__					, UpperCAmelCase__					, rtol=1e-3)


@require_tf
class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
    UpperCAmelCase__							  =      (TFOPTForCausalLM,) if is_tf_available() else ()
    UpperCAmelCase__							  =      (
        {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
    )
    UpperCAmelCase__							  =      False
    UpperCAmelCase__							  =      False
    UpperCAmelCase__							  =      False
    UpperCAmelCase__							  =      10


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Any:





           '''simple docstring'''

           A__       =    TFOPTModelTester(self)
           A__       =    ConfigTester(self					, config_class=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->List[Any]:





           '''simple docstring'''

           self.config_tester.run_common_tests()


    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->Dict:





           '''simple docstring'''

           A__       =    self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Any:





           '''simple docstring'''

           A__ ,							A__       =    self.model_tester.prepare_config_and_inputs_for_common()

           def _get_word_embedding_weight(UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	Optional[Any]):
                  if hasattr(UpperCAmelCase__					, '''weight'''):
                         return embedding_layer.weight
                  else:
                         # Here we build the word embeddings weights if not exists.
                         # And then we retry to get the attribute once built.
                         model.build()
                         if hasattr(UpperCAmelCase__					, '''weight'''):
                                return embedding_layer.weight
                         else:
                                return None

           for model_class in self.all_model_classes:
                  for size in [config.vocab_size - 10, config.vocab_size + 10]:
                         # build the embeddings
                         A__       =    model_class(config=UpperCAmelCase__)
                         A__       =    _get_word_embedding_weight(UpperCAmelCase__					, model.get_input_embeddings())
                         A__       =    _get_word_embedding_weight(UpperCAmelCase__					, model.get_output_embeddings())

                         # reshape the embeddings
                         model.resize_token_embeddings(UpperCAmelCase__)
                         A__       =    _get_word_embedding_weight(UpperCAmelCase__					, model.get_input_embeddings())
                         A__       =    _get_word_embedding_weight(UpperCAmelCase__					, model.get_output_embeddings())

                         # check that the resized embeddings size matches the desired size.
                         A__       =    size if size is not None else config.vocab_size

                         self.assertEqual(new_input_embeddings.shape[0]					, UpperCAmelCase__)

                         # check that weights remain the same after resizing
                         A__       =    True
                         for pa, pa in zip(old_input_embeddings.value()					, new_input_embeddings.value()):
                                if tf.math.reduce_sum(tf.math.abs(pa - pa)) > 0:
                                       A__       =    False
                         self.assertTrue(UpperCAmelCase__)

                         if old_output_embeddings is not None and new_output_embeddings is not None:
                                self.assertEqual(new_output_embeddings.shape[0]					, UpperCAmelCase__)

                                A__       =    True
                                for pa, pa in zip(old_output_embeddings.value()					, new_output_embeddings.value()):
                                       if tf.math.reduce_sum(tf.math.abs(pa - pa)) > 0:
                                              A__       =    False
                                self.assertTrue(UpperCAmelCase__)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Dict:



       """simple docstring"""

       return tf.constant(lowercase_ ,				dtype=tf.intaa						)


@require_tf
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      99


    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Optional[Any]:





           '''simple docstring'''

           A__       =    tf.ones((4, 1)					, dtype=tf.intaa) * 2
           A__       =    tf.concat([ids_tensor((4, 6)					, self.vocab_size - 3) + 3, eos_column_vector]					, axis=1)
           A__       =    input_ids.shape[0]
           A__       =    OPTConfig(
               vocab_size=self.vocab_size					, hidden_size=24					, num_hidden_layers=2					, num_attention_heads=2					, ffn_dim=32					, max_position_embeddings=48					, eos_token_id=2					, pad_token_id=1					, bos_token_id=0					, )
           return config, input_ids, batch_size


@require_sentencepiece
@require_tf
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Dict:





           '''simple docstring'''

           A__       =    TFOPTModel.from_pretrained('''facebook/opt-350m''')
           A__       =    _long_tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]])
           A__       =    tf.not_equal(UpperCAmelCase__					, model.config.pad_token_id)
           with tf.GradientTape():
                  A__       =    model(input_ids=UpperCAmelCase__					, attention_mask=UpperCAmelCase__).last_hidden_state
           A__       =    (1, 11, 512)
           self.assertEqual(output.shape					, UpperCAmelCase__)
           A__       =    tf.constant(
               [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]])
           self.assertTrue(np.allclose(output[:, :3, :3]					, UpperCAmelCase__					, atol=4e-3))

           A__       =    tf.function(UpperCAmelCase__					, jit_compile=UpperCAmelCase__)
           A__       =    xla_generate(UpperCAmelCase__					, UpperCAmelCase__)[0]
           self.assertTrue(np.allclose(output[:, :3, :3]					, UpperCAmelCase__					, atol=4e-2))


@require_tf
@slow
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->str:





           '''simple docstring'''

           super().setUp()
           A__       =    '''facebook/opt-350m'''





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Optional[int]:





           '''simple docstring'''

           A__       =    TFOPTForCausalLM.from_pretrained(self.path_model)
           A__       =    GPTaTokenizer.from_pretrained(self.path_model)

           A__       =    [
               '''Today is a beautiful day and I want to''',
               '''In the city of''',
               '''Paris is the capital of France and''',
               '''Computers and mobile phones have taken''',
           ]
           # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
           A__       =    tokenizer(UpperCAmelCase__					, return_tensors='''tf'''					, padding=UpperCAmelCase__					, add_special_tokens=UpperCAmelCase__)
           A__       =    tf.math.reduce_mean(model(inputs.input_ids					, attention_mask=inputs.attention_mask)[0]					, axis=-1)
           A__       =    tf.constant(
               [
                   [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                   [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                   [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                   [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
               ])
           self.assertTrue(np.allclose(UpperCAmelCase__					, UpperCAmelCase__					, atol=1e-4))

           A__       =    tf.function(UpperCAmelCase__					, jit_compile=UpperCAmelCase__)
           A__       =    tf.math.reduce_mean(xla_generate(inputs.input_ids					, attention_mask=inputs.attention_mask)[0]					, axis=-1)
           self.assertTrue(np.allclose(UpperCAmelCase__					, UpperCAmelCase__					, atol=1e-4))


@require_tf
@slow
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->int:





           '''simple docstring'''

           return [
               "Today is a beautiful day and I want",
               "In the city of",
               "Paris is the capital of France and",
               "Computers and mobile phones have taken",
           ]


    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->Dict:





           '''simple docstring'''

           A__       =    '''facebook/opt-125m'''

           A__       =    [
               '''Today is a beautiful day and I want to''',
               '''In the city of New York, the city''',
               '''Paris is the capital of France and the capital''',
               '''Computers and mobile phones have taken over the''',
           ]

           A__       =    []
           A__       =    GPTaTokenizer.from_pretrained(UpperCAmelCase__)
           A__       =    TFOPTForCausalLM.from_pretrained(UpperCAmelCase__)

           for prompt in self.prompts:
                  A__       =    tokenizer(UpperCAmelCase__					, return_tensors='''tf''').input_ids

                  A__       =    model.generate(UpperCAmelCase__					, max_length=10)

                  A__       =    tokenizer.batch_decode(UpperCAmelCase__					, skip_special_tokens=UpperCAmelCase__)
                  predicted_outputs += generated_string

           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Optional[Any]:





           '''simple docstring'''

           A__       =    '''facebook/opt-350m'''

           A__       =    GPTaTokenizer.from_pretrained(UpperCAmelCase__)
           A__       =    TFOPTForCausalLM.from_pretrained(UpperCAmelCase__)

           A__       =    '''left'''

           # use different length sentences to test batching
           A__       =    [
               '''Hello, my dog is a little''',
               '''Today, I''',
           ]

           A__       =    tokenizer(UpperCAmelCase__					, return_tensors='''tf'''					, padding=UpperCAmelCase__)
           A__       =    inputs['''input_ids''']

           A__       =    model.generate(input_ids=UpperCAmelCase__					, attention_mask=inputs['''attention_mask'''])

           A__       =    tokenizer(sentences[0]					, return_tensors='''tf''').input_ids
           A__       =    model.generate(input_ids=UpperCAmelCase__)

           A__       =    inputs_non_padded.shape[-1] - tf.math.reduce_sum(
               tf.cast(inputs['''attention_mask'''][-1]					, tf.intaa))
           A__       =    tokenizer(sentences[1]					, return_tensors='''tf''').input_ids
           A__       =    model.generate(input_ids=UpperCAmelCase__					, max_length=model.config.max_length - num_paddings)

           A__       =    tokenizer.batch_decode(UpperCAmelCase__					, skip_special_tokens=UpperCAmelCase__)
           A__       =    tokenizer.decode(output_non_padded[0]					, skip_special_tokens=UpperCAmelCase__)
           A__       =    tokenizer.decode(output_padded[0]					, skip_special_tokens=UpperCAmelCase__)

           A__       =    [
               '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
               '''Today, I was in the middle of a conversation with a friend about the''',
           ]
           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__					, [non_padded_sentence, padded_sentence])





    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->Dict:





           '''simple docstring'''

           A__       =    '''facebook/opt-350m'''

           A__       =    [
               '''Today is a beautiful day and I want to''',
               '''In the city of San Francisco, the city''',
               '''Paris is the capital of France and the capital''',
               '''Computers and mobile phones have taken over the''',
           ]

           A__       =    []
           A__       =    GPTaTokenizer.from_pretrained(UpperCAmelCase__)
           A__       =    TFOPTForCausalLM.from_pretrained(UpperCAmelCase__)

           for prompt in self.prompts:
                  A__       =    tokenizer(UpperCAmelCase__					, return_tensors='''tf''').input_ids

                  A__       =    model.generate(UpperCAmelCase__					, max_length=10)

                  A__       =    tokenizer.batch_decode(UpperCAmelCase__					, skip_special_tokens=UpperCAmelCase__)
                  predicted_outputs += generated_string

           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)








import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)   ->       List[Any]:



       """simple docstring"""

       A__       =    StableDiffusionPipeline.from_pretrained(lowercase_ ,				torch_dtype=torch.floataa						)

       # load LoRA weight from .safetensors
       A__       =    load_file(lowercase_						)

       A__       =    []

       # directly update weight in diffusers model
       for key in state_dict:
              # it is suggested to print out the key, it usually will be something like below
              # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

              # as we have set the alpha beforehand, so just skip
              if ".alpha" in key or key in visited:
                     continue

              if "text" in key:
                     A__       =    key.split('''.'''						)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''						)[-1].split('''_'''						)
                     A__       =    pipeline.text_encoder
              else:
                     A__       =    key.split('''.'''						)[0].split(LORA_PREFIX_UNET + '''_'''						)[-1].split('''_'''						)
                     A__       =    pipeline.unet

              # find the target layer
              A__       =    layer_infos.pop(0						)
              while len(lowercase_						) > -1:
                     try:
                            A__       =    curr_layer.__getattr__(lowercase_						)
                            if len(lowercase_						) > 0:
                                   A__       =    layer_infos.pop(0						)
                            elif len(lowercase_						) == 0:
                                   break
                     except Exception:
                            if len(lowercase_						) > 0:
                                   temp_name += "_" + layer_infos.pop(0						)
                            else:
                                   A__       =    layer_infos.pop(0						)

              A__       =    []
              if "lora_down" in key:
                     pair_keys.append(key.replace('''lora_down''' ,				'''lora_up'''						)						)
                     pair_keys.append(lowercase_						)
              else:
                     pair_keys.append(lowercase_						)
                     pair_keys.append(key.replace('''lora_up''' ,				'''lora_down'''						)						)

              # update weight
              if len(state_dict[pair_keys[0]].shape						) == 4:
                     A__       =    state_dict[pair_keys[0]].squeeze(3						).squeeze(2						).to(torch.floataa						)
                     A__       =    state_dict[pair_keys[1]].squeeze(3						).squeeze(2						).to(torch.floataa						)
                     curr_layer.weight.data += alpha * torch.mm(lowercase_ ,				lowercase_						).unsqueeze(2						).unsqueeze(3						)
              else:
                     A__       =    state_dict[pair_keys[0]].to(torch.floataa						)
                     A__       =    state_dict[pair_keys[1]].to(torch.floataa						)
                     curr_layer.weight.data += alpha * torch.mm(lowercase_ ,				lowercase_						)

              # update visited list
              for item in pair_keys:
                     visited.append(lowercase_						)

       return pipeline


if __name__ == "__main__":
  _lowerCamelCase	:		Tuple							       =   argparse.ArgumentParser()

  parser.add_argument(
      """--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
  )
  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
  )
  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
  parser.add_argument(
      """--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
  )
  parser.add_argument(
      """--lora_prefix_text_encoder""",
      default="""lora_te""",
      type=str,
      help="""The prefix of text encoder weight in safetensors""",
  )
  parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
  parser.add_argument(
      """--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
  )
  parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")

  _lowerCamelCase	:		Tuple							       =   parser.parse_args()

  _lowerCamelCase	:		List[Any]							       =   args.base_model_path
  _lowerCamelCase	:		Optional[int]							       =   args.checkpoint_path
  _lowerCamelCase	:		Dict							       =   args.dump_path
  _lowerCamelCase	:		Optional[Any]							       =   args.lora_prefix_unet
  _lowerCamelCase	:		Optional[int]							       =   args.lora_prefix_text_encoder
  _lowerCamelCase	:		List[Any]							       =   args.alpha

  _lowerCamelCase	:		int							       =   convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  _lowerCamelCase	:		Tuple							       =   pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


