

from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
  import tensorflow as tf

  from transformers.models.xglm.modeling_tf_xglm import (
      TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXGLMForCausalLM,
      TFXGLMModel,
  )







@require_tf
class        a__       :







  """simple docstring"""




  UpperCAmelCase__      : str			       =XGLMConfig
  UpperCAmelCase__      : Dict			       ={}
  UpperCAmelCase__      : Any			       ="""gelu"""


  def __init__(			self    : Any				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : List[str]=1_4				,				UpperCAmelCase__    : Union[str, Any]=7				,				UpperCAmelCase__    : Tuple=True				,				UpperCAmelCase__    : List[str]=True				,				UpperCAmelCase__    : int=True				,				UpperCAmelCase__    : Optional[Any]=9_9				,				UpperCAmelCase__    : Dict=3_2				,				UpperCAmelCase__    : Union[str, Any]=2				,				UpperCAmelCase__    : List[Any]=4				,				UpperCAmelCase__    : List[str]=3_7				,				UpperCAmelCase__    : Optional[int]="gelu"				,				UpperCAmelCase__    : Dict=0.1				,				UpperCAmelCase__    : List[str]=0.1				,				UpperCAmelCase__    : str=5_1_2				,				UpperCAmelCase__    : List[str]=0.02				,				)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   parent
        SCREAMING_SNAKE_CASE :      str       =   batch_size
        SCREAMING_SNAKE_CASE :      Optional[int]       =   seq_length
        SCREAMING_SNAKE_CASE :      Dict       =   is_training
        SCREAMING_SNAKE_CASE :      Tuple       =   use_input_mask
        SCREAMING_SNAKE_CASE :      List[str]       =   use_labels
        SCREAMING_SNAKE_CASE :      List[str]       =   vocab_size
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   d_model
        SCREAMING_SNAKE_CASE :      int       =   num_hidden_layers
        SCREAMING_SNAKE_CASE :      int       =   num_attention_heads
        SCREAMING_SNAKE_CASE :      str       =   ffn_dim
        SCREAMING_SNAKE_CASE :      Any       =   activation_function
        SCREAMING_SNAKE_CASE :      List[Any]       =   activation_dropout
        SCREAMING_SNAKE_CASE :      Optional[int]       =   attention_dropout
        SCREAMING_SNAKE_CASE :      Optional[int]       =   max_position_embeddings
        SCREAMING_SNAKE_CASE :      str       =   initializer_range
        SCREAMING_SNAKE_CASE :      Any       =   None
        SCREAMING_SNAKE_CASE :      int       =   0
        SCREAMING_SNAKE_CASE :      Optional[int]       =   2
        SCREAMING_SNAKE_CASE :      List[str]       =   1


  def  _lowercase      (			self    : str		)					->Optional[int]:
        """simple docstring"""







        return XGLMConfig.from_pretrained("""facebook/xglm-564M"""		)


  def  _lowercase      (			self    : Any		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   tf.clip_by_value(
            ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size		)				,				clip_value_min=0				,				clip_value_max=3		)

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

        SCREAMING_SNAKE_CASE :      Any       =   self.get_config()

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   floats_tensor([self.num_hidden_layers, self.num_attention_heads]				,				2		)

        return (
            config,
            input_ids,
            input_mask,
            head_mask,
        )


  def  _lowercase      (			self    : str		)					->str:
        """simple docstring"""







        return XGLMConfig(
            vocab_size=self.vocab_size				,				d_model=self.hidden_size				,				num_layers=self.num_hidden_layers				,				attention_heads=self.num_attention_heads				,				ffn_dim=self.ffn_dim				,				activation_function=self.activation_function				,				activation_dropout=self.activation_dropout				,				attention_dropout=self.attention_dropout				,				max_position_embeddings=self.max_position_embeddings				,				initializer_range=self.initializer_range				,				use_cache=UpperCAmelCase__				,				bos_token_id=self.bos_token_id				,				eos_token_id=self.eos_token_id				,				pad_token_id=self.pad_token_id				,				return_dict=UpperCAmelCase__				,				)




  def  _lowercase      (			self    : List[str]		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   self.prepare_config_and_inputs()

        (
            (
            SCREAMING_SNAKE_CASE
        )     ,	(
            SCREAMING_SNAKE_CASE
        )     ,	(
            SCREAMING_SNAKE_CASE
        )     ,	(
            SCREAMING_SNAKE_CASE
        )     ,	
        ) :      Any       =   config_and_inputs

        SCREAMING_SNAKE_CASE :      int       =   {
            """input_ids""": input_ids,
            """head_mask""": head_mask,
        }

        return config, inputs_dict







@require_tf
class        a__       (   UpperCAmelCase	,			UpperCAmelCase	,			unittest.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Optional[Any]			       =(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
  UpperCAmelCase__      : str			       =(TFXGLMForCausalLM,) if is_tf_available() else ()
  UpperCAmelCase__      : Union[str, Any]			       =(
      {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
  )
  UpperCAmelCase__      : Dict			       =False
  UpperCAmelCase__      : str			       =False
  UpperCAmelCase__      : List[Any]			       =False


  def  _lowercase      (			self    : Union[str, Any]		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   TFXGLMModelTester(self		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   ConfigTester(self				,				config_class=UpperCAmelCase__				,				n_embd=3_7		)


  def  _lowercase      (			self    : List[str]		)					->Tuple:
        """simple docstring"""







        self.config_tester.run_common_tests()


  @slow
  def  _lowercase      (			self    : Any		)					->List[Any]:
        """simple docstring"""







        for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE :      List[Any]       =   TFXGLMModel.from_pretrained(UpperCAmelCase__		)
              self.assertIsNotNone(UpperCAmelCase__		)




  @unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""		)
  def  _lowercase      (			self    : Tuple		)					->Optional[int]:
        """simple docstring"""







        super().test_resize_token_embeddings()







@require_tf
class        a__       (   unittest.TestCase       ):







  """simple docstring"""




  @slow
  def  _lowercase      (			self    : int				,				UpperCAmelCase__    : Union[str, Any]=True		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""		)
        SCREAMING_SNAKE_CASE :      Any       =   tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]				,				dtype=tf.intaa		)  # The dog
        # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
        # fmt: off
        SCREAMING_SNAKE_CASE :      Tuple       =   [2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
        # fmt: on
        SCREAMING_SNAKE_CASE :      List[Any]       =   model.generate(UpperCAmelCase__				,				do_sample=UpperCAmelCase__				,				num_beams=1		)
        if verify_outputs:
              self.assertListEqual(output_ids[0].numpy().tolist()				,				UpperCAmelCase__		)


  @slow
  def  _lowercase      (			self    : Optional[Any]		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""		)

        tf.random.set_seed(0		)
        SCREAMING_SNAKE_CASE :      Tuple       =   tokenizer("""Today is a nice day and"""				,				return_tensors="""tf"""		)
        SCREAMING_SNAKE_CASE :      int       =   tokenized.input_ids
        # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
        with tf.device(""":/CPU:0"""		):
              SCREAMING_SNAKE_CASE :      List[str]       =   model.generate(UpperCAmelCase__				,				do_sample=UpperCAmelCase__				,				seed=[7, 0]		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   tokenizer.decode(output_ids[0]				,				skip_special_tokens=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      str       =   (
            """Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
        )
        self.assertEqual(UpperCAmelCase__				,				UpperCAmelCase__		)




  @slow
  def  _lowercase      (			self    : List[Any]		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""		)
        SCREAMING_SNAKE_CASE :      Any       =   XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""		)

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   """left"""

        # use different length sentences to test batching
        SCREAMING_SNAKE_CASE :      str       =   [
            """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
            """left-padding, such as in batched generation. The output for the sequence below should be the same """
            """regardless of whether left padding is applied or not. When""",
            """Hello, my dog is a little""",
        ]

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   tokenizer(UpperCAmelCase__				,				return_tensors="""tf"""				,				padding=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Dict       =   inputs["""input_ids"""]

        SCREAMING_SNAKE_CASE :      List[Any]       =   model.generate(input_ids=UpperCAmelCase__				,				attention_mask=inputs["""attention_mask"""]				,				max_new_tokens=1_2		)

        SCREAMING_SNAKE_CASE :      Dict       =   tokenizer(sentences[0]				,				return_tensors="""tf"""		).input_ids
        SCREAMING_SNAKE_CASE :      str       =   model.generate(input_ids=UpperCAmelCase__				,				max_new_tokens=1_2		)

        SCREAMING_SNAKE_CASE :      Optional[int]       =   tokenizer(sentences[1]				,				return_tensors="""tf"""		).input_ids
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   model.generate(input_ids=UpperCAmelCase__				,				max_new_tokens=1_2		)

        SCREAMING_SNAKE_CASE :      str       =   tokenizer.batch_decode(UpperCAmelCase__				,				skip_special_tokens=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   tokenizer.decode(output_non_padded[0]				,				skip_special_tokens=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Any       =   tokenizer.decode(output_padded[0]				,				skip_special_tokens=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   [
            """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
            """left-padding, such as in batched generation. The output for the sequence below should be the same """
            """regardless of whether left padding is applied or not. When left padding is applied, the sequence will be """
            """a single""",
            """Hello, my dog is a little bit of a shy one, but he is very friendly""",
        ]
        self.assertListEqual(UpperCAmelCase__				,				UpperCAmelCase__		)
        self.assertListEqual(UpperCAmelCase__				,				[non_padded_sentence, padded_sentence]		)







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCAmelCase__ :     Optional[Any]				    =    False
UpperCAmelCase__ :     Union[str, Any]				    =    True
UpperCAmelCase__ :     int				    =    False


if __name__ == "__main__":
  UpperCAmelCase__ :     List[Any]				    =    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.""")

  UpperCAmelCase__ :     List[str]				    =    parser.parse_args()

  UpperCAmelCase__ :     Dict				    =    {
      """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""",
  }

  UpperCAmelCase__ :     str				    =    {
      """time_steps""": """time_proj""",
      """mid""": """mid_block""",
      """downsample_blocks""": """down_blocks""",
      """upsample_blocks""": """up_blocks""",
  }

  UpperCAmelCase__ :     Union[str, Any]				    =    """""" 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:
    UpperCAmelCase__ :     Union[str, Any]				    =    reader.read()
    UpperCAmelCase__ :     Any				    =    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"""):
    UpperCAmelCase__ :     int				    =    UNetaDModel(**config)
  else:
    UpperCAmelCase__ :     Optional[Any]				    =    UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
    UpperCAmelCase__ :     int				    =    class_name(**config)

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

  UpperCAmelCase__ :     Optional[Any]				    =    dict(model.config)

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

    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

  if do_only_weights:
    UpperCAmelCase__ :     Dict				    =    torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

    UpperCAmelCase__ :     Optional[Any]				    =    {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith(""".op.bias""") or param_key.endswith(""".op.weight"""):
        continue
      UpperCAmelCase__ :     Union[str, Any]				    =    False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split(""".""")[0] == key:
          UpperCAmelCase__ :     List[Any]				    =    param_value
          UpperCAmelCase__ :     List[str]				    =    True
      if not has_changed:
        UpperCAmelCase__ :     Any				    =    param_value

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




