
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Namespace				)			->						List[str]:
       return ConvertCommand(
           args.model_type			,							args.tf_checkpoint			,							args.pytorch_dump_output			,							args.config			,							args.finetuning_task_name				)


_lowercase											=    """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""


class 			lowercase_		(      A       ):


       @staticmethod
       def 	_snake_case				(    __A				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=parser.add_parser(
                  '''convert'''						,       help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'''						,       )
              train_parser.add_argument('''--model_type'''						,       type=__A						,       required=__A						,       help='''Model\'s type.'''				)
              train_parser.add_argument(
                  '''--tf_checkpoint'''						,       type=__A						,       required=__A						,       help='''TensorFlow checkpoint path or folder.'''				)
              train_parser.add_argument(
                  '''--pytorch_dump_output'''						,       type=__A						,       required=__A						,       help='''Path to the PyTorch saved model output.'''				)
              train_parser.add_argument('''--config'''						,       type=__A						,       default=''''''						,       help='''Configuration file path or folder.'''				)
              train_parser.add_argument(
                  '''--finetuning_task_name'''						,       type=__A						,       default=__A						,       help='''Optional fine-tuning task name if the TF model was a finetuned model.'''						,       )
              train_parser.set_defaults(func=__A				)


       def __init__(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       *__A						,       )						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=logging.get_logger('''transformers-cli/converting'''				)

              self._logger.info(F'Loading model {model_type}'				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=model_type
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=tf_checkpoint
              SCREAMING_SNAKE_CASE_							:				str		=pytorch_dump_output
              SCREAMING_SNAKE_CASE_							:				Dict		=config
              SCREAMING_SNAKE_CASE_							:				List[Any]		=finetuning_task_name





       def 	_snake_case				(    self				)						->    Optional[int]:
              if self._model_type == "albert":
                     try:
                            from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                                convert_tf_checkpoint_to_pytorch,
                            )
                     except ImportError:
                            raise ImportError(__A				)

                     convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,       self._config						,       self._pytorch_dump_output				)
              elif self._model_type == "bert":
                     try:
                            from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                                convert_tf_checkpoint_to_pytorch,
                            )
                     except ImportError:
                            raise ImportError(__A				)

                     convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,       self._config						,       self._pytorch_dump_output				)
              elif self._model_type == "funnel":
                     try:
                            from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                                convert_tf_checkpoint_to_pytorch,
                            )
                     except ImportError:
                            raise ImportError(__A				)

                     convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,       self._config						,       self._pytorch_dump_output				)
              elif self._model_type == "t5":
                     try:
                            from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
                     except ImportError:
                            raise ImportError(__A				)

                     convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,       self._config						,       self._pytorch_dump_output				)
              elif self._model_type == "gpt":
                     from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
                         convert_openai_checkpoint_to_pytorch,
                     )

                     convert_openai_checkpoint_to_pytorch(self._tf_checkpoint						,       self._config						,       self._pytorch_dump_output				)
              elif self._model_type == "transfo_xl":
                     try:
                            from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                                convert_transfo_xl_checkpoint_to_pytorch,
                            )
                     except ImportError:
                            raise ImportError(__A				)

                     if "ckpt" in self._tf_checkpoint.lower():
                            SCREAMING_SNAKE_CASE_							:				str		=self._tf_checkpoint
                            SCREAMING_SNAKE_CASE_							:				Tuple		=''''''
                     else:
                            SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self._tf_checkpoint
                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=''''''
                     convert_transfo_xl_checkpoint_to_pytorch(
                         __A						,       self._config						,       self._pytorch_dump_output						,       __A				)
              elif self._model_type == "gpt2":
                     try:
                            from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                                convert_gpta_checkpoint_to_pytorch,
                            )
                     except ImportError:
                            raise ImportError(__A				)

                     convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint						,       self._config						,       self._pytorch_dump_output				)
              elif self._model_type == "xlnet":
                     try:
                            from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                                convert_xlnet_checkpoint_to_pytorch,
                            )
                     except ImportError:
                            raise ImportError(__A				)

                     convert_xlnet_checkpoint_to_pytorch(
                         self._tf_checkpoint						,       self._config						,       self._pytorch_dump_output						,       self._finetuning_task_name				)
              elif self._model_type == "xlm":
                     from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
                         convert_xlm_checkpoint_to_pytorch,
                     )

                     convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint						,       self._pytorch_dump_output				)
              elif self._model_type == "lxmert":
                     from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
                         convert_lxmert_checkpoint_to_pytorch,
                     )

                     convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint						,       self._pytorch_dump_output				)
              elif self._model_type == "rembert":
                     from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
                         convert_rembert_tf_checkpoint_to_pytorch,
                     )

                     convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,       self._config						,       self._pytorch_dump_output				)
              else:
                     raise ValueError(
                         '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'''				)


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()


class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						PriorTransformer
       __lowerCamelCase				       =						"hidden_states"


       @property
       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=4
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=8
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=7

              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, num_embeddings, embedding_dim)				).to(__A				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }


       def 	_snake_case				(    self						,       __A=0				)						->    int:
              torch.manual_seed(__A				)
              SCREAMING_SNAKE_CASE_							:				str		=4
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=8
              SCREAMING_SNAKE_CASE_							:				List[Any]		=7

              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.randn((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				int		=torch.randn((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.randn((batch_size, num_embeddings, embedding_dim)				).to(__A				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }


       @property
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return (4, 8)


       @property
       def 	_snake_case				(    self				)						->    int:
              return (4, 8)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		={
                  '''num_attention_heads''': 2,
                  '''attention_head_dim''': 4,
                  '''num_layers''': 2,
                  '''embedding_dim''': 8,
                  '''num_embeddings''': 7,
                  '''additional_embeddings''': 4,
              }
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.dummy_input
              return init_dict, inputs_dict


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Any		=PriorTransformer.from_pretrained(
                  '''hf-internal-testing/prior-dummy'''						,       output_loading_info=__A				)
              self.assertIsNotNone(__A				)
              self.assertEqual(len(loading_info['''missing_keys''']				)						,       0				)

              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(**self.dummy_input				)[0]

              assert hidden_states is not None, "Make sure output is not None"


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.prepare_init_args_and_inputs_for_common()

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.model_class(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=['''hidden_states''', '''timestep''']
              self.assertListEqual(arg_names[:2]						,       __A				)





       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Dict		=PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=model.to(__A				)

              if hasattr(__A						,       '''set_default_attn_processor'''				):
                     model.set_default_attn_processor()

              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_dummy_seed_input()

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				str		=model(**__A				)[0]

              SCREAMING_SNAKE_CASE_							:				Any		=output[0, :5].flatten().cpu()
              print(__A				)

              # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
              # the expected output slices are not the same for CPU and GPU.
              SCREAMING_SNAKE_CASE_							:				int		=torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]				)
              self.assertTrue(torch_all_close(__A						,       __A						,       rtol=1e-2				)				)






@slow
class 			lowercase_		(      unittest.TestCase       ):


       def 	_snake_case				(    self						,       __A=1						,       __A=768						,       __A=77						,       __A=0				)						->    str:
              torch.manual_seed(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=batch_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=embedding_dim
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=num_embeddings

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.randn((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.randn((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.randn((batch_size, num_embeddings, embedding_dim)				).to(__A				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }


       def 	_snake_case				(    self				)						->    Dict:
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()





       @parameterized.expand(
           [
               # fmt: off
               [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
               [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
               # fmt: on
           ]				)
       def 	_snake_case				(    self						,       __A						,       __A				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Dict		=PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''						,       subfolder='''prior'''				)
              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_dummy_seed_input(seed=__A				)

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Dict		=model(**__A				)[0]

              assert list(sample.shape				) == [1, 768]

              SCREAMING_SNAKE_CASE_							:				Dict		=sample[0, :8].flatten().cpu()
              print(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor(__A				)

              assert torch_all_close(__A						,       __A						,       atol=1e-3				)
