



"""simple docstring"""


import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf

    from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments


@require_tf
class        a				(		unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Tuple					):
      for model_result in results.values():
        for batch_size, sequence_length in zip(model_result["""bs"""]   ,				model_result["""ss"""]					):
          _UpperCAmelCase						 =      model_result["""result"""][batch_size][sequence_length]
          self.assertIsNotNone(__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				eager_mode=__lowerCAmelCase   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase					)
      _UpperCAmelCase						 =      benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result					)
      self.check_results_dict_not_empty(results.memory_inference_result					)


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      """sgugger/tiny-distilbert-classification"""
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				multi_process=__lowerCAmelCase   ,				only_pretrain_model=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase					)
      _UpperCAmelCase						 =      benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result					)
      self.check_results_dict_not_empty(results.memory_inference_result					)


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase					)
      _UpperCAmelCase						 =      benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result					)
      self.check_results_dict_not_empty(results.memory_inference_result					)


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      _UpperCAmelCase						 =      AutoConfig.from_pretrained(__lowerCAmelCase					)
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				eager_mode=__lowerCAmelCase   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase   ,				[config]					)
      _UpperCAmelCase						 =      benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result					)
      self.check_results_dict_not_empty(results.memory_inference_result					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      _UpperCAmelCase						 =      AutoConfig.from_pretrained(__lowerCAmelCase					)
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase   ,				[config]					)
      _UpperCAmelCase						 =      benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result					)
      self.check_results_dict_not_empty(results.memory_inference_result					)


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase					)
      _UpperCAmelCase						 =      benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result					)
      self.check_results_dict_not_empty(results.memory_train_result					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      _UpperCAmelCase						 =      AutoConfig.from_pretrained(__lowerCAmelCase					)
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase   ,				[config]					)
      _UpperCAmelCase						 =      benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result					)
      self.check_results_dict_not_empty(results.memory_train_result					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      """patrickvonplaten/t5-tiny-random"""
      _UpperCAmelCase						 =      AutoConfig.from_pretrained(__lowerCAmelCase					)
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase   ,				configs=[config]					)
      _UpperCAmelCase						 =      benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result					)
      self.check_results_dict_not_empty(results.memory_inference_result					)


    @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU"""					)					) == 0   ,				"""Cannot do xla on CPU."""					)
    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				use_xla=__lowerCAmelCase   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase					)
      _UpperCAmelCase						 =      benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result					)
      self.check_results_dict_not_empty(results.memory_inference_result					)


    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      with tempfile.TemporaryDirectory() as tmp_dir:
        _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
            models=[MODEL_ID]   ,				inference=__lowerCAmelCase   ,				save_to_csv=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				inference_time_csv_file=os.path.join(__lowerCAmelCase   ,				"""inf_time.csv"""					)   ,				inference_memory_csv_file=os.path.join(__lowerCAmelCase   ,				"""inf_mem.csv"""					)   ,				env_info_csv_file=os.path.join(__lowerCAmelCase   ,				"""env.csv"""					)   ,				multi_process=__lowerCAmelCase   ,				)
        _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase					)
        benchmark.run()
        self.assertTrue(Path(os.path.join(__lowerCAmelCase   ,				"""inf_time.csv"""					)					).exists()					)
        self.assertTrue(Path(os.path.join(__lowerCAmelCase   ,				"""inf_mem.csv"""					)					).exists()					)
        self.assertTrue(Path(os.path.join(__lowerCAmelCase   ,				"""env.csv"""					)					).exists()					)


    def      lowerCAmelCase_  (					self	:  str					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""

      def _check_summary_is_not_empty(__lowerCAmelCase	:  List[str]					):
        self.assertTrue(hasattr(__lowerCAmelCase   ,				"""sequential"""					)					)
        self.assertTrue(hasattr(__lowerCAmelCase   ,				"""cumulative"""					)					)
        self.assertTrue(hasattr(__lowerCAmelCase   ,				"""current"""					)					)
        self.assertTrue(hasattr(__lowerCAmelCase   ,				"""total"""					)					)

      with tempfile.TemporaryDirectory() as tmp_dir:
        _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
            models=[MODEL_ID]   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				log_filename=os.path.join(__lowerCAmelCase   ,				"""log.txt"""					)   ,				log_print=__lowerCAmelCase   ,				trace_memory_line_by_line=__lowerCAmelCase   ,				eager_mode=__lowerCAmelCase   ,				multi_process=__lowerCAmelCase   ,				)
        _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase					)
        _UpperCAmelCase						 =      benchmark.run()
        _check_summary_is_not_empty(result.inference_summary					)
        self.assertTrue(Path(os.path.join(__lowerCAmelCase   ,				"""log.txt"""					)					).exists()					)

"""simple docstring"""


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

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

    parser.add_argument(
        """--txt2img_unclip""",
        default="""kakaobrain/karlo-v1-alpha""",
        type=str,
        required=False,
        help="""The pretrained txt2img unclip.""",
    )

    UpperCAmelCase__  		=						parser.parse_args()

    UpperCAmelCase__  		=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

    UpperCAmelCase__  		=						CLIPImageProcessor()
    UpperCAmelCase__  		=						CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

    UpperCAmelCase__  		=						UnCLIPImageVariationPipeline(
        decoder=txtaimg.decoder,
        text_encoder=txtaimg.text_encoder,
        tokenizer=txtaimg.tokenizer,
        text_proj=txtaimg.text_proj,
        feature_extractor=feature_extractor,
        image_encoder=image_encoder,
        super_res_first=txtaimg.super_res_first,
        super_res_last=txtaimg.super_res_last,
        decoder_scheduler=txtaimg.decoder_scheduler,
        super_res_scheduler=txtaimg.super_res_scheduler,
    )

    imgaimg.save_pretrained(args.dump_path)
