




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 					snake_case__							(unittest.TestCase     ):
    """simple docstring"""



    def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   __lowerCamelCase       :  Tuple							)      ->  Tuple:
       for model_result in results.values():
          for batch_size, sequence_length in zip(model_result["bs"]      ,   model_result["ss"]							):
             a					=    model_result["result"][batch_size][sequence_length]
             self.assertIsNotNone(__lowerCamelCase							)





    def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Optional[int]:
       a					=    "sshleifer/tiny-gpt2"
       a					=    TensorFlowBenchmarkArguments(
           models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   eager_mode=__lowerCamelCase      ,   multi_process=__lowerCamelCase      ,   )
       a					=    TensorFlowBenchmark(__lowerCamelCase							)
       a					=    benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result							)
       self.check_results_dict_not_empty(results.memory_inference_result							)





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





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





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





    def 							__UpperCAmelCase							(   self       :  Dict							)      ->  Union[str, Any]:
       a					=    "sshleifer/tiny-gpt2"
       a					=    AutoConfig.from_pretrained(__lowerCamelCase							)
       a					=    TensorFlowBenchmarkArguments(
           models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=__lowerCamelCase      ,   )
       a					=    TensorFlowBenchmark(__lowerCamelCase      ,   [config]							)
       a					=    benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result							)
       self.check_results_dict_not_empty(results.memory_inference_result							)





    def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Optional[Any]:
       a					=    "sshleifer/tiny-gpt2"
       a					=    TensorFlowBenchmarkArguments(
           models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=__lowerCamelCase      ,   )
       a					=    TensorFlowBenchmark(__lowerCamelCase							)
       a					=    benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result							)
       self.check_results_dict_not_empty(results.memory_train_result							)





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





    def 							__UpperCAmelCase							(   self       :  Dict							)      ->  Dict:
       a					=    "patrickvonplaten/t5-tiny-random"
       a					=    AutoConfig.from_pretrained(__lowerCamelCase							)
       a					=    TensorFlowBenchmarkArguments(
           models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=__lowerCamelCase      ,   )
       a					=    TensorFlowBenchmark(__lowerCamelCase      ,   configs=[config]							)
       a					=    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 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  List[Any]:
       a					=    "sshleifer/tiny-gpt2"
       a					=    TensorFlowBenchmarkArguments(
           models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   use_xla=__lowerCamelCase      ,   multi_process=__lowerCamelCase      ,   )
       a					=    TensorFlowBenchmark(__lowerCamelCase							)
       a					=    benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result							)
       self.check_results_dict_not_empty(results.memory_inference_result							)





    def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  List[str]:
       a					=    "sshleifer/tiny-gpt2"
       with tempfile.TemporaryDirectory() as tmp_dir:
          a					=    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      ,   )
          a					=    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 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  Optional[Any]:
       a					=    "sshleifer/tiny-gpt2"

       def _check_summary_is_not_empty(__lowerCamelCase       :  Optional[int]							):
          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:
          a					=    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      ,   )
          a					=    TensorFlowBenchmark(__lowerCamelCase							)
          a					=    benchmark.run()
          _check_summary_is_not_empty(result.inference_summary							)
          self.assertTrue(Path(os.path.join(__lowerCamelCase      ,   "log.txt"							)							).exists()							)












import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor




@require_vision
class 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  int							)      ->  Dict:
							a					=    tempfile.mkdtemp()

							a					=    [
							    "[UNK]",
							    "[CLS]",
							    "[SEP]",
							    "[PAD]",
							    "[MASK]",
							    "的",
							    "价",
							    "格",
							    "是",
							    "15",
							    "便",
							    "alex",
							    "##andra",
							    "，",
							    "。",
							    "-",
							    "t",
							    "shirt",
							]
							a					=    os.path.join(self.tmpdirname      ,   VOCAB_FILES_NAMES["vocab_file"]							)
							with open(self.vocab_file      ,   "w"      ,   encoding="utf-8"							) as vocab_writer:
										vocab_writer.write("".join([x + "\n" for x in vocab_tokens]							)							)

							a					=    {
							    "do_resize": True,
							    "size": {"height": 2_24, "width": 2_24},
							    "do_center_crop": True,
							    "crop_size": {"height": 18, "width": 18},
							    "do_normalize": True,
							    "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
							    "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
							    "do_convert_rgb": True,
							}
							a					=    os.path.join(self.tmpdirname      ,   __lowerCamelCase							)
							with open(self.image_processor_file      ,   "w"      ,   encoding="utf-8"							) as fp:
										json.dump(__lowerCamelCase      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Dict      ,   **__lowerCamelCase       :  Union[str, Any]							)      ->  List[Any]:
							return BertTokenizer.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  str      ,   **__lowerCamelCase       :  Optional[int]							)      ->  str:
							return BertTokenizerFast.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   **__lowerCamelCase       :  Optional[int]							)      ->  Tuple:
							return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Optional[Any]:
							shutil.rmtree(self.tmpdirname							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Optional[int]:
							a					=    [np.random.randint(2_55      ,   size=(3, 30, 4_00)      ,   dtype=np.uinta							)]

							a					=    [Image.fromarray(np.moveaxis(__lowerCamelCase      ,   0      ,   -1							)							) for x in image_inputs]

							return image_inputs





				def 							__UpperCAmelCase							(   self       :  int							)      ->  List[str]:
							a					=    self.get_tokenizer()
							a					=    self.get_rust_tokenizer()
							a					=    self.get_image_processor()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)
							processor_slow.save_pretrained(self.tmpdirname							)
							a					=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,   use_fast=__lowerCamelCase							)

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)
							processor_fast.save_pretrained(self.tmpdirname							)
							a					=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname							)

							self.assertEqual(processor_slow.tokenizer.get_vocab()      ,   tokenizer_slow.get_vocab()							)
							self.assertEqual(processor_fast.tokenizer.get_vocab()      ,   tokenizer_fast.get_vocab()							)
							self.assertEqual(tokenizer_slow.get_vocab()      ,   tokenizer_fast.get_vocab()							)
							self.assertIsInstance(processor_slow.tokenizer      ,   __lowerCamelCase							)
							self.assertIsInstance(processor_fast.tokenizer      ,   __lowerCamelCase							)

							self.assertEqual(processor_slow.image_processor.to_json_string()      ,   image_processor.to_json_string()							)
							self.assertEqual(processor_fast.image_processor.to_json_string()      ,   image_processor.to_json_string()							)
							self.assertIsInstance(processor_slow.image_processor      ,   __lowerCamelCase							)
							self.assertIsInstance(processor_fast.image_processor      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  List[Any]:
							a					=    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor()							)
							processor.save_pretrained(self.tmpdirname							)

							a					=    self.get_tokenizer(cls_token="(CLS)"      ,   sep_token="(SEP)"							)
							a					=    self.get_image_processor(do_normalize=__lowerCamelCase							)

							a					=    ChineseCLIPProcessor.from_pretrained(
							    self.tmpdirname      ,   cls_token="(CLS)"      ,   sep_token="(SEP)"      ,   do_normalize=__lowerCamelCase							)

							self.assertEqual(processor.tokenizer.get_vocab()      ,   tokenizer_add_kwargs.get_vocab()							)
							self.assertIsInstance(processor.tokenizer      ,   __lowerCamelCase							)

							self.assertEqual(processor.image_processor.to_json_string()      ,   image_processor_add_kwargs.to_json_string()							)
							self.assertIsInstance(processor.image_processor      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Dict:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

							a					=    self.prepare_image_inputs()

							a					=    image_processor(__lowerCamelCase      ,   return_tensors="np"							)
							a					=    processor(images=__lowerCamelCase      ,   return_tensors="np"							)

							for key in input_feat_extract.keys():
										self.assertAlmostEqual(input_feat_extract[key].sum()      ,   input_processor[key].sum()      ,   delta=1e-2							)





				def 							__UpperCAmelCase							(   self       :  str							)      ->  Optional[int]:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

							a					=    "Alexandra，T-shirt的价格是15便士。"

							a					=    processor(text=__lowerCamelCase							)

							a					=    tokenizer(__lowerCamelCase							)

							for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]      ,   encoded_processor[key]							)





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Any:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

							a					=    "Alexandra，T-shirt的价格是15便士。"
							a					=    self.prepare_image_inputs()

							a					=    processor(text=__lowerCamelCase      ,   images=__lowerCamelCase							)

							self.assertListEqual(list(inputs.keys()							)      ,   ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]							)

							# test if it raises when no input is passed
							with pytest.raises(__lowerCamelCase							):
										processor()





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Optional[int]:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

							a					=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							a					=    processor.batch_decode(__lowerCamelCase							)
							a					=    tokenizer.batch_decode(__lowerCamelCase							)

							self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  List[str]:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

							a					=    "Alexandra，T-shirt的价格是15便士。"
							a					=    self.prepare_image_inputs()

							a					=    processor(text=__lowerCamelCase      ,   images=__lowerCamelCase							)

							self.assertListEqual(list(inputs.keys()							)      ,   processor.model_input_names							)






