
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


_lowercase											=    """hf-internal-testing/tiny-random-bert"""
_lowercase											=    os.path.join(TRANSFORMERS_CACHE, """models--hf-internal-testing--tiny-random-bert""")
_lowercase											=    """9b8c223d42b2188cb49d29af482996f9d0f3e5a6"""


class 			lowercase_		(      unittest.TestCase       ):


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=cached_file(__A						,       __A				)
              # Should have downloaded the file in here
              self.assertTrue(os.path.isdir(__A				)				)
              # Cache should contain at least those three subfolders:
              for subfolder in ["blobs", "refs", "snapshots"]:
                     self.assertTrue(os.path.isdir(os.path.join(__A						,       __A				)				)				)
              with open(os.path.join(__A						,       '''refs'''						,       '''main'''				)				) as f:
                     SCREAMING_SNAKE_CASE_							:				Any		=f.read()
              self.assertEqual(__A						,       os.path.join(__A						,       '''snapshots'''						,       __A						,       __A				)				)
              self.assertTrue(os.path.isfile(__A				)				)

              # File is cached at the same place the second time.
              SCREAMING_SNAKE_CASE_							:				Dict		=cached_file(__A						,       __A				)
              self.assertEqual(__A						,       __A				)

              # Using a specific revision to test the full commit hash.
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=cached_file(__A						,       __A						,       revision='''9b8c223'''				)
              self.assertEqual(__A						,       os.path.join(__A						,       '''snapshots'''						,       __A						,       __A				)				)


       def 	_snake_case				(    self				)						->    Tuple:
              with self.assertRaisesRegex(__A						,       '''is not a valid model identifier'''				):
                     SCREAMING_SNAKE_CASE_							:				List[str]		=cached_file('''tiny-random-bert'''						,       __A				)

              with self.assertRaisesRegex(__A						,       '''is not a valid git identifier'''				):
                     SCREAMING_SNAKE_CASE_							:				Any		=cached_file(__A						,       __A						,       revision='''aaaa'''				)

              with self.assertRaisesRegex(__A						,       '''does not appear to have a file named'''				):
                     SCREAMING_SNAKE_CASE_							:				Any		=cached_file(__A						,       '''conf'''				)


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              with self.assertRaisesRegex(__A						,       '''does not appear to have a file named'''				):
                     SCREAMING_SNAKE_CASE_							:				int		=cached_file(__A						,       '''conf'''				)

              with open(os.path.join(__A						,       '''refs'''						,       '''main'''				)				) as f:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=f.read()
              self.assertTrue(os.path.isfile(os.path.join(__A						,       '''.no_exist'''						,       __A						,       '''conf'''				)				)				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=cached_file(__A						,       '''conf'''						,       _raise_exceptions_for_missing_entries=__A				)
              self.assertIsNone(__A				)

              SCREAMING_SNAKE_CASE_							:				Any		=cached_file(__A						,       '''conf'''						,       local_files_only=__A						,       _raise_exceptions_for_missing_entries=__A				)
              self.assertIsNone(__A				)

              SCREAMING_SNAKE_CASE_							:				str		=mock.Mock()
              SCREAMING_SNAKE_CASE_							:				int		=500
              SCREAMING_SNAKE_CASE_							:				Tuple		={}
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=HTTPError
              SCREAMING_SNAKE_CASE_							:				Optional[int]		={}

              # Under the mock environment we get a 500 error when trying to reach the tokenizer.
              with mock.patch('''requests.Session.request'''						,       return_value=__A				) as mock_head:
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=cached_file(__A						,       '''conf'''						,       _raise_exceptions_for_connection_errors=__A				)
                     self.assertIsNone(__A				)
                     # This check we did call the fake head request
                     mock_head.assert_called()


       def 	_snake_case				(    self				)						->    Tuple:
              self.assertTrue(has_file('''hf-internal-testing/tiny-bert-pt-only'''						,       __A				)				)
              self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only'''						,       __A				)				)
              self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only'''						,       __A				)				)


       def 	_snake_case				(    self				)						->    Any:
              # `get_file_from_repo` returns None if the file does not exist
              self.assertIsNone(get_file_from_repo('''bert-base-cased'''						,       '''ahah.txt'''				)				)

              # The function raises if the repository does not exist.
              with self.assertRaisesRegex(__A						,       '''is not a valid model identifier'''				):
                     get_file_from_repo('''bert-base-case'''						,       __A				)

              # The function raises if the revision does not exist.
              with self.assertRaisesRegex(__A						,       '''is not a valid git identifier'''				):
                     get_file_from_repo('''bert-base-cased'''						,       __A						,       revision='''ahaha'''				)

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=get_file_from_repo('''bert-base-cased'''						,       __A				)
              # The name is the cached name which is not very easy to test, so instead we load the content.
              SCREAMING_SNAKE_CASE_							:				Dict		=json.loads(open(__A						,       '''r'''				).read()				)
              self.assertEqual(config['''hidden_size''']						,       768				)





       def 	_snake_case				(    self				)						->    Any:
              with tempfile.TemporaryDirectory() as tmp_dir:
                     SCREAMING_SNAKE_CASE_							:				str		=Path(__A				) / '''a.txt'''
                     filename.touch()
                     self.assertEqual(get_file_from_repo(__A						,       '''a.txt'''				)						,       str(__A				)				)

                     self.assertIsNone(get_file_from_repo(__A						,       '''b.txt'''				)				)


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						(DDIMParallelScheduler,)
       __lowerCamelCase				       =						(("eta", 0.0), ("num_inference_steps", 5_0))


       def 	_snake_case				(    self						,       **__A				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		={
                  '''num_train_timesteps''': 1_000,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''clip_sample''': True,
              }

              config.update(**__A				)
              return config


       def 	_snake_case				(    self						,       **__A				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				int		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				str		=self.get_scheduler_config(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler_class(**__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=10, 0.0

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.dummy_sample_deter

              scheduler.set_timesteps(__A				)

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(__A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler.step(__A						,       __A						,       __A						,       __A				).prev_sample

              return sample


       def 	_snake_case				(    self				)						->    Optional[Any]:
              for timesteps in [100, 500, 1_000]:
                     self.check_over_configs(num_train_timesteps=__A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              for steps_offset in [0, 1]:
                     self.check_over_configs(steps_offset=__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_scheduler_config(steps_offset=1				)
              SCREAMING_SNAKE_CASE_							:				Dict		=scheduler_class(**__A				)
              scheduler.set_timesteps(5				)
              assert torch.equal(scheduler.timesteps						,       torch.LongTensor([801, 601, 401, 201, 1]				)				)


       def 	_snake_case				(    self				)						->    Dict:
              for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]						,       [0.002, 0.02, 0.2, 2]				):
                     self.check_over_configs(beta_start=__A						,       beta_end=__A				)


       def 	_snake_case				(    self				)						->    str:
              for schedule in ["linear", "squaredcos_cap_v2"]:
                     self.check_over_configs(beta_schedule=__A				)


       def 	_snake_case				(    self				)						->    List[str]:
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=__A				)


       def 	_snake_case				(    self				)						->    List[Any]:
              for clip_sample in [True, False]:
                     self.check_over_configs(clip_sample=__A				)


       def 	_snake_case				(    self				)						->    int:
              for timestep_spacing in ["trailing", "leading"]:
                     self.check_over_configs(timestep_spacing=__A				)


       def 	_snake_case				(    self				)						->    Tuple:
              for rescale_betas_zero_snr in [True, False]:
                     self.check_over_configs(rescale_betas_zero_snr=__A				)


       def 	_snake_case				(    self				)						->    List[Any]:
              self.check_over_configs(thresholding=__A				)
              for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "v_prediction"]:
                            self.check_over_configs(
                                thresholding=__A						,       prediction_type=__A						,       sample_max_value=__A						,       )


       def 	_snake_case				(    self				)						->    Dict:
              for t in [1, 10, 49]:
                     self.check_over_forward(time_step=__A				)


       def 	_snake_case				(    self				)						->    Dict:
              for t, num_inference_steps in zip([1, 10, 50]						,       [10, 50, 500]				):
                     self.check_over_forward(time_step=__A						,       num_inference_steps=__A				)


       def 	_snake_case				(    self				)						->    int:
              for t, eta in zip([1, 10, 49]						,       [0.0, 0.5, 1.0]				):
                     self.check_over_forward(time_step=__A						,       eta=__A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

              assert torch.sum(torch.abs(scheduler._get_variance(0						,       0				) - 0.0				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(420						,       400				) - 0.14_771				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(980						,       960				) - 0.32_460				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(0						,       0				) - 0.0				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(487						,       486				) - 0.00_979				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(999						,       998				) - 0.02				)				) < 1e-5


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				str		=scheduler_class(**__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[str]		=10, 0.0
              scheduler.set_timesteps(__A				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				str		=self.dummy_sample_deter
              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_sample_deter + 0.1
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.dummy_sample_deter - 0.1

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=samplea.shape[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.stack([samplea, samplea, samplea]						,       dim=0				)
              SCREAMING_SNAKE_CASE_							:				Any		=torch.arange(__A				)[0:3, None].repeat(1						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=model(samples.flatten(0						,       1				)						,       timesteps.flatten(0						,       1				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=scheduler.batch_step_no_noise(__A						,       timesteps.flatten(0						,       1				)						,       samples.flatten(0						,       1				)						,       __A				)

              SCREAMING_SNAKE_CASE_							:				str		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 1_147.7_904				) < 1e-2
              assert abs(result_mean.item() - 0.4_982				) < 1e-3


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

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 172.0_067				) < 1e-2
              assert abs(result_mean.item() - 0.223_967				) < 1e-3


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=self.full_loop(prediction_type='''v_prediction'''				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				str		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 52.5_302				) < 1e-2
              assert abs(result_mean.item() - 0.0_684				) < 1e-3


       def 	_snake_case				(    self				)						->    Dict:
              # We specify different beta, so that the first alpha is 0.99
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.full_loop(set_alpha_to_one=__A						,       beta_start=0.01				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 149.8_295				) < 1e-2
              assert abs(result_mean.item() - 0.1_951				) < 1e-3





       def 	_snake_case				(    self				)						->    Optional[Any]:
              # We specify different beta, so that the first alpha is 0.99
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.full_loop(set_alpha_to_one=__A						,       beta_start=0.01				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 149.0_784				) < 1e-2
              assert abs(result_mean.item() - 0.1_941				) < 1e-3
