


"""simple docstring"""






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCAmelCase							:    Dict					   =	'''facebook/wmt19-en-de'''
_lowerCAmelCase							:    int					   =	FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCAmelCase							:    List[Any]					   =	FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_lowerCAmelCase							:    Any					   =	FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCAmelCase							:    List[str]					   =	tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_lowerCAmelCase							:    Union[str, Any]					   =	tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
_lowerCAmelCase							:    int					   =	'''tiny-wmt19-en-de'''
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-de





'''simple docstring'''



import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


UpperCamelCase_    	=				get_tests_dir("fixtures")






class 							_a				(			unittest.TestCase ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						mock.Mock()
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						500
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{}
             SCREAMING_SNAKE_CASE   :      Any    					=						HTTPError
             SCREAMING_SNAKE_CASE   :      Any    					=						{}

             # Download this model to make sure it's in the cache.
             SCREAMING_SNAKE_CASE   :      str    					=						ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit'  )
             # Under the mock environment we get a 500 error when trying to reach the model.
             with mock.patch('requests.Session.request',							return_value=A  ) as mock_head:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit'  )
                   # This check we did call the fake head request
                   mock_head.assert_called()







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						ViTImageProcessor.from_pretrained(
                 'https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             with self.assertRaises(A  ):
                   # config is in subfolder, the following should not work without specifying the subfolder
                   SCREAMING_SNAKE_CASE   :      str    					=						AutoImageProcessor.from_pretrained('hf-internal-testing/stable-diffusion-all-variants'  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						AutoImageProcessor.from_pretrained(
                 'hf-internal-testing/stable-diffusion-all-variants',							subfolder='feature_extractor'  )

             self.assertIsNotNone(A  )






@is_staging_test
class 							_a				(			unittest.TestCase ):





       '''simple docstring'''






       @classmethod
       def        UpperCamelCase_							(      cls  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						TOKEN
             HfFolder.save_token(A  )







       @classmethod
       def        UpperCamelCase_							(      cls  ):







             '''simple docstring'''







             try:
                   delete_repo(token=cls._token,							repo_id='test-image-processor'  )
             except HTTPError:
                   pass

             try:
                   delete_repo(token=cls._token,							repo_id='valid_org/test-image-processor-org'  )
             except HTTPError:
                   pass

             try:
                   delete_repo(token=cls._token,							repo_id='test-dynamic-image-processor'  )
             except HTTPError:
                   pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						ViTImageProcessor.from_pretrained(A  )
             image_processor.push_to_hub('test-image-processor',							use_auth_token=self._token  )

             SCREAMING_SNAKE_CASE   :      int    					=						ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor"  )
             for k, v in image_processor.__dict__.items():
                   self.assertEqual(A,							getattr(A,							A  )  )

             # Reset repo
             delete_repo(token=self._token,							repo_id='test-image-processor'  )

             # Push to hub via save_pretrained
             with tempfile.TemporaryDirectory() as tmp_dir:
                   image_processor.save_pretrained(
                       A,							repo_id='test-image-processor',							push_to_hub=A,							use_auth_token=self._token  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor"  )
             for k, v in image_processor.__dict__.items():
                   self.assertEqual(A,							getattr(A,							A  )  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						ViTImageProcessor.from_pretrained(A  )
             image_processor.push_to_hub('valid_org/test-image-processor',							use_auth_token=self._token  )

             SCREAMING_SNAKE_CASE   :      str    					=						ViTImageProcessor.from_pretrained('valid_org/test-image-processor'  )
             for k, v in image_processor.__dict__.items():
                   self.assertEqual(A,							getattr(A,							A  )  )

             # Reset repo
             delete_repo(token=self._token,							repo_id='valid_org/test-image-processor'  )

             # Push to hub via save_pretrained
             with tempfile.TemporaryDirectory() as tmp_dir:
                   image_processor.save_pretrained(
                       A,							repo_id='valid_org/test-image-processor-org',							push_to_hub=A,							use_auth_token=self._token  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						ViTImageProcessor.from_pretrained('valid_org/test-image-processor-org'  )
             for k, v in image_processor.__dict__.items():
                   self.assertEqual(A,							getattr(A,							A  )  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             CustomImageProcessor.register_for_auto_class()
             SCREAMING_SNAKE_CASE   :      Tuple    					=						CustomImageProcessor.from_pretrained(A  )

             image_processor.push_to_hub('test-dynamic-image-processor',							use_auth_token=self._token  )

             # This has added the proper auto_map field to the config
             self.assertDictEqual(
                 image_processor.auto_map,							{'AutoImageProcessor': 'custom_image_processing.CustomImageProcessor'},							)

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						AutoImageProcessor.from_pretrained(
                 F"{USER}/test-dynamic-image-processor",							trust_remote_code=A  )
             # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
             self.assertEqual(new_image_processor.__class__.__name__,							'CustomImageProcessor'  )


