




import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch






class 	lowercase_      (			unittest.TestCase     ):






      '''simple docstring'''




      @patch('''sys.argv'''				,      ['''fakeprogrampath''', '''env''']						)
      def    __lowerCAmelCase      (   self   :		List[Any]						)					->int:




       """simple docstring"""


       import transformers.commands.transformers_cli

       with CaptureStd() as cs:
        transformers.commands.transformers_cli.main()
       self.assertIn('''Python version'''				,      cs.out						)
       self.assertIn('''Platform'''				,      cs.out						)
       self.assertIn('''Using distributed or parallel set-up in script?'''				,      cs.out						)







      @is_pt_tf_cross_test
      @patch(
          '''sys.argv'''				,      ['''fakeprogrampath''', '''pt-to-tf''', '''--model-name''', '''hf-internal-testing/tiny-random-gptj''', '''--no-pr''']						)
      def    __lowerCAmelCase      (   self   :		int						)					->List[Any]:




       """simple docstring"""


       import transformers.commands.transformers_cli

       shutil.rmtree('''/tmp/hf-internal-testing/tiny-random-gptj'''				,      ignore_errors=lowerCamelCase__						)  # cleans potential past runs
       transformers.commands.transformers_cli.main()

       # The original repo has no TF weights -- if they exist, they were created by the CLI
       self.assertTrue(os.path.exists('''/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'''						)						)







      @require_torch
      @patch('''sys.argv'''				,      ['''fakeprogrampath''', '''download''', '''hf-internal-testing/tiny-random-gptj''', '''--cache-dir''', '''/tmp''']						)
      def    __lowerCAmelCase      (   self   :		Optional[int]						)					->Dict:




       """simple docstring"""


       import transformers.commands.transformers_cli

       # # remove any previously downloaded model to start clean
       shutil.rmtree('''/tmp/models--hf-internal-testing--tiny-random-gptj'''				,      ignore_errors=lowerCamelCase__						)

       # run the command
       transformers.commands.transformers_cli.main()

       # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
       self.assertTrue(os.path.exists('''/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'''						)						)
       self.assertTrue(os.path.exists('''/tmp/models--hf-internal-testing--tiny-random-gptj/refs'''						)						)
       self.assertTrue(os.path.exists('''/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'''						)						)







      @require_torch
      @patch(
          '''sys.argv'''				,      [
              '''fakeprogrampath''',
              '''download''',
              '''hf-internal-testing/test_dynamic_model_with_tokenizer''',
              '''--trust-remote-code''',
              '''--cache-dir''',
              '''/tmp''',
          ]				,      )
      def    __lowerCAmelCase      (   self   :		str						)					->str:




       """simple docstring"""


       import transformers.commands.transformers_cli

       # # remove any previously downloaded model to start clean
       shutil.rmtree('''/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'''				,      ignore_errors=lowerCamelCase__						)

       # run the command
       transformers.commands.transformers_cli.main()

       # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
       self.assertTrue(os.path.exists('''/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'''						)						)
       self.assertTrue(os.path.exists('''/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'''						)						)
       self.assertTrue(
           os.path.exists('''/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'''						)						)










from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
     from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline


