import json
import os
import shutil
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 AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


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

from test_module.custom_configuration import CustomConfig  # noqa E402


_SCREAMING_SNAKE_CASE 		=      {
    """return_dict""": False,
    """output_hidden_states""": True,
    """output_attentions""": True,
    """torchscript""": True,
    """torch_dtype""": """float16""",
    """use_bfloat16""": True,
    """tf_legacy_loss""": True,
    """pruned_heads""": {"""a""": 1},
    """tie_word_embeddings""": False,
    """is_decoder""": True,
    """cross_attention_hidden_size""": 1_2_8,
    """add_cross_attention""": True,
    """tie_encoder_decoder""": True,
    """max_length""": 5_0,
    """min_length""": 3,
    """do_sample""": True,
    """early_stopping""": True,
    """num_beams""": 3,
    """num_beam_groups""": 3,
    """diversity_penalty""": 0.5,
    """temperature""": 2.0,
    """top_k""": 1_0,
    """top_p""": 0.7,
    """typical_p""": 0.2,
    """repetition_penalty""": 0.8,
    """length_penalty""": 0.8,
    """no_repeat_ngram_size""": 5,
    """encoder_no_repeat_ngram_size""": 5,
    """bad_words_ids""": [1, 2, 3],
    """num_return_sequences""": 3,
    """chunk_size_feed_forward""": 5,
    """output_scores""": True,
    """return_dict_in_generate""": True,
    """forced_bos_token_id""": 2,
    """forced_eos_token_id""": 3,
    """remove_invalid_values""": True,
    """architectures""": ["""BertModel"""],
    """finetuning_task""": """translation""",
    """id2label""": {0: """label"""},
    """label2id""": {"""label""": """0"""},
    """tokenizer_class""": """BertTokenizerFast""",
    """prefix""": """prefix""",
    """bos_token_id""": 6,
    """pad_token_id""": 7,
    """eos_token_id""": 8,
    """sep_token_id""": 9,
    """decoder_start_token_id""": 1_0,
    """exponential_decay_length_penalty""": (5, 1.01),
    """suppress_tokens""": [0, 1],
    """begin_suppress_tokens""": 2,
    """task_specific_params""": {"""translation""": """some_params"""},
    """problem_type""": """regression""",
}
@is_staging_test
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   @classmethod
   def 						lowerCamelCase_	(     cls						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      TOKEN
    HfFolder.save_token(UpperCamelCase_				)







   @classmethod
   def 						lowerCamelCase_	(     cls						:						int				):





    """simple docstring"""


    try:
     delete_repo(token=cls._token   ,   repo_id="""test-config"""				)
    except HTTPError:
     pass

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

    try:
     delete_repo(token=cls._token   ,   repo_id="""test-dynamic-config"""				)
    except HTTPError:
     pass







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      BertConfig(
        vocab_size=99   ,   hidden_size=32   ,   num_hidden_layers=5   ,   num_attention_heads=4   ,   intermediate_size=37				)
    config.push_to_hub("""test-config"""   ,   use_auth_token=self._token				)

    UpperCamelCase			  =      BertConfig.from_pretrained(f"""{USER}/test-config"""				)
    for k, v in config.to_dict().items():
     if k != "transformers_version":
      self.assertEqual(UpperCamelCase_   ,   getattr(UpperCamelCase_   ,   UpperCamelCase_				)				)

        # Reset repo
    delete_repo(token=self._token   ,   repo_id="""test-config"""				)

    # Push to hub via save_pretrained
    with tempfile.TemporaryDirectory() as tmp_dir:
     config.save_pretrained(UpperCamelCase_   ,   repo_id="""test-config"""   ,   push_to_hub=UpperCamelCase_   ,   use_auth_token=self._token				)

    UpperCamelCase			  =      BertConfig.from_pretrained(f"""{USER}/test-config"""				)
    for k, v in config.to_dict().items():
     if k != "transformers_version":
      self.assertEqual(UpperCamelCase_   ,   getattr(UpperCamelCase_   ,   UpperCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      BertConfig(
        vocab_size=99   ,   hidden_size=32   ,   num_hidden_layers=5   ,   num_attention_heads=4   ,   intermediate_size=37				)
    config.push_to_hub("""valid_org/test-config-org"""   ,   use_auth_token=self._token				)

    UpperCamelCase			  =      BertConfig.from_pretrained("""valid_org/test-config-org"""				)
    for k, v in config.to_dict().items():
     if k != "transformers_version":
      self.assertEqual(UpperCamelCase_   ,   getattr(UpperCamelCase_   ,   UpperCamelCase_				)				)

        # Reset repo
    delete_repo(token=self._token   ,   repo_id="""valid_org/test-config-org"""				)

    # Push to hub via save_pretrained
    with tempfile.TemporaryDirectory() as tmp_dir:
     config.save_pretrained(
         UpperCamelCase_   ,   repo_id="""valid_org/test-config-org"""   ,   push_to_hub=UpperCamelCase_   ,   use_auth_token=self._token				)

    UpperCamelCase			  =      BertConfig.from_pretrained("""valid_org/test-config-org"""				)
    for k, v in config.to_dict().items():
     if k != "transformers_version":
      self.assertEqual(UpperCamelCase_   ,   getattr(UpperCamelCase_   ,   UpperCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    CustomConfig.register_for_auto_class()
    UpperCamelCase			  =      CustomConfig(attribute=42				)

    config.push_to_hub("""test-dynamic-config"""   ,   use_auth_token=self._token				)

    # This has added the proper auto_map field to the config
    self.assertDictEqual(config.auto_map   ,   {"""AutoConfig""": """custom_configuration.CustomConfig"""}				)

    UpperCamelCase			  =      AutoConfig.from_pretrained(f"""{USER}/test-dynamic-config"""   ,   trust_remote_code=UpperCamelCase_				)
    # Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
    self.assertEqual(new_config.__class__.__name__   ,   """CustomConfig"""				)
    self.assertEqual(new_config.attribute   ,   42				)





class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      GPTaConfig()

    # attempt to modify each of int/float/bool/str config records and verify they were updated
    UpperCamelCase			  =      c.n_embd + 1  # int
    UpperCamelCase			  =      c.resid_pdrop + 1.0  # float
    UpperCamelCase			  =      not c.scale_attn_weights  # bool
    UpperCamelCase			  =      c.summary_type + 'foo'  # str
    c.update_from_string(
        f"""n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}"""				)
    self.assertEqual(UpperCamelCase_   ,   c.n_embd   ,   """mismatch for key: n_embd"""				)
    self.assertEqual(UpperCamelCase_   ,   c.resid_pdrop   ,   """mismatch for key: resid_pdrop"""				)
    self.assertEqual(UpperCamelCase_   ,   c.scale_attn_weights   ,   """mismatch for key: scale_attn_weights"""				)
    self.assertEqual(UpperCamelCase_   ,   c.summary_type   ,   """mismatch for key: summary_type"""				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      PretrainedConfig()
    UpperCamelCase			  =      [key for key in base_config.__dict__ if key not in config_common_kwargs]
    # If this part of the test fails, you have arguments to addin config_common_kwargs above.
    self.assertListEqual(
        UpperCamelCase_   ,   ["""is_encoder_decoder""", """_name_or_path""", """_commit_hash""", """transformers_version"""]				)
    UpperCamelCase			  =      [key for key, value in config_common_kwargs.items() if value == getattr(UpperCamelCase_   ,   UpperCamelCase_				)]
    if len(UpperCamelCase_				) > 0:
     raise ValueError(
         """The following keys are set with the default values in"""
         """ `test_configuration_common.config_common_kwargs` pick another value for them:"""
         f""" {", ".join(UpperCamelCase_				)}."""				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    with self.assertRaises(UpperCamelCase_				):
     # config is in subfolder, the following should not work without specifying the subfolder
     UpperCamelCase			  =      BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert-subfolder"""				)

    UpperCamelCase			  =      BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert-subfolder"""   ,   subfolder="""bert"""				)

    self.assertIsNotNone(UpperCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      mock.Mock()
    UpperCamelCase			  =      500
    UpperCamelCase			  =      {}
    UpperCamelCase			  =      HTTPError
    UpperCamelCase			  =      {}

    # Download this model to make sure it's in the cache.
    UpperCamelCase			  =      BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert"""				)

    # Under the mock environment we get a 500 error when trying to reach the model.
    with mock.patch("""requests.Session.request"""   ,   return_value=UpperCamelCase_				) as mock_head:
     UpperCamelCase			  =      BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert"""				)
     # This check we did call the fake head request
     mock_head.assert_called()







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      BertConfig.from_pretrained(
        """https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"""				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      AutoConfig.from_pretrained("""bert-base-cased"""				)
    UpperCamelCase			  =      ['config.4.0.0.json']

    with tempfile.TemporaryDirectory() as tmp_dir:
     configuration.save_pretrained(UpperCamelCase_				)
     UpperCamelCase			  =      2
     json.dump(configuration.to_dict()   ,   open(os.path.join(UpperCamelCase_   ,   """config.4.0.0.json"""				)   ,   """w"""				)				)

     # This should pick the new configuration file as the version of Transformers is > 4.0.0
     UpperCamelCase			  =      AutoConfig.from_pretrained(UpperCamelCase_				)
     self.assertEqual(new_configuration.hidden_size   ,   2				)

     # Will need to be adjusted if we reach v42 and this test is still here.
     # Should pick the old configuration file as the version of Transformers is < 4.42.0
     UpperCamelCase			  =      ['config.42.0.0.json']
     UpperCamelCase			  =      768
     configuration.save_pretrained(UpperCamelCase_				)
     shutil.move(os.path.join(UpperCamelCase_   ,   """config.4.0.0.json"""				)   ,   os.path.join(UpperCamelCase_   ,   """config.42.0.0.json"""				)				)
     UpperCamelCase			  =      AutoConfig.from_pretrained(UpperCamelCase_				)
     self.assertEqual(new_configuration.hidden_size   ,   768				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      'hf-internal-testing/test-two-configs'

    import transformers as new_transformers

    UpperCamelCase			  =      'v4.0.0'
    UpperCamelCase			  =      new_transformers.models.auto.AutoConfig.from_pretrained(
        UpperCamelCase_   ,   return_unused_kwargs=UpperCamelCase_				)
    self.assertEqual(new_configuration.hidden_size   ,   2				)
    # This checks `_configuration_file` ia not kept in the kwargs by mistake.
    self.assertDictEqual(UpperCamelCase_   ,   {}				)

    # Testing an older version by monkey-patching the version in the module it's used.
    import transformers as old_transformers

    UpperCamelCase			  =      'v3.0.0'
    UpperCamelCase			  =      old_transformers.models.auto.AutoConfig.from_pretrained(UpperCamelCase_				)
    self.assertEqual(old_configuration.hidden_size   ,   768				)


"""simple docstring"""



import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class 				a			(					lowercase						,		lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   int           =     IFImgaImgSuperResolutionPipeline
      UpperCamelCase							:   Union[str, Any]           =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
      UpperCamelCase							:   Tuple           =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}      )
      UpperCamelCase							:   Union[str, Any]           =     PipelineTesterMixin.required_optional_params - {"""latents"""}



      def  __snake_case							(   self      ):
          return self._get_superresolution_dummy_components()



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=0      ):
          if str(UpperCamelCase_      ).startswith('mps'      ):
              UpperCAmelCase__	:      Optional[int]     							=					torch.manual_seed(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      int     							=					torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					floats_tensor((1, 3, 32, 32)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					floats_tensor((1, 3, 16, 16)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )

          UpperCAmelCase__	:      int     							=					{
              'prompt': 'A painting of a squirrel eating a burger',
              'image': image,
              'original_image': original_image,
              'generator': generator,
              'num_inference_steps': 2,
              'output_type': 'numpy',
          }

          return inputs



      @unittest.skipIf(
          torch_device != 'cuda' or not is_xformers_available()     ,	reason='XFormers attention is only available with CUDA and `xformers` installed'     ,	)
      def  __snake_case							(   self      ):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )



      def  __snake_case							(   self      ):
          self._test_save_load_optional_components()



      @unittest.skipIf(torch_device != 'cuda'     ,	reason='float16 requires CUDA'      )
      def  __snake_case							(   self      ):
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1E-1      )



      def  __snake_case							(   self      ):
          self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )



      def  __snake_case							(   self      ):
          self._test_save_load_local()



      def  __snake_case							(   self      ):
          self._test_inference_batch_single_identical(
              expected_max_diff=1E-2     ,	)

