"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFLEDForConditionalGeneration, TFLEDModel






@require_tf
class       snake_case__	:
 _snake_case    : Tuple				   =		LEDConfig
 _snake_case    : List[Any]				   =		{}
 _snake_case    : Optional[int]				   =		"""gelu"""


 def __init__(      self  ,						lowerCamelCase  ,						lowerCamelCase=13  ,						lowerCamelCase=7  ,						lowerCamelCase=True  ,						lowerCamelCase=False  ,						lowerCamelCase=99  ,						lowerCamelCase=32  ,						lowerCamelCase=2  ,						lowerCamelCase=4  ,						lowerCamelCase=37  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=20  ,						lowerCamelCase=2  ,						lowerCamelCase=1  ,						lowerCamelCase=0  ,						lowerCamelCase=4  ,						):
        __a	      =  parent
        __a	      =  batch_size
        __a	      =  seq_length
        __a	      =  is_training
        __a	      =  use_labels
        __a	      =  vocab_size
        __a	      =  hidden_size
        __a	      =  num_hidden_layers
        __a	      =  num_attention_heads
        __a	      =  intermediate_size

        __a	      =  hidden_dropout_prob
        __a	      =  attention_probs_dropout_prob
        __a	      =  max_position_embeddings
        __a	      =  eos_token_id
        __a	      =  pad_token_id
        __a	      =  bos_token_id
        __a	      =  attention_window

        # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
        # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
        # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
        # because its local attention only attends to `self.attention_window` and one before and one after
        __a	      =  self.attention_window + 2

        # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
        # the `test_attention_outputs` and `test_hidden_states_output` tests
        __a	      =  (
            self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
        )


 def 		a__					(      self		):
        __a	      =  ids_tensor([self.batch_size, self.seq_length - 1]  ,						self.vocab_size		)
        __a	      =  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)  ,						1		)
        __a	      =  tf.concat([input_ids, eos_tensor]  ,						axis=1		)

        __a	      =  ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size		)

        __a	      =  self.config_cls(
            vocab_size=self.vocab_size  ,						d_model=self.hidden_size  ,						encoder_layers=self.num_hidden_layers  ,						decoder_layers=self.num_hidden_layers  ,						encoder_attention_heads=self.num_attention_heads  ,						decoder_attention_heads=self.num_attention_heads  ,						encoder_ffn_dim=self.intermediate_size  ,						decoder_ffn_dim=self.intermediate_size  ,						dropout=self.hidden_dropout_prob  ,						attention_dropout=self.attention_probs_dropout_prob  ,						max_position_embeddings=self.max_position_embeddings  ,						eos_token_ids=[2]  ,						bos_token_id=self.bos_token_id  ,						pad_token_id=self.pad_token_id  ,						decoder_start_token_id=self.pad_token_id  ,						attention_window=self.attention_window  ,						**self.config_updates  ,						)
        __a	      =  prepare_led_inputs_dict(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__		)
        __a	      =  tf.concat(
            [tf.zeros_like(SCREAMING_SNAKE_CASE__		)[:, :-1], tf.ones_like(SCREAMING_SNAKE_CASE__		)[:, -1:]]  ,						axis=-1  ,						)
        __a	      =  global_attention_mask
        return config, inputs_dict






 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a	      =  TFLEDModel(config=SCREAMING_SNAKE_CASE__		).get_decoder()
        __a	      =  inputs_dict["input_ids"]

        __a	      =  input_ids[:1, :]
        __a	      =  inputs_dict["attention_mask"][:1, :]
        __a	      =  1

        # first forward pass
        __a	      =  model(SCREAMING_SNAKE_CASE__  ,						attention_mask=SCREAMING_SNAKE_CASE__  ,						use_cache=SCREAMING_SNAKE_CASE__		)

        __a      ,					__a	      =  outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        __a	      =  ids_tensor((self.batch_size, 3)  ,						config.vocab_size		)
        __a	      =  tf.cast(ids_tensor((self.batch_size, 3)  ,						2		)  ,						tf.inta		)

        # append to next input_ids and
        __a	      =  tf.concat([input_ids, next_tokens]  ,						axis=-1		)
        __a	      =  tf.concat([attention_mask, next_attn_mask]  ,						axis=-1		)

        __a	      =  model(SCREAMING_SNAKE_CASE__  ,						attention_mask=SCREAMING_SNAKE_CASE__		)[0]
        __a	      =  model(SCREAMING_SNAKE_CASE__  ,						attention_mask=SCREAMING_SNAKE_CASE__  ,						past_key_values=SCREAMING_SNAKE_CASE__		)[0]

        self.parent.assertEqual(next_tokens.shape[1]  ,						output_from_past.shape[1]		)

        # select random slice
        __a	      =  int(ids_tensor((1,)  ,						output_from_past.shape[-1]		)		)
        __a	      =  output_from_no_past[:, -3:, random_slice_idx]
        __a	      =  output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						rtol=1E-3		)



def      _lowerCamelCase(     a					,  a					,  a					,  a=None					,  a=None					,  a=None					,  a=None					,  ):

       if attention_mask is None:
              __a	      =  tf.cast(tf.math.not_equal(snake_case_					,  config.pad_token_id							)					,  tf.inta							)
       if decoder_attention_mask is None:
              __a	      =  tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape					,  dtype=tf.inta							),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]					,  config.pad_token_id							)					,  tf.inta							),
                  ]					,  axis=-1					,  )
       if head_mask is None:
              __a	      =  tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
       if decoder_head_mask is None:
              __a	      =  tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
       return {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
           "decoder_input_ids": decoder_input_ids,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
       }






@require_tf
class       snake_case__	(    UpperCAmelCase__,		UpperCAmelCase__,		unittest.TestCase    ):
 _snake_case    : Any				   =		(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
 _snake_case    : Optional[int]				   =		(TFLEDForConditionalGeneration,) if is_tf_available() else ()
 _snake_case    : List[str]				   =		(
     {
         """conversational""": TFLEDForConditionalGeneration,
         """feature-extraction""": TFLEDModel,
         """summarization""": TFLEDForConditionalGeneration,
         """text2text-generation""": TFLEDForConditionalGeneration,
         """translation""": TFLEDForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 _snake_case    : Optional[int]				   =		True
 _snake_case    : str				   =		False
 _snake_case    : List[Any]				   =		False
 _snake_case    : str				   =		False


 def 		a__					(      self		):
        __a	      =  TFLEDModelTester(self		)
        __a	      =  ConfigTester(self  ,						config_class=SCREAMING_SNAKE_CASE__		)


 def 		a__					(      self		):
        self.config_tester.run_common_tests()


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE__		)


 def 		a__					(      self		):
        __a      ,					__a	      =  self.model_tester.prepare_config_and_inputs_for_common()
        __a	      =  tf.zeros_like(inputs_dict["attention_mask"]		)
        __a	      =  2
        __a	      =  tf.where(
            tf.range(self.model_tester.seq_length		)[None, :] < num_global_attn_indices  ,						1  ,						inputs_dict["global_attention_mask"]  ,						)

        __a	      =  True
        __a	      =  self.model_tester.seq_length
        __a	      =  self.model_tester.encoder_seq_length

        def check_decoder_attentions_output(lowerCamelCase		):
               __a	      =  outputs.decoder_attentions
               self.assertEqual(len(SCREAMING_SNAKE_CASE__		)  ,						self.model_tester.num_hidden_layers		)
               self.assertListEqual(
                   list(decoder_attentions[0].shape[-3:]		)  ,						[self.model_tester.num_attention_heads, seq_length, seq_length]  ,						)

        def check_encoder_attentions_output(lowerCamelCase		):
               __a	      =  [t.numpy() for t in outputs.encoder_attentions]
               __a	      =  [t.numpy() for t in outputs.encoder_global_attentions]
               self.assertEqual(len(SCREAMING_SNAKE_CASE__		)  ,						self.model_tester.num_hidden_layers		)
               self.assertEqual(len(SCREAMING_SNAKE_CASE__		)  ,						self.model_tester.num_hidden_layers		)
               self.assertListEqual(
                   list(attentions[0].shape[-3:]		)  ,						[self.model_tester.num_attention_heads, seq_length, seq_length]  ,						)
               self.assertListEqual(
                   list(global_attentions[0].shape[-3:]		)  ,						[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]  ,						)

        for model_class in self.all_model_classes:
               __a	      =  True
               __a	      =  False
               __a	      =  False
               __a	      =  model_class(SCREAMING_SNAKE_CASE__		)
               __a	      =  model(self._prepare_for_class(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__		)		)
               __a	      =  len(SCREAMING_SNAKE_CASE__		)
               self.assertEqual(config.output_hidden_states  ,						SCREAMING_SNAKE_CASE__		)
               check_encoder_attentions_output(SCREAMING_SNAKE_CASE__		)

               if self.is_encoder_decoder:
                      __a	      =  model_class(SCREAMING_SNAKE_CASE__		)
                      __a	      =  model(self._prepare_for_class(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__		)		)
                      self.assertEqual(config.output_hidden_states  ,						SCREAMING_SNAKE_CASE__		)
                      check_decoder_attentions_output(SCREAMING_SNAKE_CASE__		)

               # Check that output attentions can also be changed via the config
               del inputs_dict["output_attentions"]
               __a	      =  True
               __a	      =  model_class(SCREAMING_SNAKE_CASE__		)
               __a	      =  model(self._prepare_for_class(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__		)		)
               self.assertEqual(config.output_hidden_states  ,						SCREAMING_SNAKE_CASE__		)
               check_encoder_attentions_output(SCREAMING_SNAKE_CASE__		)

               # Check attention is always last and order is fine
               __a	      =  True
               __a	      =  True
               __a	      =  model_class(SCREAMING_SNAKE_CASE__		)
               __a	      =  model(self._prepare_for_class(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__		)		)

               self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)  ,						len(SCREAMING_SNAKE_CASE__		)		)
               self.assertEqual(model.config.output_hidden_states  ,						SCREAMING_SNAKE_CASE__		)
               check_encoder_attentions_output(SCREAMING_SNAKE_CASE__		)


 @unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."		)
 def 		a__					(      self		):
        pass






 def 		a__					(      self		):
        # TODO: Head-masking not yet implement
        pass



def      _lowerCamelCase(     a							):
       return tf.constant(snake_case_					,  dtype=tf.intaa							)


SCREAMING_SNAKE_CASE__:Union[str, Any]		       =       1e-4






@slow
@require_tf
class       snake_case__	(    unittest.TestCase    ):


 def 		a__					(      self		):
        __a	      =  TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"		).led

        # change to intended input here
        __a	      =  _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]		)
        __a	      =  _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]		)
        __a	      =  prepare_led_inputs_dict(model.config  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__		)
        __a	      =  model(**SCREAMING_SNAKE_CASE__		)[0]
        __a	      =  (1, 1024, 768)
        self.assertEqual(output.shape  ,						SCREAMING_SNAKE_CASE__		)
        # change to expected output here
        __a	      =  tf.convert_to_tensor(
            [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]  ,						)
        tf.debugging.assert_near(output[:, :3, :3]  ,						SCREAMING_SNAKE_CASE__  ,						atol=1E-3		)






 def 		a__					(      self		):
        __a	      =  TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"		)

        # change to intended input here
        __a	      =  _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]		)
        __a	      =  _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]		)
        __a	      =  prepare_led_inputs_dict(model.config  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__		)
        __a	      =  model(**SCREAMING_SNAKE_CASE__		)[0]
        __a	      =  (1, 1024, model.config.vocab_size)
        self.assertEqual(output.shape  ,						SCREAMING_SNAKE_CASE__		)
        # change to expected output here
        __a	      =  tf.convert_to_tensor(
            [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]  ,						)
        tf.debugging.assert_near(output[:, :3, :3]  ,						SCREAMING_SNAKE_CASE__  ,						atol=1E-3  ,						rtol=1E-3		)

'''simple docstring'''

import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


_A			:      List[Any]					      =				(
    '''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)


_A			:      List[Any]					      =				logging.get_logger(__name__)  # pylint: disable=invalid-name







def      UpperCamelCase_						(					)					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						"""https://pypi.org/pypi/diffusers/json"""
 __lowerCAmelCase											=						json.loads(request.urlopen(snake_case_      ).read()      )["""releases"""].keys()
 return sorted(snake_case_						,		key=lambda snake_case_      : version.Version(snake_case_      )      )







def      UpperCamelCase_						(					)					->     int:






 '''simple docstring'''

 if HF_MODULES_CACHE in sys.path:
  return

 sys.path.append(snake_case_      )
 os.makedirs(snake_case_						,		exist_ok=snake_case_      )
 __lowerCAmelCase											=						Path(snake_case_      ) / """__init__.py"""
 if not init_path.exists():
  init_path.touch()







def      UpperCamelCase_						(					snake_case_       :       Union[str, os.PathLike]      )					->     Dict:






 '''simple docstring'''

 init_hf_modules()
 __lowerCAmelCase											=						Path(snake_case_      ) / name
 # If the parent module does not exist yet, recursively create it.
 if not dynamic_module_path.parent.exists():
  create_dynamic_module(dynamic_module_path.parent      )
 os.makedirs(snake_case_						,		exist_ok=snake_case_      )
 __lowerCAmelCase											=						dynamic_module_path / """__init__.py"""
 if not init_path.exists():
  init_path.touch()







def      UpperCamelCase_						(					snake_case_       :       int      )					->     str:






 '''simple docstring'''

 with open(snake_case_						,		"""r"""						,		encoding="""utf-8"""      ) as f:
  __lowerCAmelCase											=						f.read()

 # Imports of the form `import .xxx`
 __lowerCAmelCase											=						re.findall("""^\s*import\s+\.(\S+)\s*$"""						,		snake_case_						,		flags=re.MULTILINE      )
 # Imports of the form `from .xxx import yyy`
 relative_imports += re.findall("""^\s*from\s+\.(\S+)\s+import"""						,		snake_case_						,		flags=re.MULTILINE      )
 # Unique-ify
 return list(set(snake_case_      )      )







def      UpperCamelCase_						(					snake_case_       :       int      )					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						False
 __lowerCAmelCase											=						[module_file]
 __lowerCAmelCase											=						[]

 # Let's recurse through all relative imports
 while not no_change:
  __lowerCAmelCase											=						[]
  for f in files_to_check:
   new_imports.extend(get_relative_imports(snake_case_      )      )

  __lowerCAmelCase											=						Path(snake_case_      ).parent
  __lowerCAmelCase											=						[str(module_path / m      ) for m in new_imports]
  __lowerCAmelCase											=						[f for f in new_import_files if f not in all_relative_imports]
  __lowerCAmelCase											=						[f"""{f}.py""" for f in new_import_files]

  __lowerCAmelCase											=						len(snake_case_      ) == 0
  all_relative_imports.extend(snake_case_      )

 return all_relative_imports







def      UpperCamelCase_						(					snake_case_       :       List[Any]      )					->     Optional[Any]:






 '''simple docstring'''

 with open(snake_case_						,		"""r"""						,		encoding="""utf-8"""      ) as f:
  __lowerCAmelCase											=						f.read()

 # Imports of the form `import xxx`
 __lowerCAmelCase											=						re.findall("""^\s*import\s+(\S+)\s*$"""						,		snake_case_						,		flags=re.MULTILINE      )
 # Imports of the form `from xxx import yyy`
 imports += re.findall("""^\s*from\s+(\S+)\s+import"""						,		snake_case_						,		flags=re.MULTILINE      )
 # Only keep the top-level module
 __lowerCAmelCase											=						[imp.split("""."""      )[0] for imp in imports if not imp.startswith("""."""      )]

 # Unique-ify and test we got them all
 __lowerCAmelCase											=						list(set(snake_case_      )      )
 __lowerCAmelCase											=						[]
 for imp in imports:
  try:
   importlib.import_module(snake_case_      )
  except ImportError:
   missing_packages.append(snake_case_      )

 if len(snake_case_      ) > 0:
  raise ImportError(
      """This modeling file requires the following packages that were not found in your environment: """
      f"""{", ".join(snake_case_      )}. Run `pip install {" ".join(snake_case_      )}`"""      )

 return get_relative_imports(snake_case_      )







def      UpperCamelCase_						(					snake_case_       :       Dict						,		snake_case_       :       Optional[int]      )					->     List[str]:






 '''simple docstring'''

 __lowerCAmelCase											=						module_path.replace(os.path.sep						,		"""."""      )
 __lowerCAmelCase											=						importlib.import_module(snake_case_      )

 if class_name is None:
  return find_pipeline_class(snake_case_      )
 return getattr(snake_case_						,		snake_case_      )







def      UpperCamelCase_						(					snake_case_       :       str      )					->     Optional[Any]:






 '''simple docstring'''

 from ..pipelines import DiffusionPipeline

 __lowerCAmelCase											=						dict(inspect.getmembers(snake_case_						,		inspect.isclass      )      )

 __lowerCAmelCase											=						None
 for cls_name, cls in cls_members.items():
  if (
      cls_name != DiffusionPipeline.__name__
      and issubclass(cls						,		snake_case_      )
      and cls.__module__.split("""."""      )[0] != "diffusers"
  ):
   if pipeline_class is not None:
    raise ValueError(
        f"""Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"""
        f""" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"""
        f""" {loaded_module}."""      )
   __lowerCAmelCase											=						cls

 return pipeline_class







def      UpperCamelCase_						(					snake_case_       :       Union[str, os.PathLike]						,		snake_case_       :       str						,		snake_case_       :       Optional[Union[str, os.PathLike]] = None						,		snake_case_       :       bool = False						,		snake_case_       :       bool = False						,		snake_case_       :       Optional[Dict[str, str]] = None						,		snake_case_       :       Optional[Union[bool, str]] = None						,		snake_case_       :       Optional[str] = None						,		snake_case_       :       bool = False						,		)					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						str(snake_case_      )

 __lowerCAmelCase											=						os.path.join(snake_case_						,		snake_case_      )

 if os.path.isfile(snake_case_      ):
  __lowerCAmelCase											=						module_file_or_url
  __lowerCAmelCase											=						"""local"""
 elif pretrained_model_name_or_path.count("""/"""      ) == 0:
  __lowerCAmelCase											=						get_diffusers_versions()
  # cut ".dev0"
  __lowerCAmelCase											=						"""v""" + """.""".join(__version__.split("""."""      )[:3]      )

  # retrieve github version that matches
  if revision is None:
   __lowerCAmelCase											=						latest_version if latest_version[1:] in available_versions else """main"""
   logger.info(f"""Defaulting to latest_version: {revision}."""      )
  elif revision in available_versions:
   __lowerCAmelCase											=						f"""v{revision}"""
  elif revision == "main":
   __lowerCAmelCase											=						revision
  else:
   raise ValueError(
       f"""`custom_revision`: {revision} does not exist. Please make sure to choose one of"""
       f""" {", ".join(available_versions + ["main"]      )}."""      )

  # community pipeline on GitHub
  __lowerCAmelCase											=						COMMUNITY_PIPELINES_URL.format(revision=snake_case_						,		pipeline=snake_case_      )
  try:
   __lowerCAmelCase											=						cached_download(
       snake_case_						,		cache_dir=snake_case_						,		force_download=snake_case_						,		proxies=snake_case_						,		resume_download=snake_case_						,		local_files_only=snake_case_						,		use_auth_token=snake_case_						,		)
   __lowerCAmelCase											=						"""git"""
   __lowerCAmelCase											=						pretrained_model_name_or_path + """.py"""
  except EnvironmentError:
   logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}."""      )
   raise
 else:
  try:
   # Load from URL or cache if already cached
   __lowerCAmelCase											=						hf_hub_download(
       snake_case_						,		snake_case_						,		cache_dir=snake_case_						,		force_download=snake_case_						,		proxies=snake_case_						,		resume_download=snake_case_						,		local_files_only=snake_case_						,		use_auth_token=snake_case_						,		)
   __lowerCAmelCase											=						os.path.join("""local"""						,		"""--""".join(pretrained_model_name_or_path.split("""/"""      )      )      )
  except EnvironmentError:
   logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}."""      )
   raise

    # Check we have all the requirements in our environment
 __lowerCAmelCase											=						check_imports(snake_case_      )

 # Now we move the module inside our cached dynamic modules.
 __lowerCAmelCase											=						DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
 create_dynamic_module(snake_case_      )
 __lowerCAmelCase											=						Path(snake_case_      ) / full_submodule
 if submodule == "local" or submodule == "git":
  # We always copy local files (we could hash the file to see if there was a change, and give them the name of
  # that hash, to only copy when there is a modification but it seems overkill for now).
  # The only reason we do the copy is to avoid putting too many folders in sys.path.
  shutil.copy(snake_case_						,		submodule_path / module_file      )
  for module_needed in modules_needed:
   __lowerCAmelCase											=						f"""{module_needed}.py"""
   shutil.copy(os.path.join(snake_case_						,		snake_case_      )						,		submodule_path / module_needed      )
 else:
  # Get the commit hash
  # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
  if isinstance(snake_case_						,		snake_case_      ):
   __lowerCAmelCase											=						use_auth_token
  elif use_auth_token is True:
   __lowerCAmelCase											=						HfFolder.get_token()
  else:
   __lowerCAmelCase											=						None

  __lowerCAmelCase											=						model_info(snake_case_						,		revision=snake_case_						,		token=snake_case_      ).sha

  # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
  # benefit of versioning.
  __lowerCAmelCase											=						submodule_path / commit_hash
  __lowerCAmelCase											=						full_submodule + os.path.sep + commit_hash
  create_dynamic_module(snake_case_      )

  if not (submodule_path / module_file).exists():
   shutil.copy(snake_case_						,		submodule_path / module_file      )
  # Make sure we also have every file with relative
  for module_needed in modules_needed:
   if not (submodule_path / module_needed).exists():
    get_cached_module_file(
        snake_case_						,		f"""{module_needed}.py"""						,		cache_dir=snake_case_						,		force_download=snake_case_						,		resume_download=snake_case_						,		proxies=snake_case_						,		use_auth_token=snake_case_						,		revision=snake_case_						,		local_files_only=snake_case_						,		)
 return os.path.join(snake_case_						,		snake_case_      )







def      UpperCamelCase_						(					snake_case_       :       Union[str, os.PathLike]						,		snake_case_       :       str						,		snake_case_       :       Optional[str] = None						,		snake_case_       :       Optional[Union[str, os.PathLike]] = None						,		snake_case_       :       bool = False						,		snake_case_       :       bool = False						,		snake_case_       :       Optional[Dict[str, str]] = None						,		snake_case_       :       Optional[Union[bool, str]] = None						,		snake_case_       :       Optional[str] = None						,		snake_case_       :       bool = False						,		**snake_case_       :       Dict						,		)					->     Tuple:






 '''simple docstring'''

 __lowerCAmelCase											=						get_cached_module_file(
     snake_case_						,		snake_case_						,		cache_dir=snake_case_						,		force_download=snake_case_						,		resume_download=snake_case_						,		proxies=snake_case_						,		use_auth_token=snake_case_						,		revision=snake_case_						,		local_files_only=snake_case_						,		)
 return get_class_in_module(snake_case_						,		final_module.replace(""".py"""						,		""""""      )      )






