





"""simple docstring"""
import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()






class 					_UpperCAmelCase   (	lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							PriorTransformer
					UpperCamelCase      =							'''hidden_states'''


					@property
					def 	lowerCamelCase       (					self			:Optional[int]		):
												A		=  4
												A		=  8
												A		=  7

												A		=  floats_tensor((batch_size, embedding_dim)		).to(__UpperCamelCase		)

												A		=  floats_tensor((batch_size, embedding_dim)		).to(__UpperCamelCase		)
												A		=  floats_tensor((batch_size, num_embeddings, embedding_dim)		).to(__UpperCamelCase		)

												return {
												    "hidden_states": hidden_states,
												    "timestep": 2,
												    "proj_embedding": proj_embedding,
												    "encoder_hidden_states": encoder_hidden_states,
												}


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:List[str]=0		):
												torch.manual_seed(__UpperCamelCase		)
												A		=  4
												A		=  8
												A		=  7

												A		=  torch.randn((batch_size, embedding_dim)		).to(__UpperCamelCase		)

												A		=  torch.randn((batch_size, embedding_dim)		).to(__UpperCamelCase		)
												A		=  torch.randn((batch_size, num_embeddings, embedding_dim)		).to(__UpperCamelCase		)

												return {
												    "hidden_states": hidden_states,
												    "timestep": 2,
												    "proj_embedding": proj_embedding,
												    "encoder_hidden_states": encoder_hidden_states,
												}


					@property
					def 	lowerCamelCase       (					self			:int		):
												return (4, 8)


					@property
					def 	lowerCamelCase       (					self			:Optional[int]		):
												return (4, 8)


					def 	lowerCamelCase       (					self			:List[str]		):
												A		=  {
												    "num_attention_heads": 2,
												    "attention_head_dim": 4,
												    "num_layers": 2,
												    "embedding_dim": 8,
												    "num_embeddings": 7,
												    "additional_embeddings": 4,
												}
												A		=  self.dummy_input
												return init_dict, inputs_dict


					def 	lowerCamelCase       (					self			:Dict		):
												A,	A		=  PriorTransformer.from_pretrained(
												    "hf-internal-testing/prior-dummy"     ,       output_loading_info=__UpperCamelCase		)
												self.assertIsNotNone(__UpperCamelCase		)
												self.assertEqual(len(loading_info["missing_keys"]		)     ,       0		)

												model.to(__UpperCamelCase		)
												A		=  model(**self.dummy_input		)[0]

												assert hidden_states is not None, "Make sure output is not None"


					def 	lowerCamelCase       (					self			:Optional[int]		):
												A,	A		=  self.prepare_init_args_and_inputs_for_common()

												A		=  self.model_class(**__UpperCamelCase		)
												A		=  inspect.signature(model.forward		)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												A		=  [*signature.parameters.keys()]

												A		=  ["hidden_states", "timestep"]
												self.assertListEqual(arg_names[:2]     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												A		=  PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"		)
												A		=  model.to(__UpperCamelCase		)

												if hasattr(__UpperCamelCase     ,       "set_default_attn_processor"		):
																			model.set_default_attn_processor()

												A		=  self.get_dummy_seed_input()

												with torch.no_grad():
																			A		=  model(**__UpperCamelCase		)[0]

												A		=  output[0, :5].flatten().cpu()
												print(__UpperCamelCase		)

												# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
												# the expected output slices are not the same for CPU and GPU.
												A		=  torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]		)
												self.assertTrue(torch_all_close(__UpperCamelCase     ,       __UpperCamelCase     ,       rtol=1e-2		)		)







@slow
class 					_UpperCAmelCase   (	unittest.TestCase ):


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Dict=1     ,       __UpperCamelCase			:List[str]=7_68     ,       __UpperCamelCase			:List[Any]=77     ,       __UpperCamelCase			:Optional[Any]=0		):
												torch.manual_seed(__UpperCamelCase		)
												A		=  batch_size
												A		=  embedding_dim
												A		=  num_embeddings

												A		=  torch.randn((batch_size, embedding_dim)		).to(__UpperCamelCase		)

												A		=  torch.randn((batch_size, embedding_dim)		).to(__UpperCamelCase		)
												A		=  torch.randn((batch_size, num_embeddings, embedding_dim)		).to(__UpperCamelCase		)

												return {
												    "hidden_states": hidden_states,
												    "timestep": 2,
												    "proj_embedding": proj_embedding,
												    "encoder_hidden_states": encoder_hidden_states,
												}


					def 	lowerCamelCase       (					self			:int		):
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


					@parameterized.expand(
					    [
					        # fmt: off
					        [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
					        [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
					        # fmt: on
					    ]		)
					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:Optional[Any]     ,       __UpperCamelCase			:List[str]		):
												A		=  PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"     ,       subfolder="prior"		)
												model.to(__UpperCamelCase		)
												A		=  self.get_dummy_seed_input(seed=__UpperCamelCase		)

												with torch.no_grad():
																			A		=  model(**__UpperCamelCase		)[0]

												assert list(sample.shape		) == [1, 7_68]

												A		=  sample[0, :8].flatten().cpu()
												print(__UpperCamelCase		)
												A		=  torch.tensor(__UpperCamelCase		)

												assert torch_all_close(__UpperCamelCase     ,       __UpperCamelCase     ,       atol=1e-3		)





import argparse
import os
import re


lowercase_									=   'src/transformers'

# Pattern that looks at the indentation in a line.
lowercase_									=   re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
lowercase_									=   re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowercase_									=   re.compile(R'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
lowercase_									=   re.compile(R'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowercase_									=   re.compile(R'\[([^\]]+)\]')
def      a      (			A__	:   Dict     )    ->      Optional[Any]:





      """simple docstring"""



      _lowercase	       =_re_indent.search(A__     )
      return "" if search is None else search.groups()[0]
def      a      (			A__	:   Optional[Any]      ,   A__	:   Dict=""      ,   A__	:   Union[str, Any]=None      ,   A__	:   Tuple=None     )    ->      Dict:





      """simple docstring"""



      _lowercase	       =0
      _lowercase	       =code.split('\n'     )
      if start_prompt is not None:
            while not lines[index].startswith(A__     ):
                  index += 1
            _lowercase	       =['\n'.join(lines[:index]     )]
      else:
            _lowercase	       =[]

      # We split into blocks until we get to the `end_prompt` (or the end of the block).
      _lowercase	       =[lines[index]]
      index += 1
      while index < len(A__     ) and (end_prompt is None or not lines[index].startswith(A__     )):
            if len(lines[index]     ) > 0 and get_indent(lines[index]     ) == indent_level:
                  if len(A__     ) > 0 and get_indent(current_block[-1]     ).startswith(indent_level + ' '     ):
                        current_block.append(lines[index]     )
                        blocks.append('\n'.join(A__     )     )
                        if index < len(A__     ) - 1:
                              _lowercase	       =[lines[index + 1]]
                              index += 1
                        else:
                              _lowercase	       =[]
                  else:
                        blocks.append('\n'.join(A__     )     )
                        _lowercase	       =[lines[index]]
            else:
                  current_block.append(lines[index]     )
            index += 1

      # Adds current block if it's nonempty.
      if len(A__     ) > 0:
            blocks.append('\n'.join(A__     )     )

      # Add final block after end_prompt if provided.
      if end_prompt is not None and index < len(A__     ):
            blocks.append('\n'.join(lines[index:]     )     )

      return blocks
def      a      (			A__	:   int     )    ->      Union[str, Any]:





      """simple docstring"""



      def _inner(A__	:   Any     ):
            return key(A__     ).lower().replace('_'      ,   ''     )

      return _inner
def      a      (			A__	:   Any      ,   A__	:   Union[str, Any]=None     )    ->      int:





      """simple docstring"""



      def noop(A__	:   Optional[int]     ):
            return x

      if key is None:
            _lowercase	       =noop
      # Constants are all uppercase, they go first.
      _lowercase	       =[obj for obj in objects if key(A__     ).isupper()]
      # Classes are not all uppercase but start with a capital, they go second.
      _lowercase	       =[obj for obj in objects if key(A__     )[0].isupper() and not key(A__     ).isupper()]
      # Functions begin with a lowercase, they go last.
      _lowercase	       =[obj for obj in objects if not key(A__     )[0].isupper()]

      _lowercase	       =ignore_underscore(A__     )
      return sorted(A__      ,   key=A__     ) + sorted(A__      ,   key=A__     ) + sorted(A__      ,   key=A__     )
def      a      (			A__	:   Union[str, Any]     )    ->      Tuple:





      """simple docstring"""



      def _replace(A__	:   Optional[int]     ):
            _lowercase	       =match.groups()[0]
            if "," not in imports:
                  return F'''[{imports}]'''
            _lowercase	       =[part.strip().replace('"'      ,   ''     ) for part in imports.split(','     )]
            # We will have a final empty element if the line finished with a comma.
            if len(keys[-1]     ) == 0:
                  _lowercase	       =keys[:-1]
            return "[" + ", ".join([F'''"{k}"''' for k in sort_objects(A__     )]     ) + "]"

      _lowercase	       =import_statement.split('\n'     )
      if len(A__     ) > 3:
            # Here we have to sort internal imports that are on several lines (one per name):
            # key: [
            #     "object1",
            #     "object2",
            #     ...
            # ]

            # We may have to ignore one or two lines on each side.
            _lowercase	       =2 if lines[1].strip() == '[' else 1
            _lowercase	       =[(i, _re_strip_line.search(A__     ).groups()[0]) for i, line in enumerate(lines[idx:-idx]     )]
            _lowercase	       =sort_objects(A__      ,   key=lambda A__     : x[1]     )
            _lowercase	       =[lines[x[0] + idx] for x in sorted_indices]
            return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]     )
      elif len(A__     ) == 3:
            # Here we have to sort internal imports that are on one separate line:
            # key: [
            #     "object1", "object2", ...
            # ]
            if _re_bracket_content.search(lines[1]     ) is not None:
                  _lowercase	       =_re_bracket_content.sub(_replace      ,   lines[1]     )
            else:
                  _lowercase	       =[part.strip().replace('"'      ,   ''     ) for part in lines[1].split(','     )]
                  # We will have a final empty element if the line finished with a comma.
                  if len(keys[-1]     ) == 0:
                        _lowercase	       =keys[:-1]
                  _lowercase	       =get_indent(lines[1]     ) + ', '.join([F'''"{k}"''' for k in sort_objects(A__     )]     )
            return "\n".join(A__     )
      else:
            # Finally we have to deal with imports fitting on one line
            _lowercase	       =_re_bracket_content.sub(_replace      ,   A__     )
            return import_statement
def      a      (			A__	:   Dict      ,   A__	:   int=True     )    ->      Optional[Any]:





      """simple docstring"""



      with open(A__      ,   encoding='utf-8'     ) as f:
            _lowercase	       =f.read()

      if "_import_structure" not in code:
            return

      # Blocks of indent level 0
      _lowercase	       =split_code_in_indented_blocks(
          A__      ,   start_prompt='_import_structure = {'      ,   end_prompt='if TYPE_CHECKING:'     )

      # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
      for block_idx in range(1      ,   len(A__     ) - 1     ):
            # Check if the block contains some `_import_structure`s thingy to sort.
            _lowercase	       =main_blocks[block_idx]
            _lowercase	       =block.split('\n'     )

            # Get to the start of the imports.
            _lowercase	       =0
            while line_idx < len(A__     ) and "_import_structure" not in block_lines[line_idx]:
                  # Skip dummy import blocks
                  if "import dummy" in block_lines[line_idx]:
                        _lowercase	       =len(A__     )
                  else:
                        line_idx += 1
            if line_idx >= len(A__     ):
                  continue

            # Ignore beginning and last line: they don't contain anything.
            _lowercase	       ='\n'.join(block_lines[line_idx:-1]     )
            _lowercase	       =get_indent(block_lines[1]     )
            # Slit the internal block into blocks of indent level 1.
            _lowercase	       =split_code_in_indented_blocks(A__      ,   indent_level=A__     )
            # We have two categories of import key: list or _import_structure[key].append/extend
            _lowercase	       =_re_direct_key if '_import_structure = {' in block_lines[0] else _re_indirect_key
            # Grab the keys, but there is a trap: some lines are empty or just comments.
            _lowercase	       =[(pattern.search(A__     ).groups()[0] if pattern.search(A__     ) is not None else None) for b in internal_blocks]
            # We only sort the lines with a key.
            _lowercase	       =[(i, key) for i, key in enumerate(A__     ) if key is not None]
            _lowercase	       =[x[0] for x in sorted(A__      ,   key=lambda A__     : x[1]     )]

            # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
            _lowercase	       =0
            _lowercase	       =[]
            for i in range(len(A__     )     ):
                  if keys[i] is None:
                        reorderded_blocks.append(internal_blocks[i]     )
                  else:
                        _lowercase	       =sort_objects_in_import(internal_blocks[sorted_indices[count]]     )
                        reorderded_blocks.append(A__     )
                        count += 1

        # And we put our main block back together with its first and last line.
            _lowercase	       ='\n'.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]     )

      if code != "\n".join(A__     ):
            if check_only:
                  return True
            else:
                  print(F'''Overwriting {file}.'''     )
                  with open(A__      ,   'w'      ,   encoding='utf-8'     ) as f:
                        f.write('\n'.join(A__     )     )
def      a      (			A__	:   List[Any]=True     )    ->      List[str]:





      """simple docstring"""



      _lowercase	       =[]
      for root, _, files in os.walk(A__     ):
            if "__init__.py" in files:
                  _lowercase	       =sort_imports(os.path.join(A__      ,   '__init__.py'     )      ,   check_only=A__     )
                  if result:
                        _lowercase	       =[os.path.join(A__      ,   '__init__.py'     )]
      if len(A__     ) > 0:
            raise ValueError(F'''Would overwrite {len(A__     )} files, run `make style`.'''     )


if __name__ == "__main__":
  lowercase_									=   argparse.ArgumentParser()
  parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
  lowercase_									=   parser.parse_args()

  sort_imports_in_all_inits(check_only=args.check_only)


