





"""simple docstring"""





import os
import sys
import unittest


__A											=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__A											=						os.path.join(git_repo_path, """src""", """transformers""")

__A											=						"""
{0} = None
"""

__A											=						"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


__A											=						"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""






class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")' )
         self.assertIsNone(__UpperCAmelCase )

         lowerCAmelCase__     :int											=				find_backend('    if not is_tokenizers_available():' )
         self.assertEqual(__UpperCAmelCase       ,					'tokenizers' )

         lowerCAmelCase__     :List[Any]											=				find_backend('    if not is_tensorflow_text_available():' )
         self.assertEqual(__UpperCAmelCase       ,					'tensorflow_text' )

         lowerCAmelCase__     :List[Any]											=				find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):' )
         self.assertEqual(__UpperCAmelCase       ,					'sentencepiece_and_tokenizers' )

         lowerCAmelCase__     :str											=				find_backend(
             '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
         self.assertEqual(__UpperCAmelCase       ,					'sentencepiece_and_tensorflow_text' )

         lowerCAmelCase__     :Any											=				find_backend(
             '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
         self.assertEqual(__UpperCAmelCase       ,					'sentencepiece_and_tokenizers_and_vision' )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				read_init()
         # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
         self.assertIn('torch'       ,					__UpperCAmelCase )
         self.assertIn('tensorflow_text'       ,					__UpperCAmelCase )
         self.assertIn('sentencepiece_and_tokenizers'       ,					__UpperCAmelCase )

         # Likewise, we can't assert on the exact content of a key
         self.assertIn('BertModel'       ,					objects['torch'] )
         self.assertIn('TFBertModel'       ,					objects['tf'] )
         self.assertIn('FlaxBertModel'       ,					objects['flax'] )
         self.assertIn('BertModel'       ,					objects['torch'] )
         self.assertIn('TFBertTokenizer'       ,					objects['tensorflow_text'] )
         self.assertIn('convert_slow_tokenizer'       ,					objects['sentencepiece_and_tokenizers'] )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				create_dummy_object('CONSTANT'       ,					'\'torch\'' )
         self.assertEqual(__UpperCAmelCase       ,					'\nCONSTANT = None\n' )

         lowerCAmelCase__     :Optional[int]											=				create_dummy_object('function'       ,					'\'torch\'' )
         self.assertEqual(
             __UpperCAmelCase       ,					'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n' )

         lowerCAmelCase__     :Union[str, Any]											=				'\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
         lowerCAmelCase__     :Union[str, Any]											=				create_dummy_object('FakeClass'       ,					'\'torch\'' )
         self.assertEqual(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				'# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
         lowerCAmelCase__     :Optional[int]											=				create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
         self.assertEqual(dummy_files['torch']       ,					__UpperCAmelCase )







"""simple docstring"""





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:torch.FloatTensor






class    _lowerCAmelCase  (	a    ,  a			):
       """simple docstring"""




       @register_to_config
       def __init__(       self       ,					__UpperCAmelCase = 1_6       ,					__UpperCAmelCase = 8_8       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 1       ,					__UpperCAmelCase = 0.0       ,					__UpperCAmelCase = 3_2       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = "geglu"       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         super().__init__()
         lowerCAmelCase__     :Dict											=				num_attention_heads
         lowerCAmelCase__     :Any											=				attention_head_dim
         lowerCAmelCase__     :Optional[int]											=				num_attention_heads * attention_head_dim

         lowerCAmelCase__     :Any											=				in_channels

         lowerCAmelCase__     :str											=				torch.nn.GroupNorm(num_groups=__UpperCAmelCase       ,					num_channels=__UpperCAmelCase       ,					eps=1E-6       ,					affine=__UpperCAmelCase )
         lowerCAmelCase__     :int											=				nn.Linear(__UpperCAmelCase       ,					__UpperCAmelCase )

         # 3. Define transformers blocks
         lowerCAmelCase__     :List[Any]											=				nn.ModuleList(
             [
                 BasicTransformerBlock(
                     __UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					dropout=__UpperCAmelCase       ,					cross_attention_dim=__UpperCAmelCase       ,					activation_fn=__UpperCAmelCase       ,					attention_bias=__UpperCAmelCase       ,					double_self_attention=__UpperCAmelCase       ,					norm_elementwise_affine=__UpperCAmelCase       ,					)
                 for d in range(__UpperCAmelCase )
             ] )

         lowerCAmelCase__     :List[Any]											=				nn.Linear(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=1       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :int											=				hidden_states.shape
         lowerCAmelCase__     :Tuple											=				batch_frames // num_frames

         lowerCAmelCase__     :str											=				hidden_states

         lowerCAmelCase__     :Union[str, Any]											=				hidden_states[None, :].reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
         lowerCAmelCase__     :str											=				hidden_states.permute(0       ,					2       ,					1       ,					3       ,					4 )

         lowerCAmelCase__     :Optional[int]											=				self.norm(__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				hidden_states.permute(0       ,					3       ,					4       ,					2       ,					1 ).reshape(batch_size * height * width       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				self.proj_in(__UpperCAmelCase )

         # 2. Blocks
         for block in self.transformer_blocks:
           lowerCAmelCase__     :Optional[int]											=				block(
               __UpperCAmelCase       ,					encoder_hidden_states=__UpperCAmelCase       ,					timestep=__UpperCAmelCase       ,					cross_attention_kwargs=__UpperCAmelCase       ,					class_labels=__UpperCAmelCase       ,					)

         # 3. Output
         lowerCAmelCase__     :Any											=				self.proj_out(__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				(
             hidden_states[None, None, :]
             .reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
             .permute(0       ,					3       ,					4       ,					1       ,					2 )
             .contiguous()
         )
         lowerCAmelCase__     :Optional[Any]											=				hidden_states.reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				hidden_states + residual

         if not return_dict:
           return (output,)

         return TransformerTemporalModelOutput(sample=__UpperCAmelCase )
