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			):
     _SCREAMING_SNAKE_CASE     :     torch.FloatTensor





class      lowerCAmelCase						(					_a			,		_a			):



     @register_to_config
     def __init__(					self				,      lowerCAmelCase__ = 16				,      lowerCAmelCase__ = 88				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = 1				,      lowerCAmelCase__ = 0.0				,      lowerCAmelCase__ = 32				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = False				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = "geglu"				,      lowerCAmelCase__ = True				,      lowerCAmelCase__ = True				,      ):
            super().__init__()
            _A=	num_attention_heads
            _A=	attention_head_dim
            _A=	num_attention_heads * attention_head_dim

            _A=	in_channels

            _A=	torch.nn.GroupNorm(num_groups=lowerCAmelCase__				,      num_channels=lowerCAmelCase__				,      eps=1E-6				,      affine=lowerCAmelCase__       )
            _A=	nn.Linear(lowerCAmelCase__				,      lowerCAmelCase__       )

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

            _A=	nn.Linear(lowerCAmelCase__				,      lowerCAmelCase__       )





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      lowerCAmelCase__=1				,      lowerCAmelCase__=None				,      lowerCAmelCase__ = True				,      ):
            _A,			_A,			_A,			_A=	hidden_states.shape
            _A=	batch_frames // num_frames

            _A=	hidden_states

            _A=	hidden_states[None, :].reshape(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )
            _A=	hidden_states.permute(0				,      2				,      1				,      3				,      4       )

            _A=	self.norm(lowerCAmelCase__       )
            _A=	hidden_states.permute(0				,      3				,      4				,      2				,      1       ).reshape(batch_size * height * width				,      lowerCAmelCase__				,      lowerCAmelCase__       )

            _A=	self.proj_in(lowerCAmelCase__       )

            # 2. Blocks
            for block in self.transformer_blocks:
                   _A=	block(
                       lowerCAmelCase__				,      encoder_hidden_states=lowerCAmelCase__				,      timestep=lowerCAmelCase__				,      cross_attention_kwargs=lowerCAmelCase__				,      class_labels=lowerCAmelCase__				,      )

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

            _A=	hidden_states + residual

            if not return_dict:
                   return (output,)

            return TransformerTemporalModelOutput(sample=lowerCAmelCase__       )
import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
      import torch

if is_soundfile_availble():
      import soundfile as sf

if is_vision_available():
      from PIL import Image

def 							UpperCamelCase				(    lowerCAmelCase_=""   )		->						str:




       '''simple docstring'''






       _A=	tempfile.mkdtemp()
       return os.path.join(lowerCAmelCase_       ,					str(uuid.uuida()   ) + suffix   )





@require_soundfile
@require_torch
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.rand(12				,      dtype=torch.floataa       ) - 0.5
            _A=	AgentAudio(lowerCAmelCase__       )
            _A=	str(agent_type.to_string()       )

            # Ensure that the tensor and the agent_type's tensor are the same
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      agent_type.to_raw()				,      atol=1E-4       )       )

            del agent_type

            # Ensure the path remains even after the object deletion
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )

            # Ensure that the file contains the same value as the original tensor
            _A,			_A=	sf.read(lowerCAmelCase__       )
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      torch.tensor(lowerCAmelCase__       )				,      atol=1E-4       )       )





     def 			a__  (					self       ):
            _A=	torch.rand(12				,      dtype=torch.floataa       ) - 0.5
            _A=	get_new_path(suffix='.wav'       )
            sf.write(lowerCAmelCase__				,      lowerCAmelCase__				,      16000       )

            _A=	AgentAudio(lowerCAmelCase__       )

            self.assertTrue(torch.allclose(lowerCAmelCase__				,      agent_type.to_raw()				,      atol=1E-4       )       )
            self.assertEqual(agent_type.to_string()				,      lowerCAmelCase__       )





@require_vision
@require_torch
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.randint(0				,      256				,      (64, 64, 3)       )
            _A=	AgentImage(lowerCAmelCase__       )
            _A=	str(agent_type.to_string()       )

            # Ensure that the tensor and the agent_type's tensor are the same
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      agent_type._tensor				,      atol=1E-4       )       )

            self.assertIsInstance(agent_type.to_raw()				,      Image.Image       )

            # Ensure the path remains even after the object deletion
            del agent_type
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )



     def 			a__  (					self       ):
            _A=	Path(get_tests_dir('fixtures/tests_samples/COCO'       )       ) / '000000039769.png'
            _A=	Image.open(lowerCAmelCase__       )
            _A=	AgentImage(lowerCAmelCase__       )

            self.assertTrue(path.samefile(agent_type.to_string()       )       )
            self.assertTrue(image == agent_type.to_raw()       )

            # Ensure the path remains even after the object deletion
            del agent_type
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )





     def 			a__  (					self       ):
            _A=	Path(get_tests_dir('fixtures/tests_samples/COCO'       )       ) / '000000039769.png'
            _A=	Image.open(lowerCAmelCase__       )
            _A=	AgentImage(lowerCAmelCase__       )

            self.assertFalse(path.samefile(agent_type.to_string()       )       )
            self.assertTrue(image == agent_type.to_raw()       )

            # Ensure the path remains even after the object deletion
            del agent_type
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )





class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	'Hey!'
            _A=	AgentText(lowerCAmelCase__       )

            self.assertEqual(lowerCAmelCase__				,      agent_type.to_string()       )
            self.assertEqual(lowerCAmelCase__				,      agent_type.to_raw()       )
            self.assertEqual(lowerCAmelCase__				,      lowerCAmelCase__       )