import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np




class 			__magic_name__	:





   """simple docstring"""




   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    torch.manual_seed(0				)
    _UpperCamelCase:  str    						=			TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

    torch.manual_seed(0				)
    _UpperCamelCase:  List[Any]    						=			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

    torch.manual_seed(0				)
    _UpperCamelCase:  Tuple    						=			UNetaDConditionModel(
        sample_size=32		,	layers_per_block=1		,	block_out_channels=[32, 64]		,	down_block_types=[
            '''ResnetDownsampleBlock2D''',
            '''SimpleCrossAttnDownBlock2D''',
        ]		,	mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''		,	up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']		,	in_channels=3		,	out_channels=6		,	cross_attention_dim=32		,	encoder_hid_dim=32		,	attention_head_dim=8		,	addition_embed_type='''text'''		,	addition_embed_type_num_heads=2		,	cross_attention_norm='''group_norm'''		,	resnet_time_scale_shift='''scale_shift'''		,	act_fn='''gelu'''		,	)
    unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

    torch.manual_seed(0				)
    _UpperCamelCase:  int    						=			DDPMScheduler(
        num_train_timesteps=1_000		,	beta_schedule='''squaredcos_cap_v2'''		,	beta_start=0.0001		,	beta_end=0.02		,	thresholding=_lowercase		,	dynamic_thresholding_ratio=0.95		,	sample_max_value=1.0		,	prediction_type='''epsilon'''		,	variance_type='''learned_range'''		,	)

    torch.manual_seed(0				)
    _UpperCamelCase:  Optional[Any]    						=			IFWatermarker()

    return {
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "unet": unet,
        "scheduler": scheduler,
        "watermarker": watermarker,
        "safety_checker": None,
        "feature_extractor": None,
    }



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    torch.manual_seed(0				)
    _UpperCamelCase:  Dict    						=			TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

    torch.manual_seed(0				)
    _UpperCamelCase:  str    						=			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

    torch.manual_seed(0				)
    _UpperCamelCase:  Optional[int]    						=			UNetaDConditionModel(
        sample_size=32		,	layers_per_block=[1, 2]		,	block_out_channels=[32, 64]		,	down_block_types=[
            '''ResnetDownsampleBlock2D''',
            '''SimpleCrossAttnDownBlock2D''',
        ]		,	mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''		,	up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']		,	in_channels=6		,	out_channels=6		,	cross_attention_dim=32		,	encoder_hid_dim=32		,	attention_head_dim=8		,	addition_embed_type='''text'''		,	addition_embed_type_num_heads=2		,	cross_attention_norm='''group_norm'''		,	resnet_time_scale_shift='''scale_shift'''		,	act_fn='''gelu'''		,	class_embed_type='''timestep'''		,	mid_block_scale_factor=1.414		,	time_embedding_act_fn='''gelu'''		,	time_embedding_dim=32		,	)
    unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

    torch.manual_seed(0				)
    _UpperCamelCase:  Union[str, Any]    						=			DDPMScheduler(
        num_train_timesteps=1_000		,	beta_schedule='''squaredcos_cap_v2'''		,	beta_start=0.0001		,	beta_end=0.02		,	thresholding=_lowercase		,	dynamic_thresholding_ratio=0.95		,	sample_max_value=1.0		,	prediction_type='''epsilon'''		,	variance_type='''learned_range'''		,	)

    torch.manual_seed(0				)
    _UpperCamelCase:  Tuple    						=			DDPMScheduler(
        num_train_timesteps=1_000		,	beta_schedule='''squaredcos_cap_v2'''		,	beta_start=0.0001		,	beta_end=0.02		,	)

    torch.manual_seed(0				)
    _UpperCamelCase:  str    						=			IFWatermarker()

    return {
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "unet": unet,
        "scheduler": scheduler,
        "image_noising_scheduler": image_noising_scheduler,
        "watermarker": watermarker,
        "safety_checker": None,
        "feature_extractor": None,
    }



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			self.get_dummy_components()
    _UpperCamelCase:  Dict    						=			self.pipeline_class(**_lowercase				)
    pipe.to(_lowercase				)
    pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  Union[str, Any]    						=			self.get_dummy_inputs(_lowercase				)

    _UpperCamelCase:  Optional[Any]    						=			inputs['''prompt''']
    _UpperCamelCase:  str    						=			inputs['''generator''']
    _UpperCamelCase:  str    						=			inputs['''num_inference_steps''']
    _UpperCamelCase:  Dict    						=			inputs['''output_type''']

    if "image" in inputs:
     _UpperCamelCase:  List[str]    						=			inputs['''image''']
    else:
     _UpperCamelCase:  Union[str, Any]    						=			None

    if "mask_image" in inputs:
     _UpperCamelCase:  str    						=			inputs['''mask_image''']
    else:
     _UpperCamelCase:  str    						=			None

    if "original_image" in inputs:
     _UpperCamelCase:  int    						=			inputs['''original_image''']
    else:
     _UpperCamelCase:  Optional[Any]    						=			None

    _UpperCamelCase						,				_UpperCamelCase:  Tuple    						=			pipe.encode_prompt(_lowercase				)

    # inputs with prompt converted to embeddings
    _UpperCamelCase:  int    						=			{
        '''prompt_embeds''': prompt_embeds,
        '''negative_prompt_embeds''': negative_prompt_embeds,
        '''generator''': generator,
        '''num_inference_steps''': num_inference_steps,
        '''output_type''': output_type,
    }

    if image is not None:
     _UpperCamelCase:  List[Any]    						=			image

    if mask_image is not None:
     _UpperCamelCase:  List[str]    						=			mask_image

    if original_image is not None:
     _UpperCamelCase:  List[Any]    						=			original_image

    # set all optional components to None
    for optional_component in pipe._optional_components:
     setattr(_lowercase		,	_lowercase		,	_lowercase				)

    _UpperCamelCase:  Dict    						=			pipe(**_lowercase				)[0]

    with tempfile.TemporaryDirectory() as tmpdir:
     pipe.save_pretrained(_lowercase				)
     _UpperCamelCase:  Any    						=			self.pipeline_class.from_pretrained(_lowercase				)
     pipe_loaded.to(_lowercase				)
     pipe_loaded.set_progress_bar_config(disable=_lowercase				)

    pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

    for optional_component in pipe._optional_components:
     self.assertTrue(
         getattr(_lowercase		,	_lowercase				) is None		,	f"""`{optional_component}` did not stay set to None after loading."""		,	)

    _UpperCamelCase:  int    						=			self.get_dummy_inputs(_lowercase				)

    _UpperCamelCase:  Optional[Any]    						=			inputs['''generator''']
    _UpperCamelCase:  Tuple    						=			inputs['''num_inference_steps''']
    _UpperCamelCase:  Tuple    						=			inputs['''output_type''']

    # inputs with prompt converted to embeddings
    _UpperCamelCase:  Dict    						=			{
        '''prompt_embeds''': prompt_embeds,
        '''negative_prompt_embeds''': negative_prompt_embeds,
        '''generator''': generator,
        '''num_inference_steps''': num_inference_steps,
        '''output_type''': output_type,
    }

    if image is not None:
     _UpperCamelCase:  List[Any]    						=			image

    if mask_image is not None:
     _UpperCamelCase:  Union[str, Any]    						=			mask_image

    if original_image is not None:
     _UpperCamelCase:  Optional[int]    						=			original_image

    _UpperCamelCase:  List[Any]    						=			pipe_loaded(**_lowercase				)[0]

    _UpperCamelCase:  Optional[int]    						=			np.abs(to_np(_lowercase				) - to_np(_lowercase				)				).max()
    self.assertLess(_lowercase		,	1E-4				)



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			self.get_dummy_components()
    _UpperCamelCase:  Optional[Any]    						=			self.pipeline_class(**_lowercase				)
    pipe.to(_lowercase				)
    pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  Dict    						=			self.get_dummy_inputs(_lowercase				)
    _UpperCamelCase:  int    						=			pipe(**_lowercase				)[0]

    with tempfile.TemporaryDirectory() as tmpdir:
     pipe.save_pretrained(_lowercase				)
     _UpperCamelCase:  Any    						=			self.pipeline_class.from_pretrained(_lowercase				)
     pipe_loaded.to(_lowercase				)
     pipe_loaded.set_progress_bar_config(disable=_lowercase				)

    pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

    _UpperCamelCase:  List[Any]    						=			self.get_dummy_inputs(_lowercase				)
    _UpperCamelCase:  str    						=			pipe_loaded(**_lowercase				)[0]

    _UpperCamelCase:  int    						=			np.abs(to_np(_lowercase				) - to_np(_lowercase				)				).max()
    self.assertLess(_lowercase		,	1E-4				)
from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def    lowerCAmelCase_  (		lowercase:				str = ""		)     ->					dict[str, float]:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
 _UpperCamelCase:  Union[str, Any]    						=			BeautifulSoup(requests.get(lowercase		).text	,       '''html.parser'''		)
 _UpperCamelCase:  List[Any]    						=			soup.find_all('''td'''	,       attrs='''titleColumn'''		)
 _UpperCamelCase:  str    						=			soup.find_all('''td'''	,       class_='''ratingColumn imdbRating'''		)
 return {
     title.a.text: float(rating.strong.text		)
     for title, rating in zip(lowercase	,       lowercase		)
 }




def    lowerCAmelCase_  (		lowercase:				str = "IMDb_Top_250_Movies.csv"		)     ->					None:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			get_imdb_top_aaa_movies()
 with open(lowercase	,       '''w'''	,       newline=''''''		) as out_file:
  _UpperCamelCase:  Optional[Any]    						=			csv.writer(lowercase		)
  writer.writerow(['''Movie title''', '''IMDb rating''']		)
  for title, rating in movies.items():
   writer.writerow([title, rating]		)


if __name__ == "__main__":
  write_movies()