


import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class lowerCAmelCase_		(					UpperCAmelCase_   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    KandinskyVaaPriorEmbaEmbPipeline
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    [
     "num_images_per_prompt",
     "strength",
     "generator",
     "num_inference_steps",
     "latents",
     "negative_prompt",
     "guidance_scale",
     "output_type",
     "return_dict",
 ]
 __snake_case				=    False



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim * 4



 @property
 def 		UpperCamelCase__	(      self   ):
       return 1_00



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
       return tokenizer



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= CLIPTextConfig(
           bos_token_id=0						, eos_token_id=2						, hidden_size=self.text_embedder_hidden_size						, projection_dim=self.text_embedder_hidden_size						, intermediate_size=37						, layer_norm_eps=1E-05						, num_attention_heads=4						, num_hidden_layers=5						, pad_token_id=1						, vocab_size=10_00						, )
       return CLIPTextModelWithProjection(__UpperCamelCase   )



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )

       snake_case_			= {
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 12,
           '''embedding_dim''': self.text_embedder_hidden_size,
           '''num_layers''': 1,
       }

       snake_case_			= PriorTransformer(**__UpperCamelCase   )
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       snake_case_			= nn.Parameter(torch.ones(model.clip_std.shape   )   )
       return model



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size						, image_size=2_24						, projection_dim=self.text_embedder_hidden_size						, intermediate_size=37						, num_attention_heads=4						, num_channels=3						, num_hidden_layers=5						, patch_size=14						, )

       snake_case_			= CLIPVisionModelWithProjection(__UpperCamelCase   )
       return model



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= CLIPImageProcessor(
           crop_size=2_24						, do_center_crop=__UpperCamelCase						, do_normalize=__UpperCamelCase						, do_resize=__UpperCamelCase						, image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]						, image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]						, resample=3						, size=2_24						, )

       return image_processor



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.dummy_prior
       snake_case_			= self.dummy_image_encoder
       snake_case_			= self.dummy_text_encoder
       snake_case_			= self.dummy_tokenizer
       snake_case_			= self.dummy_image_processor

       snake_case_			= UnCLIPScheduler(
           variance_type='''fixed_small_log'''						, prediction_type='''sample'''						, num_train_timesteps=10_00						, clip_sample=__UpperCamelCase						, clip_sample_range=10.0						, )

       snake_case_			= {
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''scheduler''': scheduler,
           '''image_processor''': image_processor,
       }

       return components



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(__UpperCamelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(__UpperCamelCase   )
       else:
             snake_case_			= torch.Generator(device=__UpperCamelCase   ).manual_seed(__UpperCamelCase   )

       snake_case_			= floats_tensor((1, 3, 64, 64)						, rng=random.Random(__UpperCamelCase   )   ).to(__UpperCamelCase   )
       snake_case_			= image.cpu().permute(0						, 2						, 3						, 1   )[0]
       snake_case_			= Image.fromarray(np.uinta(__UpperCamelCase   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )

       snake_case_			= {
           '''prompt''': '''horse''',
           '''image''': init_image,
           '''strength''': 0.5,
           '''generator''': generator,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= '''cpu'''

       snake_case_			= self.get_dummy_components()

       snake_case_			= self.pipeline_class(**__UpperCamelCase   )
       snake_case_			= pipe.to(__UpperCamelCase   )

       pipe.set_progress_bar_config(disable=__UpperCamelCase   )

       snake_case_			= pipe(**self.get_dummy_inputs(__UpperCamelCase   )   )
       snake_case_			= output.image_embeds

       snake_case_			= pipe(
           **self.get_dummy_inputs(__UpperCamelCase   )						, return_dict=__UpperCamelCase						, )[0]

       snake_case_			= image[0, -10:]
       snake_case_			= image_from_tuple[0, -10:]

       assert image.shape == (1, 32)

       snake_case_			= np.array(
           [
               0.1_071_284,
               1.3_330_271,
               0.61_260_223,
               -0.6_691_065,
               -0.3_846_852,
               -1.0_303_661,
               0.22_716_111,
               0.03_348_901,
               0.30_040_675,
               -0.24_805_029,
           ]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2



 @skip_mps
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= True
       snake_case_			= False

       self._test_inference_batch_single_identical(
           test_max_difference=__UpperCamelCase						, relax_max_difference=__UpperCamelCase						, test_mean_pixel_difference=__UpperCamelCase						, )





 @skip_mps
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= False

       self._test_attention_slicing_forward_pass(
           test_max_difference=__UpperCamelCase						, test_mean_pixel_difference=__UpperCamelCase						, )





'''simple docstring'''







import subprocess
from argparse import ArgumentParser
from typing import List, Union

from huggingface_hub.hf_api import HfFolder, create_repo, whoami
from requests.exceptions import HTTPError

from . import BaseTransformersCLICommand





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					@staticmethod
					def 							__SCREAMING_SNAKE_CASE      (       __UpperCamelCase					):
							_snake_case      			=			parser.add_parser("login",    help="Log in using the same credentials as on huggingface.co"					)
							login_parser.set_defaults(func=lambda __UpperCamelCase					: LoginCommand(__UpperCamelCase					)					)
							_snake_case      			=			parser.add_parser("whoami",    help="Find out which huggingface.co account you are logged in as."					)
							whoami_parser.set_defaults(func=lambda __UpperCamelCase					: WhoamiCommand(__UpperCamelCase					)					)
							_snake_case      			=			parser.add_parser("logout",    help="Log out"					)
							logout_parser.set_defaults(func=lambda __UpperCamelCase					: LogoutCommand(__UpperCamelCase					)					)

							# new system: git-based repo system
							_snake_case      			=			parser.add_parser(
							    "repo",    help="Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos.",    )
							_snake_case      			=			repo_parser.add_subparsers(
							    help="Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands"					)
							_snake_case      			=			repo_subparsers.add_parser(
							    "create",    help="Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co"					)
							repo_create_parser.add_argument(
							    "name",    type=__UpperCamelCase,    help="Name for your model's repo. Will be namespaced under your username to build the model id.",    )
							repo_create_parser.add_argument("--organization",    type=__UpperCamelCase,    help="Optional: organization namespace."					)
							repo_create_parser.add_argument("-y",    "--yes",    action="store_true",    help="Optional: answer Yes to the prompt"					)
							repo_create_parser.set_defaults(func=lambda __UpperCamelCase					: RepoCreateCommand(__UpperCamelCase					)					)





class 	lowerCamelCase_				:




					'''simple docstring'''






					lowercase__       =					"""\u001b[1m"""
					lowercase__       =					"""\u001b[31m"""
					lowercase__       =					"""\u001b[90m"""
					lowercase__       =					"""\u001b[0m"""






					@classmethod
					def 							__SCREAMING_SNAKE_CASE      (       cls,    __UpperCamelCase					):
							return f'''{cls._bold}{s}{cls._reset}'''






					@classmethod
					def 							__SCREAMING_SNAKE_CASE      (       cls,    __UpperCamelCase					):
							return f'''{cls._bold}{cls._red}{s}{cls._reset}'''






					@classmethod
					def 							__SCREAMING_SNAKE_CASE      (       cls,    __UpperCamelCase					):
							return f'''{cls._gray}{s}{cls._reset}'''






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			[max(len(str(lowercase_    )    ) for x in col    ) for col in zip(*lowercase_		,				lowercase_    )]
		_snake_case      			=			("{{:{}}} " * len(lowercase_    )).format(*lowercase_    )
		_snake_case      			=			[]
		lines.append(row_format.format(*lowercase_    )    )
		lines.append(row_format.format(*["-" * w for w in col_widths]    )    )
		for row in rows:
				lines.append(row_format.format(*lowercase_    )    )
		return "\n".join(lowercase_    )





class 	lowerCamelCase_				:




					'''simple docstring'''






					def __init__(       self,    __UpperCamelCase					):
							_snake_case      			=			args





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							print(
							    ANSI.red(
							        "ERROR! `huggingface-cli login` uses an outdated login mechanism "
							        "that is not compatible with the Hugging Face Hub backend anymore. "
							        "Please use `huggingface-cli login instead."					)					)





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							print(
							    ANSI.red(
							        "WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use "
							        "`huggingface-cli whoami` instead."					)					)
							_snake_case      			=			HfFolder.get_token()
							if token is None:
									print("Not logged in"					)
									exit()
							try:
									_snake_case			,       _snake_case      			=			whoami(__UpperCamelCase					)
									print(__UpperCamelCase					)
									if orgs:
											print(ANSI.bold("orgs: "					),    ",".join(__UpperCamelCase					)					)
							except HTTPError as e:
									print(__UpperCamelCase					)
									print(ANSI.red(e.response.text					)					)
									exit(1					)





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							print(
							    ANSI.red(
							        "ERROR! `transformers-cli logout` uses an outdated logout mechanism "
							        "that is not compatible with the Hugging Face Hub backend anymore. "
							        "Please use `huggingface-cli logout instead."					)					)





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							print(
							    ANSI.red(
							        "WARNING! Managing repositories through transformers-cli is deprecated. "
							        "Please use `huggingface-cli` instead."					)					)
							_snake_case      			=			HfFolder.get_token()
							if token is None:
									print("Not logged in"					)
									exit(1					)
							try:
									_snake_case      			=			subprocess.check_output(["git", "--version"]					).decode("utf-8"					)
									print(ANSI.gray(stdout.strip()					)					)
							except FileNotFoundError:
									print("Looks like you do not have git installed, please install."					)

							try:
									_snake_case      			=			subprocess.check_output(["git-lfs", "--version"]					).decode("utf-8"					)
									print(ANSI.gray(stdout.strip()					)					)
							except FileNotFoundError:
									print(
									    ANSI.red(
									        "Looks like you do not have git-lfs installed, please install."
									        " You can install from https://git-lfs.github.com/."
									        " Then run `git lfs install` (you only have to do this once)."					)					)
							print(""					)

							_snake_case			,       _snake_case      			=			whoami(__UpperCamelCase					)
							_snake_case      			=			self.args.organization if self.args.organization is not None else user
							_snake_case      			=			f'''{namespace}/{self.args.name}'''
							print(f'''You are about to create {ANSI.bold(__UpperCamelCase					)}'''					)

							if not self.args.yes:
									_snake_case      			=			input("Proceed? [Y/n] "					).lower()
									if not (choice == "" or choice == "y" or choice == "yes"):
											print("Abort"					)
											exit()
							try:
									_snake_case      			=			create_repo(__UpperCamelCase,    name=self.args.name,    organization=self.args.organization					)
							except HTTPError as e:
									print(__UpperCamelCase					)
									print(ANSI.red(e.response.text					)					)
									exit(1					)
							print("\nYour repo now lives at:"					)
							print(f'''  {ANSI.bold(__UpperCamelCase					)}'''					)
							print("\nYou can clone it locally with the command below, and commit/push as usual."					)
							print(f'''\n  git clone {url}'''					)
							print(""					)





