


import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 							A					( snake_case_				,						unittest.TestCase							):
					'''simple docstring'''






					A__    =				KandinskyPipeline
					A__    =				[
					    '''prompt''',
					    '''image_embeds''',
					    '''negative_image_embeds''',
					]
					A__    =				['''prompt''', '''negative_prompt''', '''image_embeds''', '''negative_image_embeds''']
					A__    =				[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''latents''',
					    '''guidance_scale''',
					    '''negative_prompt''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					A__    =				False

					@property
					def 						lowerCamelCase__  (self					:		Optional[int]	)		->     List[str]:




							"""simple docstring"""


							return 32

					@property
					def 						lowerCamelCase__  (self					:		Tuple	)		->     List[str]:




							"""simple docstring"""


							return 32

					@property
					def 						lowerCamelCase__  (self					:		Union[str, Any]	)		->     List[str]:




							"""simple docstring"""


							return self.time_input_dim

					@property
					def 						lowerCamelCase__  (self					:		List[str]	)		->     Union[str, Any]:




							"""simple docstring"""


							return self.time_input_dim * 4

					@property
					def 						lowerCamelCase__  (self					:		Tuple	)		->     int:




							"""simple docstring"""


							return 100

					@property
					def 						lowerCamelCase__  (self					:		Optional[Any]	)		->     int:




							"""simple docstring"""


							lowercase__      =	XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""	)
							return tokenizer

					@property
					def 						lowerCamelCase__  (self					:		Union[str, Any]	)		->     Tuple:




							"""simple docstring"""


							torch.manual_seed(0	)
							lowercase__      =	MCLIPConfig(
							    numDims=self.cross_attention_dim   ,      transformerDimensions=self.text_embedder_hidden_size   ,      hidden_size=self.text_embedder_hidden_size   ,      intermediate_size=37   ,      num_attention_heads=4   ,      num_hidden_layers=5   ,      vocab_size=1005   ,      )

							lowercase__      =	MultilingualCLIP(_UpperCAmelCase	)
							lowercase__      =	text_encoder.eval()

							return text_encoder

					@property
					def 						lowerCamelCase__  (self					:		Optional[int]	)		->     Any:




							"""simple docstring"""


							torch.manual_seed(0	)

							lowercase__      =	{
							    """in_channels""": 4,
							    # Out channels is double in channels because predicts mean and variance
							    """out_channels""": 8,
							    """addition_embed_type""": """text_image""",
							    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
							    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
							    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
							    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
							    """layers_per_block""": 1,
							    """encoder_hid_dim""": self.text_embedder_hidden_size,
							    """encoder_hid_dim_type""": """text_image_proj""",
							    """cross_attention_dim""": self.cross_attention_dim,
							    """attention_head_dim""": 4,
							    """resnet_time_scale_shift""": """scale_shift""",
							    """class_embed_type""": None,
							}

							lowercase__      =	UNetaDConditionModel(**_UpperCAmelCase	)
							return model

					@property
					def 						lowerCamelCase__  (self					:		Optional[int]	)		->     Any:




							"""simple docstring"""


							return {
							    "block_out_channels": [32, 64],
							    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}

					@property
					def 						lowerCamelCase__  (self					:		Union[str, Any]	)		->     Dict:




							"""simple docstring"""


							torch.manual_seed(0	)
							lowercase__      =	VQModel(**self.dummy_movq_kwargs	)
							return model

					def 						lowerCamelCase__  (self					:		int	)		->     Union[str, Any]:




							"""simple docstring"""


							lowercase__      =	self.dummy_text_encoder
							lowercase__      =	self.dummy_tokenizer
							lowercase__      =	self.dummy_unet
							lowercase__      =	self.dummy_movq

							lowercase__      =	DDIMScheduler(
							    num_train_timesteps=1000   ,      beta_schedule="""linear"""   ,      beta_start=0.00_085   ,      beta_end=0.012   ,      clip_sample=_UpperCAmelCase   ,      set_alpha_to_one=_UpperCAmelCase   ,      steps_offset=1   ,      prediction_type="""epsilon"""   ,      thresholding=_UpperCAmelCase   ,      )

							lowercase__      =	{
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}
							return components

					def 						lowerCamelCase__  (self					:		int   ,      _UpperCAmelCase					:		Dict   ,      _UpperCAmelCase					:		Any=0	)		->     List[str]:




							"""simple docstring"""


							lowercase__      =	floats_tensor((1, self.cross_attention_dim)   ,      rng=random.Random(_UpperCAmelCase	)	).to(_UpperCAmelCase	)
							lowercase__      =	floats_tensor((1, self.cross_attention_dim)   ,      rng=random.Random(seed + 1	)	).to(_UpperCAmelCase	)
							if str(_UpperCAmelCase	).startswith("""mps"""	):
									lowercase__      =	torch.manual_seed(_UpperCAmelCase	)
							else:
									lowercase__      =	torch.Generator(device=_UpperCAmelCase	).manual_seed(_UpperCAmelCase	)
							lowercase__      =	{
							    """prompt""": """horse""",
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """guidance_scale""": 4.0,
							    """num_inference_steps""": 2,
							    """output_type""": """np""",
							}
							return inputs


					def 						lowerCamelCase__  (self					:		List[str]	)		->     Any:




							"""simple docstring"""


							lowercase__      =	"""cpu"""

							lowercase__      =	self.get_dummy_components()

							lowercase__      =	self.pipeline_class(**_UpperCAmelCase	)
							lowercase__      =	pipe.to(_UpperCAmelCase	)

							pipe.set_progress_bar_config(disable=_UpperCAmelCase	)

							lowercase__      =	pipe(**self.get_dummy_inputs(_UpperCAmelCase	)	)
							lowercase__      =	output.images

							lowercase__      =	pipe(
							    **self.get_dummy_inputs(_UpperCAmelCase	)   ,      return_dict=_UpperCAmelCase   ,      )[0]

							lowercase__      =	image[0, -3:, -3:, -1]
							lowercase__      =	image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							lowercase__      =	np.array(
							    [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]	)

							assert (
							    np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
							), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
							), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class 							A					( unittest.TestCase							):
					'''simple docstring'''






					def 						lowerCamelCase__  (self					:		List[str]	)		->     List[str]:




							"""simple docstring"""


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


					def 						lowerCamelCase__  (self					:		int	)		->     Optional[int]:




							"""simple docstring"""


							lowercase__      =	load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""	)

							lowercase__      =	KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""   ,      torch_dtype=torch.floataa	)
							pipe_prior.to(_UpperCAmelCase	)

							lowercase__      =	KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""   ,      torch_dtype=torch.floataa	)
							lowercase__      =	pipeline.to(_UpperCAmelCase	)
							pipeline.set_progress_bar_config(disable=_UpperCAmelCase	)

							lowercase__      =	"""red cat, 4k photo"""

							lowercase__      =	torch.Generator(device="""cuda"""	).manual_seed(0	)
							lowercase__    ,			lowercase__      =	pipe_prior(
							    _UpperCAmelCase   ,      generator=_UpperCAmelCase   ,      num_inference_steps=5   ,      negative_prompt=""""""   ,      ).to_tuple()

							lowercase__      =	torch.Generator(device="""cuda"""	).manual_seed(0	)
							lowercase__      =	pipeline(
							    _UpperCAmelCase   ,      image_embeds=_UpperCAmelCase   ,      negative_image_embeds=_UpperCAmelCase   ,      generator=_UpperCAmelCase   ,      num_inference_steps=100   ,      output_type="""np"""   ,      )

							lowercase__      =	output.images[0]

							assert image.shape == (512, 512, 3)

							assert_mean_pixel_difference(_UpperCAmelCase   ,      _UpperCAmelCase	)





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict



class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					pass






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							Dict[str, Callable]					,					__magic_name__  :							List[Any]=None					,					__magic_name__  :							int=False  )     ->						int:
		"""simple docstring"""




		try:
				lowercase__      =	ast.parse(__magic_name__  )
		except SyntaxError as e:
				print("""The code generated by the agent is not valid.\n"""					,					__magic_name__  )
				return
		if state is None:
				lowercase__      =	{}
		lowercase__      =	None
		for idx, node in enumerate(expression.body  ):
				try:
						lowercase__      =	evaluate_ast(__magic_name__					,					__magic_name__					,					__magic_name__  )
				except InterpretorError as e:
						lowercase__      =	f'''Evaluation of the code stopped at line {idx} before the end because of the following error'''
						if chat_mode:
								msg += (
								    f'''. Copy paste the following error message and send it back to the agent:\nI get an error: \'{e}\''''
								)
						else:
								msg += f''':\n{e}'''
						print(__magic_name__  )
						break
				if line_result is not None:
						lowercase__      =	line_result

		return result






def 				UpperCamelCase      (			__magic_name__  :							ast.AST					,					__magic_name__  :							Dict[str, Any]					,					__magic_name__  :							Dict[str, Callable]  )     ->						Tuple:
		"""simple docstring"""




		if isinstance(__magic_name__					,					ast.Assign  ):
				# Assignement -> we evaluate the assignement which should update the state
				# We return the variable assigned as it may be used to determine the final result.
				return evaluate_assign(__magic_name__					,					__magic_name__					,					__magic_name__  )
		elif isinstance(__magic_name__					,					ast.Call  ):
				# Function call -> we return the value of the function call
				return evaluate_call(__magic_name__					,					__magic_name__					,					__magic_name__  )
		elif isinstance(__magic_name__					,					ast.Constant  ):
				# Constant -> just return the value
				return expression.value
		elif isinstance(__magic_name__					,					ast.Dict  ):
				# Dict -> evaluate all keys and values
				lowercase__      =	[evaluate_ast(__magic_name__					,					__magic_name__					,					__magic_name__  ) for k in expression.keys]
				lowercase__      =	[evaluate_ast(__magic_name__					,					__magic_name__					,					__magic_name__  ) for v in expression.values]
				return dict(zip(__magic_name__					,					__magic_name__  )  )
		elif isinstance(__magic_name__					,					ast.Expr  ):
				# Expression -> evaluate the content
				return evaluate_ast(expression.value					,					__magic_name__					,					__magic_name__  )
		elif isinstance(__magic_name__					,					ast.For  ):
				# For loop -> execute the loop
				return evaluate_for(__magic_name__					,					__magic_name__					,					__magic_name__  )
		elif isinstance(__magic_name__					,					ast.FormattedValue  ):
				# Formatted value (part of f-string) -> evaluate the content and return
				return evaluate_ast(expression.value					,					__magic_name__					,					__magic_name__  )
		elif isinstance(__magic_name__					,					ast.If  ):
				# If -> execute the right branch
				return evaluate_if(__magic_name__					,					__magic_name__					,					__magic_name__  )
		elif hasattr(__magic_name__					,					"""Index"""  ) and isinstance(__magic_name__					,					ast.Index  ):
				return evaluate_ast(expression.value					,					__magic_name__					,					__magic_name__  )
		elif isinstance(__magic_name__					,					ast.JoinedStr  ):
				return "".join([str(evaluate_ast(__magic_name__					,					__magic_name__					,					__magic_name__  )  ) for v in expression.values]  )
		elif isinstance(__magic_name__					,					ast.List  ):
				# List -> evaluate all elements
				return [evaluate_ast(__magic_name__					,					__magic_name__					,					__magic_name__  ) for elt in expression.elts]
		elif isinstance(__magic_name__					,					ast.Name  ):
				# Name -> pick up the value in the state
				return evaluate_name(__magic_name__					,					__magic_name__					,					__magic_name__  )
		elif isinstance(__magic_name__					,					ast.Subscript  ):
				# Subscript -> return the value of the indexing
				return evaluate_subscript(__magic_name__					,					__magic_name__					,					__magic_name__  )
		else:
				# For now we refuse anything else. Let's add things as we need them.
				raise InterpretorError(f'''{expression.__class__.__name__} is not supported.'''  )






def 				UpperCamelCase      (			__magic_name__  :							Any					,					__magic_name__  :							Union[str, Any]					,					__magic_name__  :							Dict  )     ->						str:
		"""simple docstring"""




		lowercase__      =	assign.targets
		lowercase__      =	evaluate_ast(assign.value					,					__magic_name__					,					__magic_name__  )

		if len(__magic_name__  ) == 1:
				lowercase__      =	result
		else:
				if len(__magic_name__  ) != len(__magic_name__  ):
						raise InterpretorError(f'''Expected {len(__magic_name__  )} values but got {len(__magic_name__  )}.'''  )
				for var_name, r in zip(__magic_name__					,					__magic_name__  ):
						lowercase__      =	r
		return result






def 				UpperCamelCase      (			__magic_name__  :							Any					,					__magic_name__  :							Dict					,					__magic_name__  :							Union[str, Any]  )     ->						int:
		"""simple docstring"""




		if not isinstance(call.func					,					ast.Name  ):
				raise InterpretorError(
				    f'''It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of '''
				    f'''type {type(call.func  )}.'''  )
		lowercase__      =	call.func.id
		if func_name not in tools:
				raise InterpretorError(
				    f'''It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id}).'''  )

		lowercase__      =	tools[func_name]
		# Todo deal with args
		lowercase__      =	[evaluate_ast(__magic_name__					,					__magic_name__					,					__magic_name__  ) for arg in call.args]
		lowercase__      =	{keyword.arg: evaluate_ast(keyword.value					,					__magic_name__					,					__magic_name__  ) for keyword in call.keywords}
		return func(*__magic_name__					,					**__magic_name__  )






def 				UpperCamelCase      (			__magic_name__  :							Tuple					,					__magic_name__  :							Any					,					__magic_name__  :							Optional[Any]  )     ->						List[Any]:
		"""simple docstring"""




		lowercase__      =	evaluate_ast(subscript.slice					,					__magic_name__					,					__magic_name__  )
		lowercase__      =	evaluate_ast(subscript.value					,					__magic_name__					,					__magic_name__  )
		if isinstance(__magic_name__					,					(list, tuple)  ):
				return value[int(__magic_name__  )]
		if index in value:
				return value[index]
		if isinstance(__magic_name__					,					__magic_name__  ) and isinstance(__magic_name__					,					__magic_name__  ):
				lowercase__      =	difflib.get_close_matches(__magic_name__					,					list(value.keys()  )  )
				if len(__magic_name__  ) > 0:
						return value[close_matches[0]]

		raise InterpretorError(f'''Could not index {value} with \'{index}\'.'''  )






def 				UpperCamelCase      (			__magic_name__  :							Dict					,					__magic_name__  :							Dict					,					__magic_name__  :							int  )     ->						int:
		"""simple docstring"""




		if name.id in state:
				return state[name.id]
		lowercase__      =	difflib.get_close_matches(name.id					,					list(state.keys()  )  )
		if len(__magic_name__  ) > 0:
				return state[close_matches[0]]
		raise InterpretorError(f'''The variable `{name.id}` is not defined.'''  )






def 				UpperCamelCase      (			__magic_name__  :							Union[str, Any]					,					__magic_name__  :							List[str]					,					__magic_name__  :							Tuple  )     ->						List[str]:
		"""simple docstring"""




		if len(condition.ops  ) > 1:
				raise InterpretorError("""Cannot evaluate conditions with multiple operators"""  )

		lowercase__      =	evaluate_ast(condition.left					,					__magic_name__					,					__magic_name__  )
		lowercase__      =	condition.ops[0]
		lowercase__      =	evaluate_ast(condition.comparators[0]					,					__magic_name__					,					__magic_name__  )

		if isinstance(__magic_name__					,					ast.Eq  ):
				return left == right
		elif isinstance(__magic_name__					,					ast.NotEq  ):
				return left != right
		elif isinstance(__magic_name__					,					ast.Lt  ):
				return left < right
		elif isinstance(__magic_name__					,					ast.LtE  ):
				return left <= right
		elif isinstance(__magic_name__					,					ast.Gt  ):
				return left > right
		elif isinstance(__magic_name__					,					ast.GtE  ):
				return left >= right
		elif isinstance(__magic_name__					,					ast.Is  ):
				return left is right
		elif isinstance(__magic_name__					,					ast.IsNot  ):
				return left is not right
		elif isinstance(__magic_name__					,					ast.In  ):
				return left in right
		elif isinstance(__magic_name__					,					ast.NotIn  ):
				return left not in right
		else:
				raise InterpretorError(f'''Operator not supported: {comparator}'''  )






def 				UpperCamelCase      (			__magic_name__  :							Union[str, Any]					,					__magic_name__  :							str					,					__magic_name__  :							Dict  )     ->						Optional[int]:
		"""simple docstring"""




		lowercase__      =	None
		if evaluate_condition(if_statement.test					,					__magic_name__					,					__magic_name__  ):
				for line in if_statement.body:
						lowercase__      =	evaluate_ast(__magic_name__					,					__magic_name__					,					__magic_name__  )
						if line_result is not None:
								lowercase__      =	line_result
		else:
				for line in if_statement.orelse:
						lowercase__      =	evaluate_ast(__magic_name__					,					__magic_name__					,					__magic_name__  )
						if line_result is not None:
								lowercase__      =	line_result
		return result







def 				UpperCamelCase      (			__magic_name__  :							Any					,					__magic_name__  :							List[Any]					,					__magic_name__  :							List[Any]  )     ->						List[str]:
		"""simple docstring"""




		lowercase__      =	None
		lowercase__      =	evaluate_ast(for_loop.iter					,					__magic_name__					,					__magic_name__  )
		for counter in iterator:
				lowercase__      =	counter
				for expression in for_loop.body:
						lowercase__      =	evaluate_ast(__magic_name__					,					__magic_name__					,					__magic_name__  )
						if line_result is not None:
								lowercase__      =	line_result
		return result

