



"""simple docstring"""






import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__snake_case							:     List[str]   =   {'LayoutLMv2Config', 'LayoutLMv3Config'}



@is_pipeline_test
class     A__     (						unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
	SCREAMING_SNAKE_CASE					   =			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

	if model_mapping is not None:
		SCREAMING_SNAKE_CASE					   =			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
	if tf_model_mapping is not None:
		SCREAMING_SNAKE_CASE					   =			{
		    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
		}

	@require_torch
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= pipeline(
				    task="text-classification" ,    model="hf-internal-testing/tiny-random-distilbert" ,    framework="pt")

				__lowerCAmelCase   :						List[str]				= text_classifier("This is great !")
				self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE) ,    [{"label": "LABEL_0", "score": 0.504}])

				__lowerCAmelCase   :						List[Any]				= text_classifier("This is great !" ,    top_k=2)
				self.assertEqual(
				    nested_simplify(_SCREAMING_SNAKE_CASE) ,    [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}])

				__lowerCAmelCase   :						Optional[int]				= text_classifier(["This is great !", "This is bad"] ,    top_k=2)
				self.assertEqual(
				    nested_simplify(_SCREAMING_SNAKE_CASE) ,    [
				        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
				        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
				    ] ,    )

				__lowerCAmelCase   :						Union[str, Any]				= text_classifier("This is great !" ,    top_k=1)
				self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE) ,    [{"label": "LABEL_0", "score": 0.504}])

				# Legacy behavior
				__lowerCAmelCase   :						Optional[int]				= text_classifier("This is great !" ,    return_all_scores=_SCREAMING_SNAKE_CASE)
				self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE) ,    [{"label": "LABEL_0", "score": 0.504}])

				__lowerCAmelCase   :						str				= text_classifier("This is great !" ,    return_all_scores=_SCREAMING_SNAKE_CASE)
				self.assertEqual(
				    nested_simplify(_SCREAMING_SNAKE_CASE) ,    [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]])

				__lowerCAmelCase   :						str				= text_classifier(["This is great !", "Something else"] ,    return_all_scores=_SCREAMING_SNAKE_CASE)
				self.assertEqual(
				    nested_simplify(_SCREAMING_SNAKE_CASE) ,    [
				        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
				        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
				    ] ,    )

				__lowerCAmelCase   :						int				= text_classifier(["This is great !", "Something else"] ,    return_all_scores=_SCREAMING_SNAKE_CASE)
				self.assertEqual(
				    nested_simplify(_SCREAMING_SNAKE_CASE) ,    [
				        {"label": "LABEL_0", "score": 0.504},
				        {"label": "LABEL_0", "score": 0.504},
				    ] ,    )

	@require_torch
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> List[Any]:

				"""simple docstring"""

				import torch

				__lowerCAmelCase   :						int				= pipeline(
				    task="text-classification" ,    model="hf-internal-testing/tiny-random-distilbert" ,    framework="pt" ,    device=torch.device("cpu") ,    )

				__lowerCAmelCase   :						Tuple				= text_classifier("This is great !")
				self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE) ,    [{"label": "LABEL_0", "score": 0.504}])

	@require_tf
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= pipeline(
				    task="text-classification" ,    model="hf-internal-testing/tiny-random-distilbert" ,    framework="tf")

				__lowerCAmelCase   :						Union[str, Any]				= text_classifier("This is great !")
				self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE) ,    [{"label": "LABEL_0", "score": 0.504}])

	@slow
	@require_torch
	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= pipeline("text-classification")

				__lowerCAmelCase   :						List[str]				= text_classifier("This is great !")
				self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE) ,    [{"label": "POSITIVE", "score": 1.0}])
				__lowerCAmelCase   :						int				= text_classifier("This is bad !")
				self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE) ,    [{"label": "NEGATIVE", "score": 1.0}])
				__lowerCAmelCase   :						str				= text_classifier("Birds are a type of animal")
				self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE) ,    [{"label": "POSITIVE", "score": 0.988}])

	@slow
	@require_tf
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= pipeline("text-classification" ,    framework="tf")

				__lowerCAmelCase   :						str				= text_classifier("This is great !")
				self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE) ,    [{"label": "POSITIVE", "score": 1.0}])
				__lowerCAmelCase   :						int				= text_classifier("This is bad !")
				self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE) ,    [{"label": "NEGATIVE", "score": 1.0}])
				__lowerCAmelCase   :						str				= text_classifier("Birds are a type of animal")
				self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE) ,    [{"label": "POSITIVE", "score": 0.988}])

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	int)       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= TextClassificationPipeline(model=_SCREAMING_SNAKE_CASE ,    tokenizer=_SCREAMING_SNAKE_CASE)
				return text_classifier, ["HuggingFace is in", "This is another test"]







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

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= text_classifier.model
				# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
				__lowerCAmelCase   :						Optional[int]				= "HuggingFace is in"
				__lowerCAmelCase   :						Any				= text_classifier(_SCREAMING_SNAKE_CASE)

				self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE) ,    [{"label": ANY(_SCREAMING_SNAKE_CASE), "score": ANY(_SCREAMING_SNAKE_CASE)}])
				self.assertTrue(outputs[0]["label"] in model.config.idalabel.values())

				__lowerCAmelCase   :						Optional[Any]				= ["HuggingFace is in ", "Paris is in France"]
				__lowerCAmelCase   :						Optional[Any]				= text_classifier(_SCREAMING_SNAKE_CASE)
				self.assertEqual(
				    nested_simplify(_SCREAMING_SNAKE_CASE) ,    [{"label": ANY(_SCREAMING_SNAKE_CASE), "score": ANY(_SCREAMING_SNAKE_CASE)}, {"label": ANY(_SCREAMING_SNAKE_CASE), "score": ANY(_SCREAMING_SNAKE_CASE)}] ,    )
				self.assertTrue(outputs[0]["label"] in model.config.idalabel.values())
				self.assertTrue(outputs[1]["label"] in model.config.idalabel.values())

				# Forcing to get all results with `top_k=None`
				# This is NOT the legacy format
				__lowerCAmelCase   :						Any				= text_classifier(_SCREAMING_SNAKE_CASE ,    top_k=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Optional[Any]				= len(model.config.idalabel.values())
				self.assertEqual(
				    nested_simplify(_SCREAMING_SNAKE_CASE) ,    [[{"label": ANY(_SCREAMING_SNAKE_CASE), "score": ANY(_SCREAMING_SNAKE_CASE)}] * N, [{"label": ANY(_SCREAMING_SNAKE_CASE), "score": ANY(_SCREAMING_SNAKE_CASE)}] * N] ,    )

				__lowerCAmelCase   :						Union[str, Any]				= {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
				__lowerCAmelCase   :						Any				= text_classifier(_SCREAMING_SNAKE_CASE)
				self.assertEqual(
				    nested_simplify(_SCREAMING_SNAKE_CASE) ,    {"label": ANY(_SCREAMING_SNAKE_CASE), "score": ANY(_SCREAMING_SNAKE_CASE)} ,    )
				self.assertTrue(outputs["label"] in model.config.idalabel.values())

				# This might be used a text pair, but tokenizer + pipe interaction
				# makes it hard to understand that it's not using the pair properly
				# https://github.com/huggingface/transformers/issues/17305
				# We disabled this usage instead as it was outputting wrong outputs.
				__lowerCAmelCase   :						List[Any]				= [["HuggingFace is in ", "Paris is in France"]]
				with self.assertRaises(_SCREAMING_SNAKE_CASE):
							text_classifier(_SCREAMING_SNAKE_CASE)

				# This used to be valid for doing text pairs
				# We're keeping it working because of backward compatibility
				__lowerCAmelCase   :						Union[str, Any]				= text_classifier([[["HuggingFace is in ", "Paris is in France"]]])
				self.assertEqual(
				    nested_simplify(_SCREAMING_SNAKE_CASE) ,    [{"label": ANY(_SCREAMING_SNAKE_CASE), "score": ANY(_SCREAMING_SNAKE_CASE)}] ,    )
				self.assertTrue(outputs[0]["label"] in model.config.idalabel.values())




"""simple docstring"""






import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)


def     _lowercase       (						__snake_case			)		->     int:
			if hor == 128:
						__lowerCAmelCase   :						str				= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase   :						int				= (32, 128, 256)
						__lowerCAmelCase   :						Optional[Any]				= ("UpResnetBlock1D", "UpResnetBlock1D")

			elif hor == 32:
						__lowerCAmelCase   :						List[str]				= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase   :						Optional[Any]				= (32, 64, 128, 256)
						__lowerCAmelCase   :						Any				= ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
			__lowerCAmelCase   :						Union[str, Any]				= torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""			)
			__lowerCAmelCase   :						List[Any]				= model.state_dict()
			__lowerCAmelCase   :						Optional[Any]				= {
			    "down_block_types": down_block_types,
			    "block_out_channels": block_out_channels,
			    "up_block_types": up_block_types,
			    "layers_per_block": 1,
			    "use_timestep_embedding": True,
			    "out_block_type": "OutConv1DBlock",
			    "norm_num_groups": 8,
			    "downsample_each_block": False,
			    "in_channels": 14,
			    "out_channels": 14,
			    "extra_in_channels": 0,
			    "time_embedding_type": "positional",
			    "flip_sin_to_cos": False,
			    "freq_shift": 1,
			    "sample_size": 65_536,
			    "mid_block_type": "MidResTemporalBlock1D",
			    "act_fn": "mish",
			}
			__lowerCAmelCase   :						Dict				= UNetaDModel(**__snake_case			)
			print(F"""length of state dict: {len(state_dict.keys()			)}"""			)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()			)}"""			)
			__lowerCAmelCase   :						Dict				= dict(zip(model.state_dict().keys()      ,hf_value_function.state_dict().keys()			)			)
			for k, v in mapping.items():
						__lowerCAmelCase   :						int				= state_dict.pop(__snake_case			)
			hf_value_function.load_state_dict(__snake_case			)

			torch.save(hf_value_function.state_dict()      ,F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""			)
			with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""      ,"w"			) as f:
						json.dump(__snake_case      ,__snake_case			)


def     _lowercase       (						)		->     List[str]:
			__lowerCAmelCase   :						Union[str, Any]				= {
			    "in_channels": 14,
			    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
			    "up_block_types": (),
			    "out_block_type": "ValueFunction",
			    "mid_block_type": "ValueFunctionMidBlock1D",
			    "block_out_channels": (32, 64, 128, 256),
			    "layers_per_block": 1,
			    "downsample_each_block": True,
			    "sample_size": 65_536,
			    "out_channels": 14,
			    "extra_in_channels": 0,
			    "time_embedding_type": "positional",
			    "use_timestep_embedding": True,
			    "flip_sin_to_cos": False,
			    "freq_shift": 1,
			    "norm_num_groups": 8,
			    "act_fn": "mish",
			}

			__lowerCAmelCase   :						int				= torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"			)
			__lowerCAmelCase   :						Any				= model
			__lowerCAmelCase   :						Optional[int]				= UNetaDModel(**__snake_case			)
			print(F"""length of state dict: {len(state_dict.keys()			)}"""			)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()			)}"""			)

			__lowerCAmelCase   :						Any				= dict(zip(state_dict.keys()      ,hf_value_function.state_dict().keys()			)			)
			for k, v in mapping.items():
						__lowerCAmelCase   :						Union[str, Any]				= state_dict.pop(__snake_case			)

			hf_value_function.load_state_dict(__snake_case			)

			torch.save(hf_value_function.state_dict()      ,"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"			)
			with open("hub/hopper-medium-v2/value_function/config.json"      ,"w"			) as f:
						json.dump(__snake_case      ,__snake_case			)


if __name__ == "__main__":
	unet(32)
	# unet(128)
	value_function()