

# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 					a__						:
			"""simple docstring"""







			__lowerCamelCase        =       42

			# setable values
			__lowerCamelCase        =       42
			__lowerCamelCase        =       42
			__lowerCamelCase        =       None


			@classmethod
			def      UpperCamelCase		(    cls      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Any:
							'''simple docstring'''






							return cls(common=lowercase      ,      init_noise_sigma=lowercase      ,      timesteps=lowercase       )

@dataclass
class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       42

class 					a__						(					snake_case							,	snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       [e.name for e in FlaxKarrasDiffusionSchedulers]

			__lowerCamelCase        =       42


			@property
			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							return True


			@register_to_config
			def __init__(    self      ,      lowercase = 1000      ,      lowercase = 0.0001      ,      lowercase = 0.02      ,      lowercase = "linear"      ,      lowercase = None      ,      lowercase = "fixed_small"      ,      lowercase = True      ,      lowercase = "epsilon"      ,      lowercase = jnp.floataa      ,      )  ->						int:
							'''simple docstring'''






							A__        =			dtype


			def      UpperCamelCase		(    self      ,      lowercase = None       )  ->						DDPMSchedulerState:
							'''simple docstring'''






							if common is None:
											A__        =			CommonSchedulerState.create(self       )

							# standard deviation of the initial noise distribution
							A__        =			jnp.array(1.0      ,      dtype=self.dtype       )

							A__        =			jnp.arange(0      ,      self.config.num_train_timesteps       ).round()[::-1]

							return DDPMSchedulerState.create(
							    common=lowercase      ,      init_noise_sigma=lowercase      ,      timesteps=lowercase      ,      )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = None       )  ->						jnp.ndarray:
							'''simple docstring'''






							return sample


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = ()       )  ->						DDPMSchedulerState:
							'''simple docstring'''






							A__        =			self.config.num_train_timesteps // num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# rounding to avoid issues when num_inference_step is power of 3
							A__        =			(jnp.arange(0      ,      lowercase       ) * step_ratio).round()[::-1]

							return state.replace(
							    num_inference_steps=lowercase      ,      timesteps=lowercase      ,      )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase=None      ,      lowercase=None       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			state.common.alphas_cumprod[t]
							A__        =			jnp.where(t > 0      ,      state.common.alphas_cumprod[t - 1]      ,      jnp.array(1.0      ,      dtype=self.dtype       )       )

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							A__        =			(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
											A__        =			self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small":
											A__        =			jnp.clip(lowercase      ,      a_min=1e-20       )
							# for rl-diffuser https://arxiv.org/abs/2205.09991
							elif variance_type == "fixed_small_log":
											A__        =			jnp.log(jnp.clip(lowercase      ,      a_min=1e-20       )       )
							elif variance_type == "fixed_large":
											A__        =			state.common.betas[t]
							elif variance_type == "fixed_large_log":
											# Glide max_log
											A__        =			jnp.log(state.common.betas[t]       )
							elif variance_type == "learned":
											return predicted_variance
							elif variance_type == "learned_range":
											A__        =			variance
											A__        =			state.common.betas[t]
											A__        =			(predicted_variance + 1) / 2
											A__        =			frac * max_log + (1 - frac) * min_log

							return variance


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      lowercase = True      ,      )  ->						Union[FlaxDDPMSchedulerOutput, Tuple]:
							'''simple docstring'''






							A__        =			timestep

							if key is None:
											A__        =			jax.random.PRNGKey(0       )

							if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
											A__    ,				A__        =			jnp.split(lowercase      ,      sample.shape[1]      ,      axis=1       )
							else:
											A__        =			None

							# 1. compute alphas, betas
							A__        =			state.common.alphas_cumprod[t]
							A__        =			jnp.where(t > 0      ,      state.common.alphas_cumprod[t - 1]      ,      jnp.array(1.0      ,      dtype=self.dtype       )       )
							A__        =			1 - alpha_prod_t
							A__        =			1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
											A__        =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
											A__        =			model_output
							elif self.config.prediction_type == "v_prediction":
											A__        =			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							else:
											raise ValueError(
											    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
											    " for the FlaxDDPMScheduler."       )

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
											A__        =			jnp.clip(lowercase      ,      -1      ,      1       )

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							A__        =			(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							A__        =			state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							A__        =			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							def random_variance():
											A__        =			jax.random.split(lowercase      ,      num=1       )
											A__        =			jax.random.normal(lowercase      ,      shape=model_output.shape      ,      dtype=self.dtype       )
											return (self._get_variance(lowercase      ,      lowercase      ,      predicted_variance=lowercase       ) ** 0.5) * noise

							A__        =			jnp.where(t > 0      ,      random_variance()      ,      jnp.zeros(model_output.shape      ,      dtype=self.dtype       )       )

							A__        =			pred_prev_sample + variance

							if not return_dict:
											return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=lowercase      ,      state=lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      )  ->						jnp.ndarray:
							'''simple docstring'''






							return add_noise_common(state.common      ,      lowercase      ,      lowercase      ,      lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      )  ->						jnp.ndarray:
							'''simple docstring'''






							return get_velocity_common(state.common      ,      lowercase      ,      lowercase      ,      lowercase       )


			def __len__(    self       )  ->						str:
							'''simple docstring'''






							return self.config.num_train_timesteps




from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
	from transformers import (
	    TFAutoModel,
	    TFAutoModelForCausalLM,
	    TFAutoModelForMaskedLM,
	    TFAutoModelForPreTraining,
	    TFAutoModelForQuestionAnswering,
	    TFAutoModelForSeqaSeqLM,
	    TFAutoModelForSequenceClassification,
	    TFAutoModelForTableQuestionAnswering,
	    TFAutoModelForTokenClassification,
	    TFAutoModelWithLMHead,
	    TFBertForMaskedLM,
	    TFBertForPreTraining,
	    TFBertForQuestionAnswering,
	    TFBertForSequenceClassification,
	    TFBertModel,
	    TFFunnelBaseModel,
	    TFFunnelModel,
	    TFGPTaLMHeadModel,
	    TFRobertaForMaskedLM,
	    TFTaForConditionalGeneration,
	    TFTapasForQuestionAnswering,
	)
	from transformers.models.auto.modeling_tf_auto import (
	    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
	    TF_MODEL_FOR_MASKED_LM_MAPPING,
	    TF_MODEL_FOR_PRETRAINING_MAPPING,
	    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
	    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
	    TF_MODEL_MAPPING,
	)
	from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST

class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       'new-model'


if is_tf_available():

	class 					a__						(					snake_case			):
				"""simple docstring"""







				__lowerCamelCase        =       NewModelConfig

@require_tf
class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			@slow
			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			"bert-base-cased"
							A__        =			AutoConfig.from_pretrained(lowercase       )
							self.assertIsNotNone(lowercase       )
							self.assertIsInstance(lowercase      ,      lowercase       )

							A__        =			TFAutoModel.from_pretrained(lowercase       )
							self.assertIsNotNone(lowercase       )
							self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			"bert-base-cased"
							A__        =			AutoConfig.from_pretrained(lowercase       )
							self.assertIsNotNone(lowercase       )
							self.assertIsInstance(lowercase      ,      lowercase       )

							A__        =			TFAutoModelForPreTraining.from_pretrained(lowercase       )
							self.assertIsNotNone(lowercase       )
							self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__        =			AutoConfig.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )

											A__        =			TFAutoModelForCausalLM.from_pretrained(lowercase       )
											A__    ,				A__        =			TFAutoModelForCausalLM.from_pretrained(lowercase      ,      output_loading_info=lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__        =			AutoConfig.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )

											A__        =			TFAutoModelWithLMHead.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__        =			AutoConfig.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )

											A__        =			TFAutoModelForMaskedLM.from_pretrained(lowercase       )
											A__    ,				A__        =			TFAutoModelForMaskedLM.from_pretrained(lowercase      ,      output_loading_info=lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__        =			AutoConfig.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )

											A__        =			TFAutoModelForSeqaSeqLM.from_pretrained(lowercase       )
											A__    ,				A__        =			TFAutoModelForSeqaSeqLM.from_pretrained(lowercase      ,      output_loading_info=lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							for model_name in ["bert-base-uncased"]:
											A__        =			AutoConfig.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )

											A__        =			TFAutoModelForSequenceClassification.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							for model_name in ["bert-base-uncased"]:
											A__        =			AutoConfig.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )

											A__        =			TFAutoModelForQuestionAnswering.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			@slow
			@require_tensorflow_probability
			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
											A__        =			AutoConfig.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )

											A__        =			TFAutoModelForTableQuestionAnswering.from_pretrained(lowercase       )
											A__    ,				A__        =			TFAutoModelForTableQuestionAnswering.from_pretrained(
											    lowercase      ,      output_loading_info=lowercase       )
											self.assertIsNotNone(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							A__        =			TFAutoModelWithLMHead.from_pretrained(lowercase       )
							self.assertIsInstance(lowercase      ,      lowercase       )
							self.assertEqual(model.num_parameters()      ,      14410       )
							self.assertEqual(model.num_parameters(only_trainable=lowercase       )      ,      14410       )


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			TFAutoModelWithLMHead.from_pretrained(lowercase       )
							self.assertIsInstance(lowercase      ,      lowercase       )
							self.assertEqual(model.num_parameters()      ,      14410       )
							self.assertEqual(model.num_parameters(only_trainable=lowercase       )      ,      14410       )


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			TFAutoModel.from_pretrained("sgugger/funnel-random-tiny"       )
							self.assertIsInstance(lowercase      ,      lowercase       )

							A__        =			copy.deepcopy(model.config       )
							A__        =			["FunnelBaseModel"]
							A__        =			TFAutoModel.from_config(lowercase       )
							self.assertIsInstance(lowercase      ,      lowercase       )

							with tempfile.TemporaryDirectory() as tmp_dir:
											model.save_pretrained(lowercase       )
											A__        =			TFAutoModel.from_pretrained(lowercase       )
											self.assertIsInstance(lowercase      ,      lowercase       )


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							try:
											AutoConfig.register("new-model"      ,      lowercase       )

											A__        =			[
											    TFAutoModel,
											    TFAutoModelForCausalLM,
											    TFAutoModelForMaskedLM,
											    TFAutoModelForPreTraining,
											    TFAutoModelForQuestionAnswering,
											    TFAutoModelForSequenceClassification,
											    TFAutoModelForTokenClassification,
											]

											for auto_class in auto_classes:
															with self.subTest(auto_class.__name__       ):
																			# Wrong config class will raise an error
																			with self.assertRaises(lowercase       ):
																							auto_class.register(lowercase      ,      lowercase       )
																			auto_class.register(lowercase      ,      lowercase       )
																			# Trying to register something existing in the Transformers library will raise an error
																			with self.assertRaises(lowercase       ):
																							auto_class.register(lowercase      ,      lowercase       )

																			# Now that the config is registered, it can be used as any other config with the auto-API
																			A__        =			BertModelTester(self       ).get_config()
																			A__        =			NewModelConfig(**tiny_config.to_dict()       )
																			A__        =			auto_class.from_config(lowercase       )
																			self.assertIsInstance(lowercase      ,      lowercase       )

																			with tempfile.TemporaryDirectory() as tmp_dir:
																							model.save_pretrained(lowercase       )
																							A__        =			auto_class.from_pretrained(lowercase       )
																							self.assertIsInstance(lowercase      ,      lowercase       )

							finally:
											if "new-model" in CONFIG_MAPPING._extra_content:
															del CONFIG_MAPPING._extra_content["new-model"]
											for mapping in (
											    TF_MODEL_MAPPING,
											    TF_MODEL_FOR_PRETRAINING_MAPPING,
											    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
											    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
											    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
											    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
											    TF_MODEL_FOR_MASKED_LM_MAPPING,
											):
															if NewModelConfig in mapping._extra_content:
																			del mapping._extra_content[NewModelConfig]


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							with self.assertRaisesRegex(
							    lowercase      ,      "bert-base is not a local folder and is not a valid model identifier"       ):
											A__        =			TFAutoModel.from_pretrained("bert-base"       )


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							with self.assertRaisesRegex(
							    lowercase      ,      R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"       ):
											A__        =			TFAutoModel.from_pretrained(lowercase      ,      revision="aaaaaa"       )


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							with self.assertRaisesRegex(
							    lowercase      ,      "hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin"      ,      ):
											A__        =			TFAutoModel.from_pretrained("hf-internal-testing/config-no-model"       )


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							with self.assertRaisesRegex(lowercase      ,      "Use `from_pt=True` to load this model"       ):
											A__        =			TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"       )


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert"       )
							with RequestCounter() as counter:
											A__        =			TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert"       )
											self.assertEqual(counter.get_request_count      ,      0       )
											self.assertEqual(counter.head_request_count      ,      1       )
											self.assertEqual(counter.other_request_count      ,      0       )

							# With a sharded checkpoint
							A__        =			TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded"       )
							with RequestCounter() as counter:
											A__        =			TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded"       )
											self.assertEqual(counter.get_request_count      ,      0       )
											self.assertEqual(counter.head_request_count      ,      1       )
											self.assertEqual(counter.other_request_count      ,      0       )

